titanic-All - Copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "s=pd.read_csv('t.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096067  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Survived  Pclass\n",
      "0          0       3\n",
      "1          1       1\n",
      "2          1       3\n",
      "3          1       1\n",
      "4          0       3\n",
      "5          0       3\n",
      "6          0       1\n",
      "7          0       3\n",
      "8          1       3\n",
      "9          1       2\n",
      "10         1       3\n",
      "11         1       1\n",
      "12         0       3\n",
      "13         0       3\n",
      "14         0       3\n",
      "15         1       2\n",
      "16         0       3\n",
      "17         1       2\n",
      "18         0       3\n",
      "19         1       3\n",
      "20         0       2\n",
      "21         1       2\n",
      "22         1       3\n",
      "23         1       1\n",
      "24         0       3\n",
      "25         1       3\n",
      "26         0       3\n",
      "27         0       1\n",
      "28         1       3\n",
      "29         0       3\n",
      "30         0       1\n",
      "31         1       1\n",
      "32         1       3\n",
      "33         0       2\n",
      "34         0       1\n",
      "35         0       1\n",
      "36         1       3\n",
      "37         0       3\n",
      "38         0       3\n",
      "39         1       3\n",
      "40         0       3\n",
      "41         0       2\n",
      "42         0       3\n",
      "43         1       2\n",
      "44         1       3\n",
      "45         0       3\n",
      "46         0       3\n",
      "47         1       3\n",
      "48         0       3\n",
      "49         0       3\n"
     ]
    }
   ],
   "source": [
    "#s1=s[['Survived','Pclass']].head(50)\n",
    "print(s[['Survived','Pclass']].head(50))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-10-f555d85ab5c4>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-10-f555d85ab5c4>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    s1=s.iloc(0,0:3).copy()\u001b[0m\n\u001b[1;37m                 ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "s1=s.iloc(0,0:3).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ccc8e09e48>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(s.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA. 2343      7\n",
       "1601          7\n",
       "347082        7\n",
       "CA 2144       6\n",
       "347088        6\n",
       "             ..\n",
       "373450        1\n",
       "C.A. 34260    1\n",
       "29105         1\n",
       "31418         1\n",
       "113783        1\n",
       "Name: Ticket, Length: 681, dtype: int64"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.Ticket.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "e=s.Cabin.str.startswith('NaN')\n",
    "s['Cabin'] = np.where(e,e,1) \n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833     1        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000     1        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    if x!=\"NaN\":\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "s['cabin']=s.Cabin.apply(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  cabin  \n",
       "0        0         A/5 21171   7.2500   NaN        S      1  \n",
       "1        0          PC 17599  71.2833     1        C      1  \n",
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       "3        0            113803  53.1000     1        S      1  \n",
       "4        0            373450   8.0500   NaN        S      1  \n",
       "..     ...               ...      ...   ...      ...    ...  \n",
       "886      0            211536  13.0000   NaN        S      1  \n",
       "887      0            112053  30.0000     1        S      1  \n",
       "888      2        W./C. 6607  23.4500   NaN        S      1  \n",
       "889      0            111369  30.0000     1        C      1  \n",
       "890      0            370376   7.7500   NaN        Q      1  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_Cabin(cols):\n",
    "    cabin=cols[0]\n",
    "    Pclass=cols[1]\n",
    "    if pd.notnull(cabin):\n",
    "        if Pclass==3:  \n",
    "            return 3\n",
    "        elif Pclass==2:\n",
    "            return 2\n",
    "        else:\n",
    "            return cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "s[\"cabin\"]=s[['cabin','Pclass']].apply(impute_Cabin,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  cabin  \n",
       "0      0         A/5 21171   7.2500   NaN        S      3  \n",
       "1      0          PC 17599  71.2833     1        C      1  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S      3  \n",
       "3      0            113803  53.1000     1        S      1  \n",
       "4      0            373450   8.0500   NaN        S      3  \n",
       "5      0            330877   8.4583   NaN        Q      3  \n",
       "6      0             17463  51.8625     1        S      1  \n",
       "7      1            349909  21.0750   NaN        S      3  \n",
       "8      2            347742  11.1333   NaN        S      3  \n",
       "9      0            237736  30.0708   NaN        C      2  "
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test_data =[s] # combining train and test dataset\n",
    "\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          517\n",
       "Miss        182\n",
       "Mrs         125\n",
       "Master       40\n",
       "Dr            7\n",
       "Rev           6\n",
       "Major         2\n",
       "Mlle          2\n",
       "Col           2\n",
       "Ms            1\n",
       "Sir           1\n",
       "Mme           1\n",
       "Countess      1\n",
       "Don           1\n",
       "Lady          1\n",
       "Jonkheer      1\n",
       "Capt          1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Title.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_mapping={'Mr':0,'Miss':1,\"Mrs\":2,\"Master\":3,'Dr':3,'Rev':3,'Col':3,'Mlle':3,'Major':3,'Ms':3,'Capt':3,'Countess':3,'Don':3,\n",
    "'Sir':3,'Lady':3,'Jonkheer':3,'Mme':3}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title']=dataset['Title'].map(title_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  cabin  Title  \n",
       "0      0         A/5 21171   7.2500   NaN        S      3      0  \n",
       "1      0          PC 17599  71.2833     1        C      1      2  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S      3      1  \n",
       "3      0            113803  53.1000     1        S      1      2  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "cabin            0\n",
       "Title            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ccc7718e88>"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(s.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):          #data cleaning\n",
    "    Age=cols[0]\n",
    "    Pclass=cols[1]\n",
    "    if pd.isnull(Age):\n",
    "        if Pclass==1:\n",
    "            return 38\n",
    "        elif Pclass==2:\n",
    "            return 29\n",
    "        elif Pclass==3:\n",
    "            return 25\n",
    "    else:\n",
    "        return Age\n",
    "s[\"Age\"]=s[['Age','Pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "\n",
       "   Parch     Ticket     Fare Cabin Embarked  cabin  Title  \n",
       "0      0  A/5 21171   7.2500   NaN        S      3      0  \n",
       "1      0   PC 17599  71.2833     1        C      1      2  "
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.drop(['Cabin','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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       "      <td>1</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
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       "    <tr>\n",
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       "      <td>889</td>\n",
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       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
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       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare  \\\n",
       "0              1         0       3    male  22.0      1      0   7.2500   \n",
       "1              2         1       1  female  38.0      1      0  71.2833   \n",
       "2              3         1       3  female  26.0      0      0   7.9250   \n",
       "3              4         1       1  female  35.0      1      0  53.1000   \n",
       "4              5         0       3    male  35.0      0      0   8.0500   \n",
       "..           ...       ...     ...     ...   ...    ...    ...      ...   \n",
       "886          887         0       2    male  27.0      0      0  13.0000   \n",
       "887          888         1       1  female  19.0      0      0  30.0000   \n",
       "888          889         0       3  female  25.0      1      2  23.4500   \n",
       "889          890         1       1    male  26.0      0      0  30.0000   \n",
       "890          891         0       3    male  32.0      0      0   7.7500   \n",
       "\n",
       "    Embarked  cabin  Title  \n",
       "0          S      3      0  \n",
       "1          C      1      2  \n",
       "2          S      3      1  \n",
       "3          S      1      2  \n",
       "4          S      3      0  \n",
       "..       ...    ...    ...  \n",
       "886        S      2      3  \n",
       "887        S      1      1  \n",
       "888        S      3      1  \n",
       "889        C      1      0  \n",
       "890        Q      3      0  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Fare           0\n",
       "Embarked       2\n",
       "cabin          0\n",
       "Title          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ccc756cb88>"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(s.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "s[\"Embarked\"].fillna(s['Embarked'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "cabin          0\n",
       "Title          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare  \\\n",
       "0            1         0       3    male  22.0      1      0   7.2500   \n",
       "1            2         1       1  female  38.0      1      0  71.2833   \n",
       "2            3         1       3  female  26.0      0      0   7.9250   \n",
       "3            4         1       1  female  35.0      1      0  53.1000   \n",
       "\n",
       "  Embarked  cabin  Title  \n",
       "0        S      3      0  \n",
       "1        C      1      2  \n",
       "2        S      3      1  \n",
       "3        S      1      2  "
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['Total_family_members']=s['SibSp']+s['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>35.0</td>\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare  \\\n",
       "0            1         0       3    male  22.0      1      0   7.2500   \n",
       "1            2         1       1  female  38.0      1      0  71.2833   \n",
       "2            3         1       3  female  26.0      0      0   7.9250   \n",
       "3            4         1       1  female  35.0      1      0  53.1000   \n",
       "\n",
       "  Embarked  cabin  Title  Total_family_members  \n",
       "0        S      3      0                     1  \n",
       "1        C      1      2                     1  \n",
       "2        S      3      1                     0  \n",
       "3        S      1      2                     1  "
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['Fare_Per_Person']=s['Fare']/(s['Total_family_members']+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
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       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare  \\\n",
       "0            1         0       3    male  22.0      1      0   7.2500   \n",
       "1            2         1       1  female  38.0      1      0  71.2833   \n",
       "\n",
       "  Embarked  cabin  Title  Total_family_members  Fare_Per_Person  \n",
       "0        S      3      0                     1          3.62500  \n",
       "1        C      1      2                     1         35.64165  "
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(2)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
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       "      <td>3</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.05000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex   Age  SibSp  Parch     Fare  \\\n",
       "0            1         0       3    male  22.0      1      0   7.2500   \n",
       "1            2         1       1  female  38.0      1      0  71.2833   \n",
       "2            3         1       3  female  26.0      0      0   7.9250   \n",
       "3            4         1       1  female  35.0      1      0  53.1000   \n",
       "4            5         0       3    male  35.0      0      0   8.0500   \n",
       "\n",
       "  Embarked  cabin  Title  Total_family_members  Fare_Per_Person  \n",
       "0        S      3      0                     1          3.62500  \n",
       "1        C      1      2                     1         35.64165  \n",
       "2        S      3      1                     0          7.92500  \n",
       "3        S      1      2                     1         26.55000  \n",
       "4        S      3      0                     0          8.05000  "
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.Age=pd.cut(s.Age,bins=[0,18,25,99],labels=['child','young Adult','adult']).head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>PassengerId</th>\n",
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       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>cabin</th>\n",
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       "      <th>Fare_Per_Person</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>young Adult</td>\n",
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       "      <td>S</td>\n",
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       "      <td>adult</td>\n",
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       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>adult</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.92500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass     Sex          Age  SibSp  Parch     Fare  \\\n",
       "0            1         0       3    male  young Adult      1      0   7.2500   \n",
       "1            2         1       1  female        adult      1      0  71.2833   \n",
       "2            3         1       3  female        adult      0      0   7.9250   \n",
       "\n",
       "  Embarked  cabin  Title  Total_family_members  Fare_Per_Person  \n",
       "0        S      3      0                     1          3.62500  \n",
       "1        C      1      2                     1         35.64165  \n",
       "2        S      3      1                     0          7.92500  "
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['Sex']=pd.get_dummies(s['Sex'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['Age']=pd.get_dummies(s['Age'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['Embarked']=pd.get_dummies(s['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from scipy import stats\n",
    "#z = np.abs(stats.zscore(s))\n",
    "#print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "#threshold = 3\n",
    "#print(np.where(z > 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.92500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>26.55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.05000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.45830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.86250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4.21500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.71110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15.03540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch     Fare  Embarked  \\\n",
       "0            1         0       3    1    1      1      0   7.2500         0   \n",
       "1            2         1       1    0    0      1      0  71.2833         0   \n",
       "2            3         1       3    0    0      0      0   7.9250         0   \n",
       "3            4         1       1    0    0      1      0  53.1000         0   \n",
       "4            5         0       3    1    0      0      0   8.0500         0   \n",
       "5            6         0       3    1    0      0      0   8.4583         1   \n",
       "6            7         0       1    1    0      0      0  51.8625         0   \n",
       "7            8         0       3    1    0      3      1  21.0750         0   \n",
       "8            9         1       3    0    0      0      2  11.1333         0   \n",
       "9           10         1       2    0    0      1      0  30.0708         0   \n",
       "\n",
       "   cabin  Title  Total_family_members  Fare_Per_Person  \n",
       "0      3      0                     1          3.62500  \n",
       "1      1      2                     1         35.64165  \n",
       "2      3      1                     0          7.92500  \n",
       "3      1      2                     1         26.55000  \n",
       "4      3      0                     0          8.05000  \n",
       "5      3      0                     0          8.45830  \n",
       "6      1      0                     0         51.86250  \n",
       "7      3      3                     4          4.21500  \n",
       "8      3      2                     2          3.71110  \n",
       "9      2      2                     1         15.03540  "
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "s11=s.drop(['PassengerId','Survived'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x1=s.drop(['PassengerId','Survived','SibSp','Parch'],axis=1)\n",
    "#y1=s['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 11)"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s11.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "      <th>Total_family_members</th>\n",
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       "      <td>0</td>\n",
       "      <td>7.92500</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>26.55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.05000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5.86250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch     Fare  Embarked  \\\n",
       "0              1         0       3    1    1      1      0   7.2500         0   \n",
       "1              2         1       1    0    0      1      0  71.2833         0   \n",
       "2              3         1       3    0    0      0      0   7.9250         0   \n",
       "3              4         1       1    0    0      1      0  53.1000         0   \n",
       "4              5         0       3    1    0      0      0   8.0500         0   \n",
       "..           ...       ...     ...  ...  ...    ...    ...      ...       ...   \n",
       "886          887         0       2    1    0      0      0  13.0000         0   \n",
       "887          888         1       1    0    0      0      0  30.0000         0   \n",
       "888          889         0       3    0    0      1      2  23.4500         0   \n",
       "889          890         1       1    1    0      0      0  30.0000         0   \n",
       "890          891         0       3    1    0      0      0   7.7500         1   \n",
       "\n",
       "     cabin  Title  Total_family_members  Fare_Per_Person  \n",
       "0        3      0                     1          3.62500  \n",
       "1        1      2                     1         35.64165  \n",
       "2        3      1                     0          7.92500  \n",
       "3        1      2                     1         26.55000  \n",
       "4        3      0                     0          8.05000  \n",
       "..     ...    ...                   ...              ...  \n",
       "886      2      3                     0         13.00000  \n",
       "887      1      1                     0         30.00000  \n",
       "888      3      1                     3          5.86250  \n",
       "889      1      0                     0         30.00000  \n",
       "890      3      0                     0          7.75000  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "X = s[['Pclass','Sex','Age','SibSp','Parch','Fare','Embarked','cabin','Title','Total_family_members','Fare_Per_Person']]  #independent columns\n",
    "y = s['Survived']    #target column i.e price range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "bestfeatures = SelectKBest(score_func=chi2, k=10)\n",
    "fit = bestfeatures.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfscores = pd.DataFrame(fit.scores_)\n",
    "dfcolumns = pd.DataFrame(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
    "featureScores.columns = ['Specs','Score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Specs</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>30.873699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>92.702447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.622951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>2.581865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parch</td>\n",
       "      <td>10.097499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fare</td>\n",
       "      <td>4518.319091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.010847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cabin</td>\n",
       "      <td>30.873699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Title</td>\n",
       "      <td>233.656143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Total_family_members</td>\n",
       "      <td>0.709091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Fare_Per_Person</td>\n",
       "      <td>2818.936692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Specs        Score\n",
       "0                 Pclass    30.873699\n",
       "1                    Sex    92.702447\n",
       "2                    Age     0.622951\n",
       "3                  SibSp     2.581865\n",
       "4                  Parch    10.097499\n",
       "5                   Fare  4518.319091\n",
       "6               Embarked     0.010847\n",
       "7                  cabin    30.873699\n",
       "8                  Title   233.656143\n",
       "9   Total_family_members     0.709091\n",
       "10       Fare_Per_Person  2818.936692"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "featureScores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Specs        Score\n",
      "5                   Fare  4518.319091\n",
      "10       Fare_Per_Person  2818.936692\n",
      "8                  Title   233.656143\n",
      "1                    Sex    92.702447\n",
      "0                 Pclass    30.873699\n",
      "7                  cabin    30.873699\n",
      "4                  Parch    10.097499\n",
      "3                  SibSp     2.581865\n",
      "9   Total_family_members     0.709091\n",
      "2                    Age     0.622951\n"
     ]
    }
   ],
   "source": [
    "print(featureScores.nlargest(10,'Score'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "xsc=s[['Pclass','Sex','Age','SibSp','Parch','Fare','Embarked','cabin','Title','Total_family_members','Fare_Per_Person']]\n",
    "ysc=s['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
       "                     criterion='gini', max_depth=None, max_features='auto',\n",
       "                     max_leaf_nodes=None, max_samples=None,\n",
       "                     min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                     min_samples_leaf=1, min_samples_split=2,\n",
       "                     min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                     n_jobs=None, oob_score=False, random_state=None, verbose=0,\n",
       "                     warm_start=False)"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "model = ExtraTreesClassifier()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.43693466e-02 2.66546367e-01 1.57812831e-04 3.37960731e-02\n",
      " 2.49126658e-02 1.68702728e-01 1.70143419e-02 6.61828338e-02\n",
      " 1.45101952e-01 4.91507747e-02 1.64065104e-01]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Specs        Score\n",
      "5                   Fare  4518.319091\n",
      "10       Fare_Per_Person  2818.936692\n",
      "8                  Title   233.656143\n",
      "1                    Sex    92.702447\n",
      "0                 Pclass    30.873699\n",
      "7                  cabin    30.873699\n",
      "4                  Parch    10.097499\n",
      "3                  SibSp     2.581865\n",
      "9   Total_family_members     0.709091\n",
      "2                    Age     0.622951\n",
      "6               Embarked     0.010847\n"
     ]
    }
   ],
   "source": [
    "print(featureScores.nlargest(11,'Score'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
    "feat_importances.nlargest(10).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.82737724,  0.73769513, 29.83286778, ..., -0.73374551,\n",
       "         0.05915988, -0.4547978 ],\n",
       "       [-1.56610693, -1.35557354, -0.03352008, ...,  1.33186981,\n",
       "         0.05915988,  0.43899428],\n",
       "       [ 0.82737724, -1.35557354, -0.03352008, ...,  0.29906215,\n",
       "        -0.56097483, -0.33475695],\n",
       "       ...,\n",
       "       [ 0.82737724, -1.35557354, -0.03352008, ...,  0.29906215,\n",
       "         1.29942929, -0.39233468],\n",
       "       [-1.56610693,  0.73769513, -0.03352008, ..., -0.73374551,\n",
       "        -0.56097483,  0.28149928],\n",
       "       [ 0.82737724,  0.73769513, -0.03352008, ..., -0.73374551,\n",
       "        -0.56097483, -0.33964233]])"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "scaler.fit(s11)\n",
    "scaled_features=scaler.transform(s11)\n",
    "scaled_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'cabin',\n",
       "       'Title', 'Total_family_members', 'Fare_Per_Person'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s11.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "      <th>Total_family_members</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.827377</td>\n",
       "      <td>0.737695</td>\n",
       "      <td>29.832868</td>\n",
       "      <td>0.432793</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>-0.502445</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>0.827377</td>\n",
       "      <td>-0.733746</td>\n",
       "      <td>0.059160</td>\n",
       "      <td>-0.454798</td>\n",
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       "      <th>1</th>\n",
       "      <td>-1.566107</td>\n",
       "      <td>-1.355574</td>\n",
       "      <td>-0.033520</td>\n",
       "      <td>0.432793</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>0.786845</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>-1.566107</td>\n",
       "      <td>1.331870</td>\n",
       "      <td>0.059160</td>\n",
       "      <td>0.438994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.827377</td>\n",
       "      <td>-1.355574</td>\n",
       "      <td>-0.033520</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>-0.488854</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>0.827377</td>\n",
       "      <td>0.299062</td>\n",
       "      <td>-0.560975</td>\n",
       "      <td>-0.334757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.566107</td>\n",
       "      <td>-1.355574</td>\n",
       "      <td>-0.033520</td>\n",
       "      <td>0.432793</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>0.420730</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>-1.566107</td>\n",
       "      <td>1.331870</td>\n",
       "      <td>0.059160</td>\n",
       "      <td>0.185187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.827377</td>\n",
       "      <td>0.737695</td>\n",
       "      <td>-0.033520</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>-0.486337</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>0.827377</td>\n",
       "      <td>-0.733746</td>\n",
       "      <td>-0.560975</td>\n",
       "      <td>-0.331267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>-0.369365</td>\n",
       "      <td>0.737695</td>\n",
       "      <td>-0.033520</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>-0.386671</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>-0.369365</td>\n",
       "      <td>2.364677</td>\n",
       "      <td>-0.560975</td>\n",
       "      <td>-0.193081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>-1.566107</td>\n",
       "      <td>-1.355574</td>\n",
       "      <td>-0.033520</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>-0.044381</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>-1.566107</td>\n",
       "      <td>0.299062</td>\n",
       "      <td>-0.560975</td>\n",
       "      <td>0.281499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0.827377</td>\n",
       "      <td>-1.355574</td>\n",
       "      <td>-0.033520</td>\n",
       "      <td>0.432793</td>\n",
       "      <td>2.008933</td>\n",
       "      <td>-0.176263</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>0.827377</td>\n",
       "      <td>0.299062</td>\n",
       "      <td>1.299429</td>\n",
       "      <td>-0.392335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>-1.566107</td>\n",
       "      <td>0.737695</td>\n",
       "      <td>-0.033520</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>-0.044381</td>\n",
       "      <td>-0.307562</td>\n",
       "      <td>-1.566107</td>\n",
       "      <td>-0.733746</td>\n",
       "      <td>-0.560975</td>\n",
       "      <td>0.281499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0.827377</td>\n",
       "      <td>0.737695</td>\n",
       "      <td>-0.033520</td>\n",
       "      <td>-0.474545</td>\n",
       "      <td>-0.473674</td>\n",
       "      <td>-0.492378</td>\n",
       "      <td>3.251373</td>\n",
       "      <td>0.827377</td>\n",
       "      <td>-0.733746</td>\n",
       "      <td>-0.560975</td>\n",
       "      <td>-0.339642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pclass       Sex        Age     SibSp     Parch      Fare  Embarked  \\\n",
       "0    0.827377  0.737695  29.832868  0.432793 -0.473674 -0.502445 -0.307562   \n",
       "1   -1.566107 -1.355574  -0.033520  0.432793 -0.473674  0.786845 -0.307562   \n",
       "2    0.827377 -1.355574  -0.033520 -0.474545 -0.473674 -0.488854 -0.307562   \n",
       "3   -1.566107 -1.355574  -0.033520  0.432793 -0.473674  0.420730 -0.307562   \n",
       "4    0.827377  0.737695  -0.033520 -0.474545 -0.473674 -0.486337 -0.307562   \n",
       "..        ...       ...        ...       ...       ...       ...       ...   \n",
       "886 -0.369365  0.737695  -0.033520 -0.474545 -0.473674 -0.386671 -0.307562   \n",
       "887 -1.566107 -1.355574  -0.033520 -0.474545 -0.473674 -0.044381 -0.307562   \n",
       "888  0.827377 -1.355574  -0.033520  0.432793  2.008933 -0.176263 -0.307562   \n",
       "889 -1.566107  0.737695  -0.033520 -0.474545 -0.473674 -0.044381 -0.307562   \n",
       "890  0.827377  0.737695  -0.033520 -0.474545 -0.473674 -0.492378  3.251373   \n",
       "\n",
       "        cabin     Title  Total_family_members  Fare_Per_Person  \n",
       "0    0.827377 -0.733746              0.059160        -0.454798  \n",
       "1   -1.566107  1.331870              0.059160         0.438994  \n",
       "2    0.827377  0.299062             -0.560975        -0.334757  \n",
       "3   -1.566107  1.331870              0.059160         0.185187  \n",
       "4    0.827377 -0.733746             -0.560975        -0.331267  \n",
       "..        ...       ...                   ...              ...  \n",
       "886 -0.369365  2.364677             -0.560975        -0.193081  \n",
       "887 -1.566107  0.299062             -0.560975         0.281499  \n",
       "888  0.827377  0.299062              1.299429        -0.392335  \n",
       "889 -1.566107 -0.733746             -0.560975         0.281499  \n",
       "890  0.827377 -0.733746             -0.560975        -0.339642  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df=pd.DataFrame(scaled_features,columns=s11.columns)\n",
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df1=train_df[['Fare','Age','Fare_Per_Person','Title','Sex','Pclass','cabin','Parch','SibSp','Total_family_members','Embarked']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "xs=train_df1\n",
    "ys=s['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(xsc,ysc,test_size=0.25,random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xs_train,xs_test,ys_train,ys_test=train_test_split(xs,ys,test_size=0.25,random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.84      0.81       135\n",
      "           1       0.73      0.64      0.68        88\n",
      "\n",
      "    accuracy                           0.76       223\n",
      "   macro avg       0.75      0.74      0.75       223\n",
      "weighted avg       0.76      0.76      0.76       223\n",
      "\n",
      "[[114  21]\n",
      " [ 32  56]]\n",
      "Accuracy: 0.7623318385650224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv_score: 0.7968539325842696\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logmodel=LogisticRegression(solver='lbfgs')\n",
    "logmodel.fit(X_train,y_train)\n",
    "predictions=logmodel.predict(X_test)\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "print(classification_report(y_test,predictions))\n",
    "print(confusion_matrix(y_test,predictions))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,predictions))\n",
    "print (\"cv_score:\",cross_val_score(logmodel,xsc,ysc,cv=10, scoring = 'accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.84      0.81       135\n",
      "           1       0.73      0.64      0.68        88\n",
      "\n",
      "    accuracy                           0.76       223\n",
      "   macro avg       0.75      0.74      0.75       223\n",
      "weighted avg       0.76      0.76      0.76       223\n",
      "\n",
      "[[114  21]\n",
      " [ 32  56]]\n",
      "Accuracy: 0.7623318385650224\n",
      "cv_score: 0.7934831460674158\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logmodels=LogisticRegression(solver='lbfgs')\n",
    "logmodels.fit(xs_train,ys_train)\n",
    "predictionss=logmodels.predict(xs_test)\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "print(classification_report(ys_test,predictionss))\n",
    "print(confusion_matrix(ys_test,predictionss))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(ys_test,predictionss))\n",
    "print (\"cv_score:\",cross_val_score(logmodels, xs, ys, cv=10, scoring = 'accuracy').mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2ccc79c0208>"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "plot_confusion_matrix(logmodel,xs_test,ys_test,cmap='viridis' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.90      0.84       135\n",
      "           1       0.80      0.62      0.70        88\n",
      "\n",
      "    accuracy                           0.79       223\n",
      "   macro avg       0.79      0.76      0.77       223\n",
      "weighted avg       0.79      0.79      0.78       223\n",
      "\n",
      "[[121  14]\n",
      " [ 33  55]]\n",
      "Accuracy: 0.7892376681614349\n",
      "cv: 0.821585518102372\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knnclassifier = KNeighborsClassifier(n_neighbors=8,leaf_size=10,p=1)\n",
    "knnclassifier.fit(xs_train,ys_train)\n",
    "predknn = knnclassifier.predict(xs_test)\n",
    "print(classification_report(ys_test,predknn))\n",
    "print(confusion_matrix(ys_test,predknn))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(ys_test,predknn))\n",
    "print (\"cv:\",cross_val_score(knnclassifier, xs, ys, cv=10, scoring = 'accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2ccc744fdc8>"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "plot_confusion_matrix(knnclassifier,xs_test,ys_test,cmap='viridis' )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "# Will take some time\n",
    "for i in range(1,40):\n",
    "    \n",
    "    knnclassifie = KNeighborsClassifier(n_neighbors=i)\n",
    "    knnclassifie.fit(xs_train,ys_train)\n",
    "    pred_i = knnclassifie.predict(X_test)\n",
    "    error_rate.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error Rate')"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,color='blue', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='red', markersize=10)\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'leaf_size': 19, 'n_neighbors': 18, 'p': 1}"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from sklearn.metrics import accuracy_score\n",
    "#from sklearn.metrics import make_scorer\n",
    "#scoring = {'accuracy': make_scorer(accuracy_score)}\n",
    "param_grid={\"n_neighbors\":list(range(1,40)),\n",
    "    \"leaf_size\":list(range(1,40)),\n",
    "    \"p\":[1,2]}\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "kng=KNeighborsClassifier(n_neighbors=10)\n",
    "clf = GridSearchCV(estimator=kng, param_grid=param_grid,cv=10,n_jobs=-1)\n",
    "clf=clf.fit(X_train,y_train)\n",
    "clf.best_params_\n",
    "#clf = GridSearchCV(estimator=kng, param_grid=param_grid,scoring=scoring,cv=10,n_jobs=-1)\n",
    "#clf=clf.fit(X_train,y_train)\n",
    "#Accuracy=clf.best_score_\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.87      0.82       135\n",
      "           1       0.76      0.61      0.68        88\n",
      "\n",
      "    accuracy                           0.77       223\n",
      "   macro avg       0.77      0.74      0.75       223\n",
      "weighted avg       0.77      0.77      0.77       223\n",
      "\n",
      "[[118  17]\n",
      " [ 34  54]]\n",
      "Accuracy: 0.7713004484304933\n",
      "cv: 0.8204244694132334\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knnclassifier = KNeighborsClassifier(n_neighbors=18,leaf_size=19,p=1)\n",
    "knnclassifier.fit(xs_train,ys_train)\n",
    "predknn = knnclassifier.predict(xs_test)\n",
    "print(classification_report(ys_test,predknn))\n",
    "print(confusion_matrix(ys_test,predknn))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(ys_test,predknn))\n",
    "print (\"cv:\",cross_val_score(knnclassifier, xs, ys, cv=10, scoring = 'accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.84      0.81       135\n",
      "           1       0.72      0.66      0.69        88\n",
      "\n",
      "    accuracy                           0.77       223\n",
      "   macro avg       0.76      0.75      0.75       223\n",
      "weighted avg       0.76      0.77      0.76       223\n",
      "\n",
      "[[113  22]\n",
      " [ 30  58]]\n",
      "Accuracy: 0.7668161434977578\n",
      "Accuracy: 0.7668161434977578\n",
      "cv_score: 0.818214731585518\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier#78\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "rfc.fit(X_train, y_train)\n",
    "rfc_pred = rfc.predict(X_test)\n",
    "print(classification_report(y_test,rfc_pred))\n",
    "print(confusion_matrix(y_test,rfc_pred))\n",
    "print(\"Accuracy:\",rfc.score(X_test,y_test))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,rfc_pred))\n",
    "print (\"cv_score:\",cross_val_score(rfc,xsc,ysc, cv=10, scoring = 'accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = { \"criterion\" : [\"gini\", \"entropy\"], \"min_samples_leaf\" : [1, 5, 10, 25, 50, 70], \"min_samples_split\" : [2, 4, 10, 12, 16, 18, 25, 35], \"n_estimators\": [100, 400, 700, 1000, 1500]}\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "rf = RandomForestClassifier(n_estimators=100, max_features='auto', oob_score=True, random_state=1, n_jobs=-1)\n",
    "clf = GridSearchCV(estimator=rfc, param_grid=param_grid,cv=10, n_jobs=-1)\n",
    "clf=clf.fit(X_train, y_train)\n",
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.87      0.84       135\n",
      "           1       0.77      0.68      0.72        88\n",
      "\n",
      "    accuracy                           0.79       223\n",
      "   macro avg       0.79      0.77      0.78       223\n",
      "weighted avg       0.79      0.79      0.79       223\n",
      "\n",
      "[[117  18]\n",
      " [ 28  60]]\n",
      "Accuracy: 0.7937219730941704\n",
      "cv_score: 0.829450686641698\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier#67\n",
    "rfcs = RandomForestClassifier(n_estimators=100)\n",
    "rfcs.fit(xs_train, ys_train)\n",
    "rfc_preds = rfcs.predict(xs_test)\n",
    "print(classification_report(ys_test,rfc_preds))\n",
    "print(confusion_matrix(ys_test,rfc_preds))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(ys_test,rfc_preds))\n",
    "print (\"cv_score:\",cross_val_score(rfcs, xs,ys, cv=10, scoring = 'accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = { \"criterion\":[\"gini\", \"entropy\"], \"min_samples_leaf\" : [1, 5, 10, 25, 50, 70], \"min_samples_split\" : [2, 4, 10, 12, 16, 18, 25, 35]}\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "rfcs = RandomForestClassifier(n_estimators=100)\n",
    "clf = GridSearchCV(estimator=rfcs, param_grid=param_grid, n_jobs=-1)\n",
    "clf=clf.fit(X_train, y_train)\n",
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.91      0.84       135\n",
      "           1       0.82      0.60      0.69        88\n",
      "\n",
      "    accuracy                           0.79       223\n",
      "   macro avg       0.80      0.76      0.77       223\n",
      "weighted avg       0.79      0.79      0.78       223\n",
      "\n",
      "[[123  12]\n",
      " [ 35  53]]\n",
      "Accuracy: 0.7892376681614349\n",
      "cv: 0.8115106117353308\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier#77\n",
    "dtree = DecisionTreeClassifier(criterion='entropy',min_samples_leaf=5,min_samples_split=25)\n",
    "dtree.fit(X_train,y_train)\n",
    "predtree = dtree.predict(X_test)\n",
    "print(classification_report(y_test,predtree))\n",
    "print(confusion_matrix(y_test,predtree))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,predtree))\n",
    "print (\"cv:\",cross_val_score(dtree,xsc,ysc, cv=10, scoring = 'accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.87      0.85       135\n",
      "           1       0.78      0.70      0.74        88\n",
      "\n",
      "    accuracy                           0.81       223\n",
      "   macro avg       0.80      0.79      0.79       223\n",
      "weighted avg       0.81      0.81      0.81       223\n",
      "\n",
      "[[118  17]\n",
      " [ 26  62]]\n",
      "Accuracy: 0.8071748878923767\n",
      "cv: 0.8115106117353308\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier#77\n",
    "dtree = DecisionTreeClassifier(criterion='gini',min_samples_leaf=10,min_samples_split=35)\n",
    "dtree.fit(X_train,y_train)\n",
    "predtree = dtree.predict(X_test)\n",
    "print(classification_report(y_test,predtree))\n",
    "print(confusion_matrix(y_test,predtree))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,predtree))\n",
    "print (\"cv:\",cross_val_score(dtree,xsc,ysc, cv=10, scoring = 'accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.87      0.84       135\n",
      "           1       0.78      0.67      0.72        88\n",
      "\n",
      "    accuracy                           0.79       223\n",
      "   macro avg       0.79      0.77      0.78       223\n",
      "weighted avg       0.79      0.79      0.79       223\n",
      "\n",
      "[[118  17]\n",
      " [ 29  59]]\n",
      "Accuracy: 0.7937219730941704\n",
      "cv: 0.8294506866416977\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier#77\n",
    "dtree = DecisionTreeClassifier()\n",
    "dtree.fit(X_train,y_train)\n",
    "predtree = dtree.predict(X_test)\n",
    "print(classification_report(y_test,predtree))\n",
    "print(confusion_matrix(y_test,predtree))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,predtree))\n",
    "print (\"cv:\",cross_val_score(dtree,xsc,ysc, cv=10, scoring = 'accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini', 'min_samples_leaf': 10, 'min_samples_split': 35}"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = { \"criterion\":[\"gini\", \"entropy\"], \"min_samples_leaf\" : [1, 5, 10, 25, 50, 70], \"min_samples_split\" : [2, 4, 10, 12, 16, 18, 25, 35]}\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "dtree =DecisionTreeClassifier(criterion='gini')\n",
    "clf = GridSearchCV(estimator=dtree, param_grid=param_grid, n_jobs=-1)\n",
    "clf=clf.fit(X_train, y_train)\n",
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.98      0.79       135\n",
      "           1       0.88      0.24      0.37        88\n",
      "\n",
      "    accuracy                           0.69       223\n",
      "   macro avg       0.77      0.61      0.58       223\n",
      "weighted avg       0.75      0.69      0.63       223\n",
      "\n",
      "[[132   3]\n",
      " [ 67  21]]\n",
      "Accuracy: 0.6860986547085202\n",
      "cv: 0.6723720349563047\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC()\n",
    "svc.fit(X_train,y_train)\n",
    "predsvc = svc.predict(X_test)\n",
    "print(classification_report(y_test,predsvc))\n",
    "print(confusion_matrix(y_test,predsvc))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,predsvc))\n",
    "print (\"cv:\",cross_val_score(svc,xsc,ysc,cv=10, scoring = 'accuracy').mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "X.shape[1] = 7 should be equal to 8, the number of features at training time",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-79-d15cb9f6b880>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0msvcs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSVC\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0msvcs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mys_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mpredsvcs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msvc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxs_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mys_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredsvcs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfusion_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mys_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredsvcs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    592\u001b[0m             \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    593\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 594\u001b[1;33m             \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    595\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    596\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    313\u001b[0m         \u001b[0my_pred\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m         \"\"\"\n\u001b[1;32m--> 315\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_for_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    316\u001b[0m         \u001b[0mpredict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sparse_predict\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dense_predict\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    317\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py\u001b[0m in \u001b[0;36m_validate_for_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    465\u001b[0m             raise ValueError(\"X.shape[1] = %d should be equal to %d, \"\n\u001b[0;32m    466\u001b[0m                              \u001b[1;34m\"the number of features at training time\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 467\u001b[1;33m                              (n_features, self.shape_fit_[1]))\n\u001b[0m\u001b[0;32m    468\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    469\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: X.shape[1] = 7 should be equal to 8, the number of features at training time"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svcs = SVC()\n",
    "svcs.fit(xs_train,ys_train)\n",
    "predsvcs = svc.predict(xs_test)\n",
    "print(classification_report(ys_test,predsvcs))\n",
    "print(confusion_matrix(ys_test,predsvcs))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(ys_test,predsvcs))\n",
    "print (\"cv:\",cross_val_score(svcs, xs, ys, cv=10, scoring = 'accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
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      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
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      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
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      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
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      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
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      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
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      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
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      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
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      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
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      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
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      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
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      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
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      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
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      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
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      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
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      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
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      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
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      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
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      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
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      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
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      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
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      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
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      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
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      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
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      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
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      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
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      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
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      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
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      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
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      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
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      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[[132  24]\n",
      " [ 43  69]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.85      0.80       156\n",
      "           1       0.74      0.62      0.67       112\n",
      "\n",
      "    accuracy                           0.75       268\n",
      "   macro avg       0.75      0.73      0.74       268\n",
      "weighted avg       0.75      0.75      0.75       268\n",
      "\n",
      "Accuracy: 0.75\n",
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    3.8s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    4.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    4.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    3.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    3.8s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    4.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    4.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    4.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.2s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.3s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.3s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.2s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.3s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.3s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.2s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.4s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    7.5s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.3s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.3s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.4s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    6.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=0.1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=0.1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=0.1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=0.1, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=0.1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=1, kernel=rbf ........................................\n",
      "[CV] ......................... C=1, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.1, kernel=rbf ......................................\n",
      "[CV] ....................... C=1, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.01, kernel=rbf .....................................\n",
      "[CV] ...................... C=1, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.0s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=1, gamma=0.001, kernel=rbf ....................................\n",
      "[CV] ..................... C=1, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=1, kernel=rbf .......................................\n",
      "[CV] ........................ C=10, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.1, kernel=rbf .....................................\n",
      "[CV] ...................... C=10, gamma=0.1, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.01, kernel=rbf ....................................\n",
      "[CV] ..................... C=10, gamma=0.01, kernel=rbf, total=   0.0s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=10, gamma=0.001, kernel=rbf ...................................\n",
      "[CV] .................... C=10, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=1, kernel=rbf ......................................\n",
      "[CV] ....................... C=100, gamma=1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.1, kernel=rbf ....................................\n",
      "[CV] ..................... C=100, gamma=0.1, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.01, kernel=rbf ...................................\n",
      "[CV] .................... C=100, gamma=0.01, kernel=rbf, total=   0.2s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n",
      "[CV] C=100, gamma=0.001, kernel=rbf ..................................\n",
      "[CV] ................... C=100, gamma=0.001, kernel=rbf, total=   0.1s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  80 out of  80 | elapsed:    5.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv: 0.78\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'C': [0.1,1, 10, 100], 'gamma': [1,0.1,0.01,0.001],'kernel': ['rbf']} \n",
    "grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=2)\n",
    "grid.fit(X_train,y_train)\n",
    "grid_predictions = grid.predict(X_test)\n",
    "print(confusion_matrix(y_test,grid_predictions))\n",
    "print(classification_report(y_test,grid_predictions))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,grid_predictions))\n",
    "print (\"cv:\",cross_val_score(grid,xsc,ysc,cv=10, scoring = 'accuracy').mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.75      0.82       135\n",
      "           1       0.69      0.88      0.77        88\n",
      "\n",
      "    accuracy                           0.80       223\n",
      "   macro avg       0.80      0.81      0.80       223\n",
      "weighted avg       0.82      0.80      0.80       223\n",
      "\n",
      "[[101  34]\n",
      " [ 11  77]]\n",
      "Accuracy: 0.7982062780269058\n",
      "cv: 0.8294382022471909\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB#70\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)\n",
    "y_predgnb =gnb.predict(X_test)\n",
    "print(classification_report(y_test,y_predgnb))\n",
    "print(confusion_matrix(y_test,y_predgnb))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,y_predgnb))\n",
    "print (\"cv:\",cross_val_score(dtree,xsc,ysc, cv=10, scoring = 'accuracy').mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.04      0.08       135\n",
      "           1       0.40      0.99      0.57        88\n",
      "\n",
      "    accuracy                           0.42       223\n",
      "   macro avg       0.63      0.52      0.33       223\n",
      "weighted avg       0.68      0.42      0.28       223\n",
      "\n",
      "[[  6 129]\n",
      " [  1  87]]\n",
      "Accuracy: 0.4170403587443946\n",
      "cv: 0.826079900124844\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB#62\n",
    "gnbs = GaussianNB()\n",
    "gnbs.fit(xs_train, ys_train)\n",
    "y_predgnbs =gnbs.predict(xs_test)\n",
    "print(classification_report(ys_test,y_predgnbs))\n",
    "print(confusion_matrix(ys_test,y_predgnbs))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(ys_test,y_predgnbs))\n",
    "print (\"cv:\",cross_val_score(dtree,xs,ys,cv=10,scoring = 'accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.87      0.83       135\n",
      "           1       0.76      0.62      0.69        88\n",
      "\n",
      "    accuracy                           0.78       223\n",
      "   macro avg       0.77      0.75      0.76       223\n",
      "weighted avg       0.77      0.78      0.77       223\n",
      "\n",
      "[[118  17]\n",
      " [ 33  55]]\n",
      "Accuracy: 0.7757847533632287\n",
      "cv: 0.8283270911360798\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "regressor = xgb.XGBClassifier(\n",
    "    n_estimators=100,\n",
    "    reg_lambda=1,\n",
    "    gamma=0,\n",
    "    max_depth=3\n",
    ")\n",
    "regressor.fit(X_train, y_train)\n",
    "y_predx = regressor.predict(X_test)\n",
    "print(classification_report(y_test,y_predx))\n",
    "print(confusion_matrix(y_test,y_predx))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test,y_predx))\n",
    "print (\"cv:\",cross_val_score(dtree,xsc,ysc, cv=10, scoring = 'accuracy').mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,\n",
       "       1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.63      0.96      0.76       135\n",
      "           1       0.67      0.14      0.23        88\n",
      "\n",
      "    accuracy                           0.63       223\n",
      "   macro avg       0.65      0.55      0.49       223\n",
      "weighted avg       0.64      0.63      0.55       223\n",
      "\n",
      "[[129   6]\n",
      " [ 76  12]]\n",
      "Accuracy: 0.6322869955156951\n",
      "cv: 0.8216104868913858\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "regressors = xgb.XGBClassifier(\n",
    "    n_estimators=100,\n",
    "    reg_lambda=1,\n",
    "    gamma=0,\n",
    "    max_depth=3\n",
    ")\n",
    "regressors.fit(xs_train,ys_train)\n",
    "y_predxs = regressor.predict(xs_test)\n",
    "print(classification_report(ys_test,y_predxs))\n",
    "print(confusion_matrix(ys_test,y_predxs))\n",
    "print(\"Accuracy:\",metrics.accuracy_score(ys_test,y_predxs))\n",
    "print (\"cv:\",cross_val_score(dtree,xs,ys,cv=10,scoring = 'accuracy').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=pd.read_csv('te.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Fare'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    if x!=\"NaN\":\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "test['cabin']=test.Cabin.apply(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"cabin\"]=test[['cabin','Pclass']].apply(impute_Cabin,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "      <th>Total_family_members</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.829200</td>\n",
       "    </tr>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.662500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4.095833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>108.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7.452767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex  Age  SibSp  Parch      Fare  Embarked  cabin  Title  \\\n",
       "0         3    1    0      0      0    7.8292         1      3      0   \n",
       "1         3    0    0      1      0    7.0000         0      3      2   \n",
       "2         2    1    0      0      0    9.6875         1      2      0   \n",
       "3         3    1    0      0      0    8.6625         0      3      0   \n",
       "4         3    0    0      1      1   12.2875         0      3      2   \n",
       "..      ...  ...  ...    ...    ...       ...       ...    ...    ...   \n",
       "413       3    1    0      0      0    8.0500         0      3      0   \n",
       "414       1    0    0      0      0  108.9000         0      1      3   \n",
       "415       3    1    0      0      0    7.2500         0      3      0   \n",
       "416       3    1    0      0      0    8.0500         0      3      0   \n",
       "417       3    1    0      1      1   22.3583         0      3      3   \n",
       "\n",
       "     Total_family_members  Fare_Per_Person  \n",
       "0                       0         7.829200  \n",
       "1                       1         3.500000  \n",
       "2                       0         9.687500  \n",
       "3                       0         8.662500  \n",
       "4                       2         4.095833  \n",
       "..                    ...              ...  \n",
       "413                     0         8.050000  \n",
       "414                     0       108.900000  \n",
       "415                     0         7.250000  \n",
       "416                     0         8.050000  \n",
       "417                     2         7.452767  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop(['Cabin'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test_data1 =[test] # combining train and test dataset\n",
    "\n",
    "for dataset1 in train_test_data1:\n",
    "    dataset1['Title'] = dataset1['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        240\n",
       "Miss       78\n",
       "Mrs        72\n",
       "Master     21\n",
       "Rev         2\n",
       "Col         2\n",
       "Dona        1\n",
       "Dr          1\n",
       "Ms          1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1.Title.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_mapping={'Mr':0,'Miss':1,\"Mrs\":2,\"Master\":3,'Dr':3,'Rev':3,'Col':3,'Mlle':3,'Major':3,'Ms':3,'Capt':3,'Countess':3,'Don':3,\n",
    "'Sir':3,'Lady':3,'Jonkheer':3,'Mme':3,'Dona':3}\n",
    "for dataset in train_test_data1:\n",
    "    dataset['Title']=dataset['Title'].map(title_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "      <th>Total_family_members</th>\n",
       "      <th>Fare_Per_Person</th>\n",
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       "      <td>7.8292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>3.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  SibSp  Parch    Fare  Embarked  cabin  Title  \\\n",
       "0       3    1    0      0      0  7.8292         1      3      0   \n",
       "1       3    0    0      1      0  7.0000         0      3      2   \n",
       "2       2    1    0      0      0  9.6875         1      2      0   \n",
       "\n",
       "   Total_family_members  Fare_Per_Person  \n",
       "0                     0           7.8292  \n",
       "1                     1           3.5000  \n",
       "2                     0           9.6875  "
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "      <th>Total_family_members</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.829200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.662500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4.095833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.879200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35.641650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.854200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>75.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>37.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.225000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>238 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex  Age  SibSp  Parch     Fare  Embarked  cabin  Title  \\\n",
       "0         3    1    0      0      0   7.8292         1      3      0   \n",
       "1         3    0    0      1      0   7.0000         0      3      2   \n",
       "2         2    1    0      0      0   9.6875         1      2      0   \n",
       "3         3    1    0      0      0   8.6625         0      3      0   \n",
       "4         3    0    0      1      1  12.2875         0      3      2   \n",
       "..      ...  ...  ...    ...    ...      ...       ...    ...    ...   \n",
       "233       3    1    0      0      0   7.8792         1      3      0   \n",
       "234       1    1    0      1      0  71.2833         0      1      0   \n",
       "235       3    1    0      0      0   7.8542         0      3      0   \n",
       "236       1    1    0      1      0  75.2500         0      1      0   \n",
       "237       3    1    0      0      0   7.2250         0      3      0   \n",
       "\n",
       "     Total_family_members  Fare_Per_Person  \n",
       "0                       0         7.829200  \n",
       "1                       1         3.500000  \n",
       "2                       0         9.687500  \n",
       "3                       0         8.662500  \n",
       "4                       2         4.095833  \n",
       "..                    ...              ...  \n",
       "233                     0         7.879200  \n",
       "234                     1        35.641650  \n",
       "235                     0         7.854200  \n",
       "236                     1        37.625000  \n",
       "237                     0         7.225000  \n",
       "\n",
       "[238 rows x 11 columns]"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(238)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    240\n",
       "1     78\n",
       "2     72\n",
       "3     28\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.Title.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.Title.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop(['Name'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop(['Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>62.0</td>\n",
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       "      <td>895</td>\n",
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       "      <th>4</th>\n",
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       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>1305</td>\n",
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       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "      <td>1309</td>\n",
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       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "  </tbody>\n",
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       "<p>418 rows × 10 columns</p>\n",
       "</div>"
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       "     PassengerId  Pclass     Sex   Age  SibSp  Parch      Fare Embarked  \\\n",
       "0            892       3    male  34.5      0      0    7.8292        Q   \n",
       "1            893       3  female  47.0      1      0    7.0000        S   \n",
       "2            894       2    male  62.0      0      0    9.6875        Q   \n",
       "3            895       3    male  27.0      0      0    8.6625        S   \n",
       "4            896       3  female  22.0      1      1   12.2875        S   \n",
       "..           ...     ...     ...   ...    ...    ...       ...      ...   \n",
       "413         1305       3    male   NaN      0      0    8.0500        S   \n",
       "414         1306       1  female  39.0      0      0  108.9000        C   \n",
       "415         1307       3    male  38.5      0      0    7.2500        S   \n",
       "416         1308       3    male   NaN      0      0    8.0500        S   \n",
       "417         1309       3    male   NaN      1      1   22.3583        C   \n",
       "\n",
       "     cabin  Title  \n",
       "0        3      0  \n",
       "1        3      2  \n",
       "2        2      0  \n",
       "3        3      0  \n",
       "4        3      2  \n",
       "..     ...    ...  \n",
       "413      3      0  \n",
       "414      1      3  \n",
       "415      3      0  \n",
       "416      3      0  \n",
       "417      3      3  \n",
       "\n",
       "[418 rows x 10 columns]"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0\n",
       "Pclass          0\n",
       "Sex             0\n",
       "Age            86\n",
       "SibSp           0\n",
       "Parch           0\n",
       "Fare            1\n",
       "Embarked        0\n",
       "cabin           0\n",
       "Title           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0    17\n",
       "21.0    17\n",
       "22.0    16\n",
       "30.0    15\n",
       "18.0    13\n",
       "        ..\n",
       "7.0      1\n",
       "60.5     1\n",
       "26.5     1\n",
       "76.0     1\n",
       "34.5     1\n",
       "Name: Age, Length: 79, dtype: int64"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.Age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.6271884892086"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.Fare.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.75\n",
       "dtype: float64"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.Fare.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.4542"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.Fare.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   PassengerId  Pclass   Sex   Age  SibSp  Parch    Fare Embarked  cabin  \\\n",
       "0          892       3  male  34.5      0      0  7.8292        Q      3   \n",
       "\n",
       "   Title  \n",
       "0      0  "
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass     Sex   Age  SibSp  Parch      Fare Embarked  \\\n",
       "0            892       3    male  34.5      0      0    7.8292        Q   \n",
       "1            893       3  female  47.0      1      0    7.0000        S   \n",
       "2            894       2    male  62.0      0      0    9.6875        Q   \n",
       "3            895       3    male  27.0      0      0    8.6625        S   \n",
       "4            896       3  female  22.0      1      1   12.2875        S   \n",
       "..           ...     ...     ...   ...    ...    ...       ...      ...   \n",
       "413         1305       3    male   NaN      0      0    8.0500        S   \n",
       "414         1306       1  female  39.0      0      0  108.9000        C   \n",
       "415         1307       3    male  38.5      0      0    7.2500        S   \n",
       "416         1308       3    male   NaN      0      0    8.0500        S   \n",
       "417         1309       3    male   NaN      1      1   22.3583        C   \n",
       "\n",
       "     cabin  Title  \n",
       "0        3      0  \n",
       "1        3      2  \n",
       "2        2      0  \n",
       "3        3      0  \n",
       "4        3      2  \n",
       "..     ...    ...  \n",
       "413      3      0  \n",
       "414      1      3  \n",
       "415      3      0  \n",
       "416      3      0  \n",
       "417      3      3  \n",
       "\n",
       "[418 rows x 10 columns]"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ccc8e5fac8>"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(x='Pclass',y='Age',data=test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_aget(cols):          #data cleaning\n",
    "    Age=cols[0]\n",
    "    Pclass=cols[1]\n",
    "    if pd.isnull(Age):\n",
    "        if Pclass==1:\n",
    "            return 42\n",
    "        elif Pclass==2:\n",
    "            return 27\n",
    "        elif Pclass==3:\n",
    "            return 24\n",
    "    else:\n",
    "        return Age\n",
    "test[\"Age\"]=test[['Age','Pclass']].apply(impute_aget,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Pclass         0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Fare           1\n",
       "Embarked       0\n",
       "cabin          0\n",
       "Title          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:253: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  res_values = method(rvalues)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Series([], Name: cabin, dtype: int64)"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test['Fare']==\"NaN\"]['cabin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Fare'].fillna(\n",
    "    \n",
    "    value=7.0,\n",
    "    axis=0,\n",
    "    inplace=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.7500     21\n",
       "26.0000    19\n",
       "8.0500     17\n",
       "13.0000    17\n",
       "7.8958     11\n",
       "           ..\n",
       "9.3250      1\n",
       "14.4583     1\n",
       "15.0333     1\n",
       "25.4667     1\n",
       "21.0750     1\n",
       "Name: Fare, Length: 169, dtype: int64"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Fare'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
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       "   PassengerId  Pclass     Sex   Age  SibSp  Parch     Fare Embarked  cabin  \\\n",
       "0          892       3    male  34.5      0      0   7.8292        Q      3   \n",
       "1          893       3  female  47.0      1      0   7.0000        S      3   \n",
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       "\n",
       "   Title  \n",
       "0      0  \n",
       "1      2  \n",
       "2      0  \n",
       "3      0  \n",
       "4      2  "
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     "execution_count": 367,
     "metadata": {},
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    "test.head(5)"
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  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Total_family_members']=test['SibSp']+test['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.Age=pd.cut(test.Age,bins=[0,18,25,99],labels=['child','young Adult','adult']).head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
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       "     PassengerId  Pclass     Sex    Age  SibSp  Parch      Fare Embarked  \\\n",
       "0            892       3    male  adult      0      0    7.8292        Q   \n",
       "1            893       3  female  adult      1      0    7.0000        S   \n",
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       "\n",
       "     cabin  Title  Total_family_members  \n",
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       "1        3      2                     1  \n",
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       "3        3      0                     0  \n",
       "4        3      2                     2  \n",
       "..     ...    ...                   ...  \n",
       "413      3      0                     0  \n",
       "414      1      3                     0  \n",
       "415      3      0                     0  \n",
       "416      3      0                     0  \n",
       "417      3      3                     2  \n",
       "\n",
       "[418 rows x 11 columns]"
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     },
     "execution_count": 370,
     "metadata": {},
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    "test"
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  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Fare_Per_Person']=test['Fare']/(test['Total_family_members']+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
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       "<p>418 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass     Sex    Age  SibSp  Parch      Fare Embarked  \\\n",
       "0            892       3    male  adult      0      0    7.8292        Q   \n",
       "1            893       3  female  adult      1      0    7.0000        S   \n",
       "2            894       2    male  adult      0      0    9.6875        Q   \n",
       "3            895       3    male  adult      0      0    8.6625        S   \n",
       "4            896       3  female    NaN      1      1   12.2875        S   \n",
       "..           ...     ...     ...    ...    ...    ...       ...      ...   \n",
       "413         1305       3    male    NaN      0      0    8.0500        S   \n",
       "414         1306       1  female    NaN      0      0  108.9000        C   \n",
       "415         1307       3    male    NaN      0      0    7.2500        S   \n",
       "416         1308       3    male    NaN      0      0    8.0500        S   \n",
       "417         1309       3    male    NaN      1      1   22.3583        C   \n",
       "\n",
       "     cabin  Title  Total_family_members  Fare_Per_Person  \n",
       "0        3      0                     0         7.829200  \n",
       "1        3      2                     1         3.500000  \n",
       "2        2      0                     0         9.687500  \n",
       "3        3      0                     0         8.662500  \n",
       "4        3      2                     2         4.095833  \n",
       "..     ...    ...                   ...              ...  \n",
       "413      3      0                     0         8.050000  \n",
       "414      1      3                     0       108.900000  \n",
       "415      3      0                     0         7.250000  \n",
       "416      3      0                     0         8.050000  \n",
       "417      3      3                     2         7.452767  \n",
       "\n",
       "[418 rows x 12 columns]"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Age']=pd.get_dummies(test['Age'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Sex']=pd.get_dummies(test['Sex'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Embarked']=pd.get_dummies(test['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  SibSp  Parch    Fare  Embarked  cabin  \\\n",
       "0          892       3    1    0      0      0  7.8292         1      3   \n",
       "1          893       3    0    0      1      0  7.0000         0      3   \n",
       "2          894       2    1    0      0      0  9.6875         1      2   \n",
       "3          895       3    1    0      0      0  8.6625         0      3   \n",
       "\n",
       "   Title  Total_family_members  Fare_Per_Person  \n",
       "0      0                     0           7.8292  \n",
       "1      2                     1           3.5000  \n",
       "2      0                     0           9.6875  \n",
       "3      0                     0           8.6625  "
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop(['PassengerId'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <th>Total_family_members</th>\n",
       "      <th>Fare_Per_Person</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  SibSp  Parch    Fare  Embarked  cabin  Title  \\\n",
       "0       3    1    0      0      0  7.8292         1      3      0   \n",
       "1       3    0    0      1      0  7.0000         0      3      2   \n",
       "\n",
       "   Total_family_members  Fare_Per_Person  \n",
       "0                     0           7.8292  \n",
       "1                     1           3.5000  "
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>108.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7.452767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex  Age  SibSp  Parch      Fare  Embarked  cabin  Title  \\\n",
       "0         3    1    0      0      0    7.8292         1      3      0   \n",
       "1         3    0    0      1      0    7.0000         0      3      2   \n",
       "2         2    1    0      0      0    9.6875         1      2      0   \n",
       "3         3    1    0      0      0    8.6625         0      3      0   \n",
       "4         3    0    0      1      1   12.2875         0      3      2   \n",
       "..      ...  ...  ...    ...    ...       ...       ...    ...    ...   \n",
       "413       3    1    0      0      0    8.0500         0      3      0   \n",
       "414       1    0    0      0      0  108.9000         0      1      3   \n",
       "415       3    1    0      0      0    7.2500         0      3      0   \n",
       "416       3    1    0      0      0    8.0500         0      3      0   \n",
       "417       3    1    0      1      1   22.3583         0      3      3   \n",
       "\n",
       "     Total_family_members  Fare_Per_Person  \n",
       "0                       0         7.829200  \n",
       "1                       1         3.500000  \n",
       "2                       0         9.687500  \n",
       "3                       0         8.662500  \n",
       "4                       2         4.095833  \n",
       "..                    ...              ...  \n",
       "413                     0         8.050000  \n",
       "414                     0       108.900000  \n",
       "415                     0         7.250000  \n",
       "416                     0         8.050000  \n",
       "417                     2         7.452767  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.87348191,  0.75592895,  0.        , ..., -0.75403317,\n",
       "        -0.5534426 , -0.39198133],\n",
       "       [ 0.87348191, -1.32287566,  0.        , ...,  1.30600516,\n",
       "         0.10564289, -0.51371885],\n",
       "       [-0.31581919,  0.75592895,  0.        , ..., -0.75403317,\n",
       "        -0.5534426 , -0.33972575],\n",
       "       ...,\n",
       "       [ 0.87348191,  0.75592895,  0.        , ..., -0.75403317,\n",
       "        -0.5534426 , -0.40826849],\n",
       "       [ 0.87348191,  0.75592895,  0.        , ..., -0.75403317,\n",
       "        -0.5534426 , -0.38577241],\n",
       "       [ 0.87348191,  0.75592895,  0.        , ...,  2.33602432,\n",
       "         0.76472838, -0.40256667]])"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler1=StandardScaler()\n",
    "scaler1.fit(test)\n",
    "scaled_features1=scaler1.transform(test)\n",
    "scaled_features1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "      <th>Total_family_members</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.873482</td>\n",
       "      <td>0.755929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.499470</td>\n",
       "      <td>-0.400248</td>\n",
       "      <td>-0.497023</td>\n",
       "      <td>2.843757</td>\n",
       "      <td>0.873482</td>\n",
       "      <td>-0.754033</td>\n",
       "      <td>-0.553443</td>\n",
       "      <td>-0.391981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.873482</td>\n",
       "      <td>-1.322876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.616992</td>\n",
       "      <td>-0.400248</td>\n",
       "      <td>-0.511885</td>\n",
       "      <td>-0.351647</td>\n",
       "      <td>0.873482</td>\n",
       "      <td>1.306005</td>\n",
       "      <td>0.105643</td>\n",
       "      <td>-0.513719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.315819</td>\n",
       "      <td>0.755929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.499470</td>\n",
       "      <td>-0.400248</td>\n",
       "      <td>-0.463715</td>\n",
       "      <td>2.843757</td>\n",
       "      <td>-0.315819</td>\n",
       "      <td>-0.754033</td>\n",
       "      <td>-0.553443</td>\n",
       "      <td>-0.339726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.873482</td>\n",
       "      <td>0.755929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.499470</td>\n",
       "      <td>-0.400248</td>\n",
       "      <td>-0.482087</td>\n",
       "      <td>-0.351647</td>\n",
       "      <td>0.873482</td>\n",
       "      <td>-0.754033</td>\n",
       "      <td>-0.553443</td>\n",
       "      <td>-0.368549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.873482</td>\n",
       "      <td>-1.322876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.616992</td>\n",
       "      <td>0.619896</td>\n",
       "      <td>-0.417112</td>\n",
       "      <td>-0.351647</td>\n",
       "      <td>0.873482</td>\n",
       "      <td>1.306005</td>\n",
       "      <td>0.764728</td>\n",
       "      <td>-0.496964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>0.873482</td>\n",
       "      <td>0.755929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.499470</td>\n",
       "      <td>-0.400248</td>\n",
       "      <td>-0.493065</td>\n",
       "      <td>-0.351647</td>\n",
       "      <td>0.873482</td>\n",
       "      <td>-0.754033</td>\n",
       "      <td>-0.553443</td>\n",
       "      <td>-0.385772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>-1.505120</td>\n",
       "      <td>-1.322876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.499470</td>\n",
       "      <td>-0.400248</td>\n",
       "      <td>1.314568</td>\n",
       "      <td>-0.351647</td>\n",
       "      <td>-1.505120</td>\n",
       "      <td>2.336024</td>\n",
       "      <td>-0.553443</td>\n",
       "      <td>2.450139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>0.873482</td>\n",
       "      <td>0.755929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.499470</td>\n",
       "      <td>-0.400248</td>\n",
       "      <td>-0.507404</td>\n",
       "      <td>-0.351647</td>\n",
       "      <td>0.873482</td>\n",
       "      <td>-0.754033</td>\n",
       "      <td>-0.553443</td>\n",
       "      <td>-0.408268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>0.873482</td>\n",
       "      <td>0.755929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.499470</td>\n",
       "      <td>-0.400248</td>\n",
       "      <td>-0.493065</td>\n",
       "      <td>-0.351647</td>\n",
       "      <td>0.873482</td>\n",
       "      <td>-0.754033</td>\n",
       "      <td>-0.553443</td>\n",
       "      <td>-0.385772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>0.873482</td>\n",
       "      <td>0.755929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.616992</td>\n",
       "      <td>0.619896</td>\n",
       "      <td>-0.236604</td>\n",
       "      <td>-0.351647</td>\n",
       "      <td>0.873482</td>\n",
       "      <td>2.336024</td>\n",
       "      <td>0.764728</td>\n",
       "      <td>-0.402567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pclass       Sex  Age     SibSp     Parch      Fare  Embarked  \\\n",
       "0    0.873482  0.755929  0.0 -0.499470 -0.400248 -0.497023  2.843757   \n",
       "1    0.873482 -1.322876  0.0  0.616992 -0.400248 -0.511885 -0.351647   \n",
       "2   -0.315819  0.755929  0.0 -0.499470 -0.400248 -0.463715  2.843757   \n",
       "3    0.873482  0.755929  0.0 -0.499470 -0.400248 -0.482087 -0.351647   \n",
       "4    0.873482 -1.322876  0.0  0.616992  0.619896 -0.417112 -0.351647   \n",
       "..        ...       ...  ...       ...       ...       ...       ...   \n",
       "413  0.873482  0.755929  0.0 -0.499470 -0.400248 -0.493065 -0.351647   \n",
       "414 -1.505120 -1.322876  0.0 -0.499470 -0.400248  1.314568 -0.351647   \n",
       "415  0.873482  0.755929  0.0 -0.499470 -0.400248 -0.507404 -0.351647   \n",
       "416  0.873482  0.755929  0.0 -0.499470 -0.400248 -0.493065 -0.351647   \n",
       "417  0.873482  0.755929  0.0  0.616992  0.619896 -0.236604 -0.351647   \n",
       "\n",
       "        cabin     Title  Total_family_members  Fare_Per_Person  \n",
       "0    0.873482 -0.754033             -0.553443        -0.391981  \n",
       "1    0.873482  1.306005              0.105643        -0.513719  \n",
       "2   -0.315819 -0.754033             -0.553443        -0.339726  \n",
       "3    0.873482 -0.754033             -0.553443        -0.368549  \n",
       "4    0.873482  1.306005              0.764728        -0.496964  \n",
       "..        ...       ...                   ...              ...  \n",
       "413  0.873482 -0.754033             -0.553443        -0.385772  \n",
       "414 -1.505120  2.336024             -0.553443         2.450139  \n",
       "415  0.873482 -0.754033             -0.553443        -0.408268  \n",
       "416  0.873482 -0.754033             -0.553443        -0.385772  \n",
       "417  0.873482  2.336024              0.764728        -0.402567  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df1=pd.DataFrame(scaled_features1,columns=test.columns)\n",
    "test_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "      <th>Total_family_members</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  SibSp  Parch    Fare  Embarked  cabin  Title  \\\n",
       "0       3    1    0      0      0  7.8292         1      3      0   \n",
       "1       3    0    0      1      0  7.0000         0      3      2   \n",
       "2       2    1    0      0      0  9.6875         1      2      0   \n",
       "\n",
       "   Total_family_members  Fare_Per_Person  \n",
       "0                     0           7.8292  \n",
       "1                     1           3.5000  \n",
       "2                     0           9.6875  "
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "      <th>Total_family_members</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.873482</td>\n",
       "      <td>0.755929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.499470</td>\n",
       "      <td>-0.400248</td>\n",
       "      <td>-0.497023</td>\n",
       "      <td>2.843757</td>\n",
       "      <td>0.873482</td>\n",
       "      <td>-0.754033</td>\n",
       "      <td>-0.553443</td>\n",
       "      <td>-0.391981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.873482</td>\n",
       "      <td>-1.322876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.616992</td>\n",
       "      <td>-0.400248</td>\n",
       "      <td>-0.511885</td>\n",
       "      <td>-0.351647</td>\n",
       "      <td>0.873482</td>\n",
       "      <td>1.306005</td>\n",
       "      <td>0.105643</td>\n",
       "      <td>-0.513719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass       Sex  Age     SibSp     Parch      Fare  Embarked     cabin  \\\n",
       "0  0.873482  0.755929  0.0 -0.499470 -0.400248 -0.497023  2.843757  0.873482   \n",
       "1  0.873482 -1.322876  0.0  0.616992 -0.400248 -0.511885 -0.351647  0.873482   \n",
       "\n",
       "      Title  Total_family_members  Fare_Per_Person  \n",
       "0 -0.754033             -0.553443        -0.391981  \n",
       "1  1.306005              0.105643        -0.513719  "
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knnclassifier = KNeighborsClassifier(n_neighbors=18,leaf_size=19,p=1)\n",
    "knnclassifier.fit(xs_train,ys_train)\n",
    "predknn1 = knnclassifier.predict(test_df1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1=pd.DataFrame(predknn1)\n",
    "sub=pd.read_csv('gender_submission.csv')\n",
    "datasets1=pd.concat([sub['PassengerId'],p1],axis=1)\n",
    "datasets1.columns=['PassengerId','Survived']\n",
    "datasets1.to_csv('kr01.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knnclassifierr = KNeighborsClassifier(n_neighbors=9,leaf_size=1,p=1)\n",
    "knnclassifierr.fit(xs_train,ys_train)\n",
    "predknn1r = knnclassifierr.predict(test_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1=pd.DataFrame(predknn1r)\n",
    "sub=pd.read_csv('gender_submission.csv')\n",
    "datasets1=pd.concat([sub['PassengerId'],p1],axis=1)\n",
    "datasets1.columns=['PassengerId','Survived']\n",
    "datasets1.to_csv('kr2.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfctest = RandomForestClassifier(n_estimators=40)\n",
    "rfctest.fit(X_train, y_train)\n",
    "rfc_pred = rfctest.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "p2=pd.DataFrame(rfc_pred)\n",
    "sub1=pd.read_csv('gender_submission.csv')\n",
    "datasets2=pd.concat([sub['PassengerId'],p2],axis=1)\n",
    "datasets2.columns=['PassengerId','Survived']\n",
    "datasets2.to_csv('Randomfnew1.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfcsc = RandomForestClassifier(n_estimators=40)\n",
    "rfcsc.fit(xs_train, ys_train)\n",
    "rfc_predsc = rfcsc.predict(test_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "p3=pd.DataFrame(rfc_predsc)\n",
    "sub2=pd.read_csv('gender_submission.csv')\n",
    "datasets3=pd.concat([sub2['PassengerId'],p3],axis=1)\n",
    "datasets3.columns=['PassengerId','Survived']\n",
    "datasets3.to_csv('rfscaled1new1.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtreet = DecisionTreeClassifier(criterion='gini',min_samples_leaf=5,min_samples_split=25)\n",
    "dtreet.fit(X_train,y_train)\n",
    "predtreet = dtreet.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "p4=pd.DataFrame(predtreet)\n",
    "sub3=pd.read_csv('gender_submission.csv')\n",
    "datasets4=pd.concat([sub3['PassengerId'],p4],axis=1)\n",
    "datasets4.columns=['PassengerId','Survived']\n",
    "datasets4.to_csv('dr1.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtreet = DecisionTreeClassifier(criterion='gini',min_samples_leaf=10,min_samples_split=35)\n",
    "dtreet.fit(X_train,y_train)\n",
    "predtreett = dtreet.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "p44=pd.DataFrame(predtreett)\n",
    "sub33=pd.read_csv('gender_submission.csv')\n",
    "datasets44=pd.concat([sub33['PassengerId'],p44],axis=1)\n",
    "datasets44.columns=['PassengerId','Survived']\n",
    "datasets44.to_csv('dr22.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnbtest = GaussianNB()\n",
    "gnbtest.fit(X_train, y_train)\n",
    "y_predgnbt =gnbtest.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "p5=pd.DataFrame(y_predgnbt)\n",
    "sub4=pd.read_csv('gender_submission.csv')\n",
    "datasets5=pd.concat([sub4['PassengerId'],p5],axis=1)\n",
    "datasets5.columns=['PassengerId','Survived']\n",
    "datasets5.to_csv('gausnbnew1.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnbst = GaussianNB()\n",
    "gnbst.fit(xs_train, ys_train)\n",
    "y_predgnbst =gnbst.predict(test_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "p6=pd.DataFrame(y_predgnbst)\n",
    "sub5=pd.read_csv('gender_submission.csv')\n",
    "datasets6=pd.concat([sub5['PassengerId'],p6],axis=1)\n",
    "datasets6.columns=['PassengerId','Survived']\n",
    "datasets6.to_csv('gausnbscaling1new.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "regressortest = xgb.XGBClassifier(\n",
    "    n_estimators=100,\n",
    "    reg_lambda=1,\n",
    "    gamma=0,\n",
    "    max_depth=3\n",
    ")\n",
    "regressortest.fit(X_train, y_train)\n",
    "y_predxtest = regressor.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "p8=pd.DataFrame(y_predxtest)\n",
    "sub7=pd.read_csv('gender_submission.csv')\n",
    "datasets8=pd.concat([sub7['PassengerId'],p8],axis=1)\n",
    "datasets8.columns=['PassengerId','Survived']\n",
    "datasets8.to_csv('xgboostnoscaling1.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "regressorstests = xgb.XGBClassifier(\n",
    "    n_estimators=100,\n",
    "    reg_lambda=1,\n",
    "    gamma=0,\n",
    "    max_depth=3\n",
    ")\n",
    "regressorstests.fit(xs_train,ys_train)\n",
    "y_predxstests = regressor.predict(test_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "p9=pd.DataFrame(y_predxtest)\n",
    "sub8=pd.read_csv('gender_submission.csv')\n",
    "datasets9=pd.concat([sub8['PassengerId'],p9],axis=1)\n",
    "datasets9.columns=['PassengerId','Survived']\n",
    "datasets9.to_csv('xgboostscaling1new1.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, y_train)\n",
    "Y_predsto = sgd.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "p10=pd.DataFrame(Y_predsto)\n",
    "sub9=pd.read_csv('gender_submission.csv')\n",
    "datasets10=pd.concat([sub9['PassengerId'],p10],axis=1)\n",
    "datasets10.columns=['PassengerId','Survived']\n",
    "datasets10.to_csv('sgd1.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes','xgboost', \n",
    "              'Decision Tree'],\n",
    "    'Score': [metrics.accuracy_score(ys_test,predsvcs),metrics.accuracy_score(ys_test,predknn),cross_val_score(logmodel,x,y,cv=10, scoring = 'accuracy').mean(),\n",
    "metrics.accuracy_score(ys_test,rfc_preds),metrics.accuracy_score(y_test,y_predmnb),metrics.accuracy_score(y_test,y_predx),metrics.accuracy_score(y_test,predtree)],\n",
    "\"CV_score\":[cross_val_score(svcs, xs, ys, cv=10, scoring = 'accuracy').mean(),cross_val_score(knnclassifier, xs, ys, cv=10, scoring = 'accuracy').mean(),\n",
    "cross_val_score(logmodels, xs, ys, cv=10, scoring = 'accuracy').mean(),cross_val_score(rfcs, xs,ys, cv=10, scoring = 'accuracy').mean(),cross_val_score(dtree, x, y, cv=10, scoring = 'accuracy').mean(),cross_val_score(dtree, x, y, cv=10, scoring = 'accuracy').mean(),cross_val_score(dtree, x, y, cv=10, scoring = 'accuracy').mean()]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "      <th>CV_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>0.605381</td>\n",
       "      <td>0.822672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.811659</td>\n",
       "      <td>0.817091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.801336</td>\n",
       "      <td>0.800212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.798206</td>\n",
       "      <td>0.826067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.784753</td>\n",
       "      <td>0.828327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>xgboost</td>\n",
       "      <td>0.780269</td>\n",
       "      <td>0.833945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.802691</td>\n",
       "      <td>0.833945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model     Score  CV_score\n",
       "0  Support Vector Machines  0.605381  0.822672\n",
       "1                      KNN  0.811659  0.817091\n",
       "2      Logistic Regression  0.801336  0.800212\n",
       "3            Random Forest  0.798206  0.826067\n",
       "4              Naive Bayes  0.784753  0.828327\n",
       "5                  xgboost  0.780269  0.833945\n",
       "6            Decision Tree  0.802691  0.833945"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1_train=s.drop(['PassengerId','Survived'],axis=1)\n",
    "y1_train=s['Survived']\n",
    "x1_test=test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "      <th>Total_family_members</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.62500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>35.64165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.92500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>26.55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.05000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5.86250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex  Age  SibSp  Parch     Fare  Embarked  cabin  Title  \\\n",
       "0         3    1    1      1      0   7.2500         0      3      0   \n",
       "1         1    0    0      1      0  71.2833         0      1      2   \n",
       "2         3    0    0      0      0   7.9250         0      3      1   \n",
       "3         1    0    0      1      0  53.1000         0      1      2   \n",
       "4         3    1    0      0      0   8.0500         0      3      0   \n",
       "..      ...  ...  ...    ...    ...      ...       ...    ...    ...   \n",
       "886       2    1    0      0      0  13.0000         0      2      3   \n",
       "887       1    0    0      0      0  30.0000         0      1      1   \n",
       "888       3    0    0      1      2  23.4500         0      3      1   \n",
       "889       1    1    0      0      0  30.0000         0      1      0   \n",
       "890       3    1    0      0      0   7.7500         1      3      0   \n",
       "\n",
       "     Total_family_members  Fare_Per_Person  \n",
       "0                       1          3.62500  \n",
       "1                       1         35.64165  \n",
       "2                       0          7.92500  \n",
       "3                       1         26.55000  \n",
       "4                       0          8.05000  \n",
       "..                    ...              ...  \n",
       "886                     0         13.00000  \n",
       "887                     0         30.00000  \n",
       "888                     3          5.86250  \n",
       "889                     0         30.00000  \n",
       "890                     0          7.75000  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>cabin</th>\n",
       "      <th>Title</th>\n",
       "      <th>Total_family_members</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.4625</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.231250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>27.719433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.629200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>106.4250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>106.425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.618067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>712 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex  Age  SibSp  Parch      Fare  Embarked  cabin  Title  \\\n",
       "205       3    0    0      0      1   10.4625         0      3      1   \n",
       "718       3    1    0      0      0   15.5000         1      3      0   \n",
       "835       1    0    0      1      1   83.1583         0      1      1   \n",
       "851       3    1    0      0      0    7.7750         0      3      0   \n",
       "773       3    1    0      0      0    7.2250         0      3      0   \n",
       "..      ...  ...  ...    ...    ...       ...       ...    ...    ...   \n",
       "579       3    1    0      0      0    7.9250         0      3      0   \n",
       "502       3    0    0      0      0    7.6292         1      3      1   \n",
       "537       1    0    0      0      0  106.4250         0      1      1   \n",
       "196       3    1    0      0      0    7.7500         1      3      0   \n",
       "175       3    1    0      1      1    7.8542         0      3      0   \n",
       "\n",
       "     Total_family_members  Fare_Per_Person  \n",
       "205                     1         5.231250  \n",
       "718                     0        15.500000  \n",
       "835                     2        27.719433  \n",
       "851                     0         7.775000  \n",
       "773                     0         7.225000  \n",
       "..                    ...              ...  \n",
       "579                     0         7.925000  \n",
       "502                     0         7.629200  \n",
       "537                     0       106.425000  \n",
       "196                     0         7.750000  \n",
       "175                     2         2.618067  \n",
       "\n",
       "[712 rows x 11 columns]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
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  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
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     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y1_train"
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  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "725    0\n",
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     "execution_count": 223,
     "metadata": {},
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   "source": [
    "y_test"
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   "execution_count": 224,
   "metadata": {},
   "outputs": [
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     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "predknn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>SibSp</th>\n",
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      "text/plain": [
       "   Pclass  Sex  Age  SibSp  Parch     Fare  Embarked  cabin  Title  \\\n",
       "0       3    1    1      1      0   7.2500         0      3      0   \n",
       "1       1    0    0      1      0  71.2833         0      1      2   \n",
       "\n",
       "   Total_family_members  Fare_Per_Person  \n",
       "0                     1          3.62500  \n",
       "1                     1         35.64165  "
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y1_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 228,
     "metadata": {},
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   "source": [
    "predknn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93.04"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(x1_train,y1_train)\n",
    "Y_pred = random_forest.predict(x1_test)\n",
    "random_forest.score(x1_train,y1_train)\n",
    "acc_random_forest = round(random_forest.score(x1_train,y1_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "2      1\n",
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       "889    1\n",
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       "Name: Survived, Length: 891, dtype: int64"
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     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y1_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "p21=pd.DataFrame(Y_predsto)\n",
    "sub21=pd.read_csv('gender_submission.csv')\n",
    "datasets21=pd.concat([sub21['PassengerId'],p21],axis=1)\n",
    "datasets21.columns=['PassengerId','Survived']\n",
    "datasets21.to_csv('21.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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