main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering using K-Means with Titanic Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then let's read train set and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train.csv')\n",
    "test_data = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 691,
   "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>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",
       "  </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   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  "
      ]
     },
     "execution_count": 691,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation and Checking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the number of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "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",
       "dtype: int64"
      ]
     },
     "execution_count": 693,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 694,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kayıp yaş oranı 19.87%\n"
     ]
    }
   ],
   "source": [
    "print('Kayıp yaş oranı %.2f%%' %((train_data['Age'].isnull().sum()/train_data.shape[0])*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kayıp kabin no oranı 77.10%\n"
     ]
    }
   ],
   "source": [
    "print('Kayıp kabin no oranı %.2f%%' %((train_data['Cabin'].isnull().sum()/train_data.shape[0])*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 697,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kayıp binilen liman adı 0.22%\n"
     ]
    }
   ],
   "source": [
    "print('Kayıp binilen liman adı %.2f%%' %((train_data['Embarked'].isnull().sum()/train_data.shape[0])*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's examine embarked column.<br>\n",
    "The number of passengers getting from S port is higher than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230fb0c6588>"
      ]
     },
     "execution_count": 699,
     "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.countplot(train_data['Embarked'], hue=train_data['Survived'], palette='BuPu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fill in the lost values according to the most preferred port. So with S."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 701,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data['Embarked'] = train_data['Embarked'].fillna(\"S\")\n",
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>445.357143</td>\n",
       "      <td>0.553571</td>\n",
       "      <td>1.886905</td>\n",
       "      <td>30.814769</td>\n",
       "      <td>0.386905</td>\n",
       "      <td>0.363095</td>\n",
       "      <td>59.954144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>417.896104</td>\n",
       "      <td>0.389610</td>\n",
       "      <td>2.909091</td>\n",
       "      <td>28.089286</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.168831</td>\n",
       "      <td>13.276030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>449.517028</td>\n",
       "      <td>0.339009</td>\n",
       "      <td>2.346749</td>\n",
       "      <td>29.519335</td>\n",
       "      <td>0.569659</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>27.243651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PassengerId  Survived    Pclass        Age     SibSp     Parch  \\\n",
       "Embarked                                                                   \n",
       "C          445.357143  0.553571  1.886905  30.814769  0.386905  0.363095   \n",
       "Q          417.896104  0.389610  2.909091  28.089286  0.428571  0.168831   \n",
       "S          449.517028  0.339009  2.346749  29.519335  0.569659  0.411765   \n",
       "\n",
       "               Fare  \n",
       "Embarked             \n",
       "C         59.954144  \n",
       "Q         13.276030  \n",
       "S         27.243651  "
      ]
     },
     "execution_count": 702,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Embarked']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that people boarding at port C pay the highest fare, with the highest survival rate. The most crowded port S but survival probability of C have more than others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another column with missing values is age column.<br>\n",
    "We find the average age value and replace it with missing data.<br>\n",
    "Since it will be a float value, we finally rounded the value using round() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 707,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_mean = train_data['Age'].mean()\n",
    "train_data['Age'] = train_data['Age'].replace(np.nan, age_mean)\n",
    "train_data['Age'] = train_data['Age'].round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While doing data analysis, every column may not be necessary for us.<br>\n",
    "Also, not removing the columns that are not required for analysis may distort our results.<br>\n",
    "There were many missing values in the cabin column. We cannot fill in so many missing values. This changes our results considerably. If we do, we can skew the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 708,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S\n",
       "3         1       1  female  35.0      1      0  53.1000        S\n",
       "4         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 708,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = ['PassengerId','Name','Ticket','Cabin']\n",
    "train_data = train_data.drop(d, axis=1)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 709,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One-Hot Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is one-hot encoding part.<br>\n",
    "Sex and embarked columns consist of categorical variables.<br>\n",
    "To convert categorical variables into numerical variables, we will use get_dummies() function of Pandas for this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></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>Q</th>\n",
       "      <th>S</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0</td>\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>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare  Q  S\n",
       "0         0       3    male  22.0      1      0   7.2500  0  1\n",
       "1         1       1  female  38.0      1      0  71.2833  0  0\n",
       "2         1       3  female  26.0      0      0   7.9250  0  1\n",
       "3         1       1  female  35.0      1      0  53.1000  0  1\n",
       "4         0       3    male  35.0      0      0   8.0500  0  1"
      ]
     },
     "execution_count": 710,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked = pd.get_dummies(train_data['Embarked'], drop_first = True)\n",
    "train_data = pd.concat([train_data, embarked], axis = 1)\n",
    "train_data.drop(['Embarked'] , axis = 1 , inplace = True) #now this can be deleted\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>male</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>38.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  Q  S  male\n",
       "0         0       3  22.0      1      0   7.2500  0  1     1\n",
       "1         1       1  38.0      1      0  71.2833  0  0     0\n",
       "2         1       3  26.0      0      0   7.9250  0  1     0\n",
       "3         1       1  35.0      1      0  53.1000  0  1     0\n",
       "4         0       3  35.0      0      0   8.0500  0  1     1"
      ]
     },
     "execution_count": 711,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex = pd.get_dummies(train_data['Sex'], drop_first = True)\n",
    "train_data = pd.concat([train_data, sex], axis = 1)\n",
    "train_data.drop(['Sex'] , axis = 1 , inplace = True) #now this can be deleted\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert the SibSp and Parch columns into one column. Thus, we can get a better data set.<br>\n",
    "I collect the SibSp and Parch column as TravelAlone in one column.<br>\n",
    "The SibSp and Parch column meant things like spouse, parent, child accompanying the journey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['TravelAlone'] = np.where((train_data['SibSp'] + train_data['Parch'])> 0, 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>male</th>\n",
       "      <th>TravelAlone</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  Q  S  male  TravelAlone\n",
       "0         0       3  22.0      1      0   7.2500  0  1     1            0\n",
       "1         1       1  38.0      1      0  71.2833  0  0     0            0\n",
       "2         1       3  26.0      0      0   7.9250  0  1     0            1\n",
       "3         1       1  35.0      1      0  53.1000  0  1     0            0\n",
       "4         0       3  35.0      0      0   8.0500  0  1     1            1"
      ]
     },
     "execution_count": 713,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>male</th>\n",
       "      <th>TravelAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age     Fare  Q  S  male  TravelAlone\n",
       "0         0       3  22.0   7.2500  0  1     1            0\n",
       "1         1       1  38.0  71.2833  0  0     0            0\n",
       "2         1       3  26.0   7.9250  0  1     0            1\n",
       "3         1       1  35.0  53.1000  0  1     0            0\n",
       "4         0       3  35.0   8.0500  0  1     1            1"
      ]
     },
     "execution_count": 714,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = train_data.drop(['SibSp','Parch'], axis=1)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do the same for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 715,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 715,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Age'] = train_data['Age'].replace(np.nan, age_mean)\n",
    "test_data['Age'] = test_data['Age'].round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "metadata": {},
   "outputs": [],
   "source": [
    "fare_median = test_data['Fare'].median(skipna=True)\n",
    "test_data['Fare'] = test_data['Fare'].replace(np.nan, fare_median)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found that there was too much missing value in the cabin column. If we complete this missing data, we probably distort our result.<br>\n",
    "Therefore, we are removing this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "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",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0       3    male  22.0      0      0   7.8292        Q\n",
       "1       3  female  38.0      1      0   7.0000        S\n",
       "2       2    male  26.0      0      0   9.6875        Q\n",
       "3       3    male  35.0      0      0   8.6625        S\n",
       "4       3  female  35.0      1      1  12.2875        S"
      ]
     },
     "execution_count": 718,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = test_data.drop('Cabin', axis=1)\n",
    "drop_t = ['PassengerId','Name','Ticket']\n",
    "test_data = test_data.drop(drop_t, axis=1)\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 719,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0       3    male  22.0      0      0   7.8292        Q\n",
       "1       3  female  38.0      1      0   7.0000        S\n",
       "2       2    male  26.0      0      0   9.6875        Q\n",
       "3       3    male  35.0      0      0   8.6625        S\n",
       "4       3  female  35.0      1      1  12.2875        S"
      ]
     },
     "execution_count": 720,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>Q</th>\n",
       "      <th>S</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch     Fare  Q  S\n",
       "0       3    male  22.0      0      0   7.8292  1  0\n",
       "1       3  female  38.0      1      0   7.0000  0  1\n",
       "2       2    male  26.0      0      0   9.6875  1  0\n",
       "3       3    male  35.0      0      0   8.6625  0  1\n",
       "4       3  female  35.0      1      1  12.2875  0  1"
      ]
     },
     "execution_count": 721,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked = pd.get_dummies(test_data['Embarked'], drop_first = True) #one-hot encoding\n",
    "test_data = pd.concat([test_data, embarked], axis = 1)\n",
    "test_data.drop(['Embarked'] , axis = 1 , inplace = True)\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age  SibSp  Parch     Fare  Q  S  male\n",
       "0       3  22.0      0      0   7.8292  1  0     1\n",
       "1       3  38.0      1      0   7.0000  0  1     0\n",
       "2       2  26.0      0      0   9.6875  1  0     1\n",
       "3       3  35.0      0      0   8.6625  0  1     1\n",
       "4       3  35.0      1      1  12.2875  0  1     0"
      ]
     },
     "execution_count": 722,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex = pd.get_dummies(test_data['Sex'], drop_first = True) #one-hot encoding\n",
    "test_data = pd.concat([test_data, sex], axis = 1)\n",
    "test_data.drop(['Sex'] , axis = 1 , inplace = True)\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age  SibSp  Parch     Fare  Q  S  male\n",
       "0       3  22.0      0      0   7.8292  1  0     1\n",
       "1       3  38.0      1      0   7.0000  0  1     0\n",
       "2       2  26.0      0      0   9.6875  1  0     1\n",
       "3       3  35.0      0      0   8.6625  0  1     1\n",
       "4       3  35.0      1      1  12.2875  0  1     0"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['TravelAlone'] = np.where((test_data['SibSp']+test_data['Parch'])>0, 0, 1)\n",
    "test_data = test_data.drop(['SibSp','Parch'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>male</th>\n",
       "      <th>TravelAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age     Fare  Q  S  male  TravelAlone\n",
       "0       3  22.0   7.8292  1  0     1            1\n",
       "1       3  38.0   7.0000  0  1     0            0\n",
       "2       2  26.0   9.6875  1  0     1            1\n",
       "3       3  35.0   8.6625  0  1     1            1\n",
       "4       3  35.0  12.2875  0  1     0            0"
      ]
     },
     "execution_count": 725,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas dataframe.corr() is used to find the pairwise correlation of all columns in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>male</th>\n",
       "      <th>TravelAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.070324</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>0.003650</td>\n",
       "      <td>-0.149683</td>\n",
       "      <td>-0.543351</td>\n",
       "      <td>-0.203367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.330103</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.221009</td>\n",
       "      <td>0.074053</td>\n",
       "      <td>0.131900</td>\n",
       "      <td>0.135207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.070324</td>\n",
       "      <td>-0.330103</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090824</td>\n",
       "      <td>-0.011089</td>\n",
       "      <td>-0.021322</td>\n",
       "      <td>0.084586</td>\n",
       "      <td>0.181104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.090824</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117216</td>\n",
       "      <td>-0.162184</td>\n",
       "      <td>-0.182333</td>\n",
       "      <td>-0.271832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>0.003650</td>\n",
       "      <td>0.221009</td>\n",
       "      <td>-0.011089</td>\n",
       "      <td>-0.117216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.499421</td>\n",
       "      <td>-0.074115</td>\n",
       "      <td>0.086464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>-0.149683</td>\n",
       "      <td>0.074053</td>\n",
       "      <td>-0.021322</td>\n",
       "      <td>-0.162184</td>\n",
       "      <td>-0.499421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.119224</td>\n",
       "      <td>0.029074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>-0.543351</td>\n",
       "      <td>0.131900</td>\n",
       "      <td>0.084586</td>\n",
       "      <td>-0.182333</td>\n",
       "      <td>-0.074115</td>\n",
       "      <td>0.119224</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.303646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TravelAlone</th>\n",
       "      <td>-0.203367</td>\n",
       "      <td>0.135207</td>\n",
       "      <td>0.181104</td>\n",
       "      <td>-0.271832</td>\n",
       "      <td>0.086464</td>\n",
       "      <td>0.029074</td>\n",
       "      <td>0.303646</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived    Pclass       Age      Fare         Q         S  \\\n",
       "Survived     1.000000 -0.338481 -0.070324  0.257307  0.003650 -0.149683   \n",
       "Pclass      -0.338481  1.000000 -0.330103 -0.549500  0.221009  0.074053   \n",
       "Age         -0.070324 -0.330103  1.000000  0.090824 -0.011089 -0.021322   \n",
       "Fare         0.257307 -0.549500  0.090824  1.000000 -0.117216 -0.162184   \n",
       "Q            0.003650  0.221009 -0.011089 -0.117216  1.000000 -0.499421   \n",
       "S           -0.149683  0.074053 -0.021322 -0.162184 -0.499421  1.000000   \n",
       "male        -0.543351  0.131900  0.084586 -0.182333 -0.074115  0.119224   \n",
       "TravelAlone -0.203367  0.135207  0.181104 -0.271832  0.086464  0.029074   \n",
       "\n",
       "                 male  TravelAlone  \n",
       "Survived    -0.543351    -0.203367  \n",
       "Pclass       0.131900     0.135207  \n",
       "Age          0.084586     0.181104  \n",
       "Fare        -0.182333    -0.271832  \n",
       "Q           -0.074115     0.086464  \n",
       "S            0.119224     0.029074  \n",
       "male         1.000000     0.303646  \n",
       "TravelAlone  0.303646     1.000000  "
      ]
     },
     "execution_count": 726,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's examine again our data with heatmap, a data visualization technique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230fb573608>"
      ]
     },
     "execution_count": 727,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sns.heatmap(train_data.corr(), linewidths=5, annot=True, cmap='BuPu_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's interpret this.<br>\n",
    "\n",
    "Pclass has negative corelation with survived. We have seen the relationship between survived and ticket classes. We saw that the number 3 ticket class was the most crowded and had the highest death rate. Therefore, it makes sense that the correlation is negative.(-0.34)<br>\n",
    "\n",
    "We see that there is high negative correlation between survived and male. We have seen the relationship between survived and sex before with histogram. The death rate of men was higher than women. Therefore, it is very reasonable to see a high negative correlation here.(-0.54)<br>\n",
    "\n",
    "Addition, relationship between fare and pclass has negative correlation because the cheapest class is 3 also the most crowded class is 3.(-0.55)<br>\n",
    "\n",
    "1.0: positive correlation, when one attribute rises, the other attribute rises.<br>\n",
    "-1.0: negative correlation, when one attribute rises, the other attribute falls.<br>\n",
    "0.0: no correlation, two columns are not linearly related."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3.     22.      7.25   ...  1.      1.      0.    ]\n",
      " [ 1.     38.     71.2833 ...  0.      0.      0.    ]\n",
      " [ 3.     26.      7.925  ...  1.      0.      1.    ]\n",
      " ...\n",
      " [ 3.     30.     23.45   ...  1.      0.      0.    ]\n",
      " [ 1.     26.     30.     ...  0.      1.      1.    ]\n",
      " [ 3.     32.      7.75   ...  0.      1.      1.    ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Age', 'Fare', 'Q', 'S', 'male', 'TravelAlone'], dtype='object')"
      ]
     },
     "execution_count": 728,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X = train_data.drop('Survived', axis=1) \n",
    "#y = train_data['Survived'] \n",
    "X = np.array(train_data.drop(['Survived'], 1).astype(float)) #features\n",
    "y = np.array(train_data['Survived']) #target\n",
    "print(X)\n",
    "train_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I showed the X matrix. We had removed the 'Survived' section. The first column of the matrix is 'Pclass', the second column is 'Age', the third column is 'Fare'.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the train_test_split function in order to make the split. The test_size=0.2 inside the function indicates the percentage of the data that should be held over for testing. The separation is 80/20(80% training set, 20% test set)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we move on to the normalization section.\n",
    "We use the StandardScaler() function of the Sklearn library to normalize the data.\n",
    "Thanks to this function, we add bias to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train_sc = sc.fit_transform(X_train)\n",
    "X_test_sc = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KMeans -  Clustering for Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will try to increase the accuracy value by adding a preprocessing on the best two models(rf and svm) in my previous classification project.<br>\n",
    "I will use clustering for preprocessing step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before applying preprocessing, let's apply the svm model on this data set and take a look at the accuracy value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 733,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=30, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=0, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 733,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC(C = 30, gamma = 0.01,random_state=0) #apply the model\n",
    "svm.fit(X_train_sc, y_train) #training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 734,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.90      0.85       110\n",
      "           1       0.81      0.67      0.73        69\n",
      "\n",
      "    accuracy                           0.81       179\n",
      "   macro avg       0.81      0.78      0.79       179\n",
      "weighted avg       0.81      0.81      0.81       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "prediction_svm = svm.predict(X_test_sc)\n",
    "print(classification_report(y_test, prediction_svm))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is first accuracy value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 735,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8100558659217877\n"
     ]
    }
   ],
   "source": [
    "svm_acc1 = svm.score(X_test_sc,y_test)\n",
    "print(svm_acc1) #first accuracy for svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using clustering, we can further increase this accuracy value.<br>\n",
    "First, I gave the number of clusters randomly. I set the number of random clusters to 50.<br>\n",
    "Using pipeline, I putted together different pieces.I applied clustering first, then applied the model. Finally, training was applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 736,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('kmeans',\n",
       "                 KMeans(algorithm='auto', copy_x=True, init='k-means++',\n",
       "                        max_iter=300, n_clusters=50, n_init=10, n_jobs=None,\n",
       "                        precompute_distances='auto', random_state=1, tol=0.0001,\n",
       "                        verbose=0)),\n",
       "                ('svm',\n",
       "                 SVC(C=30, break_ties=False, cache_size=200, class_weight=None,\n",
       "                     coef0=0.0, decision_function_shape='ovr', degree=3,\n",
       "                     gamma=0.01, kernel='rbf', max_iter=-1, probability=False,\n",
       "                     random_state=0, shrinking=True, tol=0.001,\n",
       "                     verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 736,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_svm = Pipeline([\n",
    "    (\"kmeans\", KMeans(n_clusters=50, random_state=1)),\n",
    "    (\"svm\", SVC(C = 30, gamma = 0.01,random_state=0)),\n",
    "])\n",
    "pipeline_svm.fit(X_train_sc, y_train) #training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 737,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8212290502793296\n"
     ]
    }
   ],
   "source": [
    "svm_acc2 = pipeline_svm.score(X_test_sc,y_test)\n",
    "print(svm_acc2) #second accuracy for svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 738,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05882352941176461"
      ]
     },
     "execution_count": 738,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - (1 - svm_acc2) / (1 - svm_acc1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I reduced the error rate by over 5%. The accuracy value has increased, although not much.<br>\n",
    "For better accuracy, we need to find the optimum number of clusters. For this, cross-validation(cv) was applied.<br>\n",
    "Thanks to GridSearchCV, there is a exhaustive search over parameter values we have specified for the estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 739,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 98 candidates, totalling 490 fits\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=2, total=   0.0s\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=2, total=   0.0s\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=2, total=   0.0s\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=2, total=   0.0s\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=2, total=   0.0s\n",
      "[CV] kmeans__n_clusters=3 ............................................\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] ............................. kmeans__n_clusters=3, total=   0.1s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=3, total=   0.0s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=3, total=   0.0s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=3, total=   0.0s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=3, total=   0.1s\n",
      "[CV] kmeans__n_clusters=4 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=4, total=   0.1s\n",
      "[CV] kmeans__n_clusters=4 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=4, total=   0.0s\n",
      "[CV] kmeans__n_clusters=4 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=4, total=   0.0s\n",
      "[CV] kmeans__n_clusters=4 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=4, total=   0.1s\n",
      "[CV] kmeans__n_clusters=4 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=4, total=   0.3s\n",
      "[CV] kmeans__n_clusters=5 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=5, total=   0.1s\n",
      "[CV] kmeans__n_clusters=5 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=5, total=   0.1s\n",
      "[CV] kmeans__n_clusters=5 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=5, total=   0.0s\n",
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      "[CV] ............................. kmeans__n_clusters=5, total=   0.1s\n",
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      "[CV] kmeans__n_clusters=6 ............................................\n",
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      "[CV] kmeans__n_clusters=6 ............................................\n",
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      "[CV] ............................. kmeans__n_clusters=6, total=   0.1s\n",
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      "[CV] kmeans__n_clusters=7 ............................................\n",
      "[CV] ............................. kmeans__n_clusters=7, total=   0.1s\n",
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      "[CV] ............................. kmeans__n_clusters=8, total=   0.1s\n",
      "[CV] kmeans__n_clusters=8 ............................................\n",
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      "[CV] ............................ kmeans__n_clusters=10, total=   0.1s\n",
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      "[CV] ............................ kmeans__n_clusters=11, total=   0.1s\n",
      "[CV] kmeans__n_clusters=11 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=11, total=   0.1s\n",
      "[CV] kmeans__n_clusters=11 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=11, total=   0.1s\n",
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      "[CV] ............................ kmeans__n_clusters=12, total=   0.1s\n",
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      "[CV] ............................ kmeans__n_clusters=12, total=   0.1s\n",
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      "[CV] ............................ kmeans__n_clusters=13, total=   0.1s\n",
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      "[CV] kmeans__n_clusters=14 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=14, total=   0.1s\n",
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      "[CV] ............................ kmeans__n_clusters=14, total=   0.1s\n",
      "[CV] kmeans__n_clusters=14 ...........................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............................ kmeans__n_clusters=14, total=   0.1s\n",
      "[CV] kmeans__n_clusters=14 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=14, total=   0.1s\n",
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      "[CV] kmeans__n_clusters=26 ...........................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............................ kmeans__n_clusters=26, total=   0.1s\n",
      "[CV] kmeans__n_clusters=26 ...........................................\n",
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      "[CV] ............................ kmeans__n_clusters=49, total=   0.2s\n",
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     ]
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     ]
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      "[CV] ............................ kmeans__n_clusters=84, total=   0.3s\n",
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      "[CV] ............................ kmeans__n_clusters=90, total=   0.3s\n",
      "[CV] kmeans__n_clusters=90 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=90, total=   0.3s\n",
      "[CV] kmeans__n_clusters=90 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=90, total=   0.3s\n",
      "[CV] kmeans__n_clusters=90 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=90, total=   0.3s\n",
      "[CV] kmeans__n_clusters=91 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=91, total=   0.3s\n",
      "[CV] kmeans__n_clusters=91 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=91, total=   0.3s\n",
      "[CV] kmeans__n_clusters=91 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=91, total=   0.3s\n",
      "[CV] kmeans__n_clusters=91 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=91, total=   0.3s\n",
      "[CV] kmeans__n_clusters=91 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=91, total=   0.3s\n",
      "[CV] kmeans__n_clusters=92 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=92, total=   0.3s\n",
      "[CV] kmeans__n_clusters=92 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=92, total=   0.3s\n",
      "[CV] kmeans__n_clusters=92 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=92, total=   0.3s\n",
      "[CV] kmeans__n_clusters=92 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=92, total=   0.3s\n",
      "[CV] kmeans__n_clusters=92 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=92, total=   0.3s\n",
      "[CV] kmeans__n_clusters=93 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=93, total=   0.3s\n",
      "[CV] kmeans__n_clusters=93 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=93, total=   0.3s\n",
      "[CV] kmeans__n_clusters=93 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=93, total=   0.3s\n",
      "[CV] kmeans__n_clusters=93 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=93, total=   0.3s\n",
      "[CV] kmeans__n_clusters=93 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=93, total=   0.3s\n",
      "[CV] kmeans__n_clusters=94 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=94, total=   0.3s\n",
      "[CV] kmeans__n_clusters=94 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=94, total=   0.3s\n",
      "[CV] kmeans__n_clusters=94 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=94, total=   0.3s\n",
      "[CV] kmeans__n_clusters=94 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=94, total=   0.3s\n",
      "[CV] kmeans__n_clusters=94 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=94, total=   0.3s\n",
      "[CV] kmeans__n_clusters=95 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=95, total=   0.3s\n",
      "[CV] kmeans__n_clusters=95 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=95, total=   0.3s\n",
      "[CV] kmeans__n_clusters=95 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=95, total=   0.3s\n",
      "[CV] kmeans__n_clusters=95 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=95, total=   0.3s\n",
      "[CV] kmeans__n_clusters=95 ...........................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............................ kmeans__n_clusters=95, total=   0.3s\n",
      "[CV] kmeans__n_clusters=96 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=96, total=   0.3s\n",
      "[CV] kmeans__n_clusters=96 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=96, total=   0.3s\n",
      "[CV] kmeans__n_clusters=96 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=96, total=   0.3s\n",
      "[CV] kmeans__n_clusters=96 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=96, total=   0.3s\n",
      "[CV] kmeans__n_clusters=96 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=96, total=   0.3s\n",
      "[CV] kmeans__n_clusters=97 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=97, total=   0.3s\n",
      "[CV] kmeans__n_clusters=97 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=97, total=   0.4s\n",
      "[CV] kmeans__n_clusters=97 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=97, total=   0.4s\n",
      "[CV] kmeans__n_clusters=97 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=97, total=   0.3s\n",
      "[CV] kmeans__n_clusters=97 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=97, total=   0.3s\n",
      "[CV] kmeans__n_clusters=98 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=98, total=   0.3s\n",
      "[CV] kmeans__n_clusters=98 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=98, total=   0.3s\n",
      "[CV] kmeans__n_clusters=98 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=98, total=   0.3s\n",
      "[CV] kmeans__n_clusters=98 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=98, total=   0.3s\n",
      "[CV] kmeans__n_clusters=98 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=98, total=   0.3s\n",
      "[CV] kmeans__n_clusters=99 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=99, total=   0.3s\n",
      "[CV] kmeans__n_clusters=99 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=99, total=   0.3s\n",
      "[CV] kmeans__n_clusters=99 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=99, total=   0.3s\n",
      "[CV] kmeans__n_clusters=99 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=99, total=   0.3s\n",
      "[CV] kmeans__n_clusters=99 ...........................................\n",
      "[CV] ............................ kmeans__n_clusters=99, total=   0.3s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 490 out of 490 | elapsed:  1.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('kmeans',\n",
       "                                        KMeans(algorithm='auto', copy_x=True,\n",
       "                                               init='k-means++', max_iter=300,\n",
       "                                               n_clusters=50, n_init=10,\n",
       "                                               n_jobs=None,\n",
       "                                               precompute_distances='auto',\n",
       "                                               random_state=1, tol=0.0001,\n",
       "                                               verbose=0)),\n",
       "                                       ('svm',\n",
       "                                        SVC(C=30, break_ties=False,\n",
       "                                            cache_size=200, class_weight=None,\n",
       "                                            coef0=0.0,\n",
       "                                            decision_function_shape='ovr',\n",
       "                                            degree=3, gamma=0.01, kernel='rbf',\n",
       "                                            max_iter=-1, probability=False,\n",
       "                                            random_state=0, shrinking=True,\n",
       "                                            tol=0.001, verbose=False))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'kmeans__n_clusters': range(2, 100)},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=2)"
      ]
     },
     "execution_count": 739,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid_svm = dict(kmeans__n_clusters=range(2, 100))\n",
    "grid_clf_svm = GridSearchCV(pipeline_svm, param_grid_svm, cv=5, verbose=2)\n",
    "grid_clf_svm.fit(X_train_sc, y_train) #training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the best number of clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'kmeans__n_clusters': 17}"
      ]
     },
     "execution_count": 740,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf_svm.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8268156424581006\n"
     ]
    }
   ],
   "source": [
    "svm_acc3 = grid_clf_svm.score(X_test_sc, y_test)\n",
    "print(svm_acc3) #third accuracy for svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, a better accuracy value was obtained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03125000000000022"
      ]
     },
     "execution_count": 742,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - (1 - svm_acc3) / (1 - svm_acc2) # error rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 743,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Model  Accuracy  Random clustering  Optimum clustering\n",
      "0   SVM  0.810056           0.821229            0.826816\n"
     ]
    }
   ],
   "source": [
    "svm_table = pd.DataFrame( \n",
    "    {  'Model' : ['SVM'], \n",
    "       'Accuracy' : [svm_acc1], \n",
    "       'Random clustering' : [svm_acc2],\n",
    "       'Optimum clustering' : [svm_acc3]\n",
    "     \n",
    "    }) \n",
    "\n",
    "print(svm_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's repeat the same process for random forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "n_estimators: number of trees in the forest.<br>\n",
    "This parameter is used to determine the number of trees to be used in the process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='entropy', 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=20,\n",
       "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 745,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=20, criterion='entropy', random_state=0) #apply the model\n",
    "random_forest.fit(X_train_sc, y_train) #training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 746,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.90      0.86       110\n",
      "           1       0.81      0.70      0.75        69\n",
      "\n",
      "    accuracy                           0.82       179\n",
      "   macro avg       0.82      0.80      0.81       179\n",
      "weighted avg       0.82      0.82      0.82       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "prediction_rf = random_forest.predict(X_test_sc)\n",
    "print(classification_report(y_test, prediction_rf))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random forest has a higher accuracy than SVM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 747,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8212290502793296\n"
     ]
    }
   ],
   "source": [
    "rf_acc1 = random_forest.score(X_test_sc, y_test)\n",
    "print(rf_acc1) #first accuracy for rf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, I randomly determined the number of clusters -> n_clusters=20<br>\n",
    "We applied the model again after clustering. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 748,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('kmeans',\n",
       "                 KMeans(algorithm='auto', copy_x=True, init='k-means++',\n",
       "                        max_iter=300, n_clusters=20, n_init=10, n_jobs=None,\n",
       "                        precompute_distances='auto', random_state=0, tol=0.0001,\n",
       "                        verbose=0)),\n",
       "                ('random_forest',\n",
       "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                        class_weight=None, criterion='entropy',\n",
       "                                        max_depth=None, max_features='auto',\n",
       "                                        max_leaf_nodes=None, max_samples=None,\n",
       "                                        min_impurity_decrease=0.0,\n",
       "                                        min_impurity_split=None,\n",
       "                                        min_samples_leaf=1, min_samples_split=2,\n",
       "                                        min_weight_fraction_leaf=0.0,\n",
       "                                        n_estimators=20, n_jobs=None,\n",
       "                                        oob_score=False, random_state=1,\n",
       "                                        verbose=0, warm_start=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 748,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_rf = Pipeline([\n",
    "    (\"kmeans\", KMeans(n_clusters=20, random_state=0)),\n",
    "    (\"random_forest\", RandomForestClassifier(n_estimators=20, criterion='entropy', random_state=1)),\n",
    "])\n",
    "pipeline_rf.fit(X_train_sc, y_train) #training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy value increased slightly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8268156424581006\n"
     ]
    }
   ],
   "source": [
    "rf_acc2 = pipeline_rf.score(X_test_sc,y_test)\n",
    "print(rf_acc2) #second accuracy for rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 750,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03125000000000022"
      ]
     },
     "execution_count": 750,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - (1 - rf_acc2) / (1 - rf_acc1) #error rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I reduced the error rate by over 3%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can increase the accuracy value even more. We will use GridSearchCV () to find the optimum cluster value. I have explained in more detail in the SVM section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 98 candidates, totalling 980 fits\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ................ kmeans__n_clusters=2, score=0.667, total=   0.1s\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ................ kmeans__n_clusters=2, score=0.694, total=   0.1s\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ................ kmeans__n_clusters=2, score=0.732, total=   0.1s\n",
      "[CV] kmeans__n_clusters=2 ............................................\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",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ................ kmeans__n_clusters=2, score=0.775, total=   0.1s\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ................ kmeans__n_clusters=2, score=0.789, total=   0.1s\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ................ kmeans__n_clusters=2, score=0.634, total=   0.1s\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ................ kmeans__n_clusters=2, score=0.718, total=   0.1s\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ................ kmeans__n_clusters=2, score=0.634, total=   0.1s\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ................ kmeans__n_clusters=2, score=0.761, total=   0.1s\n",
      "[CV] kmeans__n_clusters=2 ............................................\n",
      "[CV] ................ kmeans__n_clusters=2, score=0.732, total=   0.1s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ................ kmeans__n_clusters=3, score=0.736, total=   0.1s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ................ kmeans__n_clusters=3, score=0.708, total=   0.1s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ................ kmeans__n_clusters=3, score=0.789, total=   0.1s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ................ kmeans__n_clusters=3, score=0.789, total=   0.1s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ................ kmeans__n_clusters=3, score=0.746, total=   0.1s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ................ kmeans__n_clusters=3, score=0.704, total=   0.1s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ................ kmeans__n_clusters=3, score=0.704, total=   0.1s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ................ kmeans__n_clusters=3, score=0.732, total=   0.1s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ................ kmeans__n_clusters=3, score=0.789, total=   0.1s\n",
      "[CV] kmeans__n_clusters=3 ............................................\n",
      "[CV] ................ kmeans__n_clusters=3, score=0.732, total=   0.1s\n",
      "[CV] kmeans__n_clusters=4 ............................................\n",
      "[CV] ................ kmeans__n_clusters=4, score=0.708, total=   0.1s\n",
      "[CV] kmeans__n_clusters=4 ............................................\n",
      "[CV] ................ kmeans__n_clusters=4, score=0.722, total=   0.1s\n",
      "[CV] kmeans__n_clusters=4 ............................................\n",
      "[CV] ................ kmeans__n_clusters=4, score=0.775, total=   0.1s\n",
      "[CV] kmeans__n_clusters=4 ............................................\n",
      "[CV] ................ kmeans__n_clusters=4, score=0.775, total=   0.1s\n",
      "[CV] kmeans__n_clusters=4 ............................................\n",
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     ]
    },
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     "output_type": "stream",
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      "[CV] ................ kmeans__n_clusters=8, score=0.736, total=   0.1s\n",
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      "[CV] ............... kmeans__n_clusters=14, score=0.764, total=   0.1s\n",
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      "[CV] ............... kmeans__n_clusters=31, score=0.803, total=   0.2s\n",
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      "[CV] ............... kmeans__n_clusters=60, score=0.746, total=   0.2s\n",
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      "[CV] ............... kmeans__n_clusters=72, score=0.736, total=   0.3s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ............... kmeans__n_clusters=89, score=0.746, total=   0.4s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "[CV] ............... kmeans__n_clusters=99, score=0.789, total=   0.4s\n",
      "[CV] kmeans__n_clusters=99 ...........................................\n",
      "[CV] ............... kmeans__n_clusters=99, score=0.803, total=   0.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 980 out of 980 | elapsed:  3.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('kmeans',\n",
       "                                        KMeans(algorithm='auto', copy_x=True,\n",
       "                                               init='k-means++', max_iter=300,\n",
       "                                               n_clusters=20, n_init=10,\n",
       "                                               n_jobs=None,\n",
       "                                               precompute_distances='auto',\n",
       "                                               random_state=0, tol=0.0001,\n",
       "                                               verbose=0)),\n",
       "                                       ('random_forest',\n",
       "                                        RandomForestClassifier(bootstrap=True,\n",
       "                                                               ccp_alpha=0.0,\n",
       "                                                               class_weight=None,\n",
       "                                                               criterion='e...\n",
       "                                                               min_impurity_split=None,\n",
       "                                                               min_samples_leaf=1,\n",
       "                                                               min_samples_split=2,\n",
       "                                                               min_weight_fraction_leaf=0.0,\n",
       "                                                               n_estimators=20,\n",
       "                                                               n_jobs=None,\n",
       "                                                               oob_score=False,\n",
       "                                                               random_state=1,\n",
       "                                                               verbose=0,\n",
       "                                                               warm_start=False))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'kmeans__n_clusters': range(2, 100)},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=4)"
      ]
     },
     "execution_count": 751,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid_rf = dict(kmeans__n_clusters=range(2, 100))\n",
    "grid_clf_rf = GridSearchCV(pipeline_rf, param_grid_rf, cv=10, verbose=4)\n",
    "grid_clf_rf.fit(X_train_sc, y_train) #training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the best number of clusters for random forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'kmeans__n_clusters': 21}"
      ]
     },
     "execution_count": 752,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf_rf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the new accuracy value. As you can see, the accuracy value(rf_acc3) is higher than previous value(rf_acc2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 753,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8324022346368715\n"
     ]
    }
   ],
   "source": [
    "rf_acc3 = grid_clf_rf.score(X_test_sc, y_test)\n",
    "print(rf_acc3) #third accuracy for rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03225806451612867"
      ]
     },
     "execution_count": 754,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - (1 - rf_acc3) / (1 - rf_acc2) #error rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Model  Accuracy  Random clustering  Optimum clustering\n",
      "0  Random Forest  0.821229           0.826816            0.832402\n"
     ]
    }
   ],
   "source": [
    "rf_table = pd.DataFrame( \n",
    "    {  'Model' : ['Random Forest'], \n",
    "       'Accuracy' : [rf_acc1], \n",
    "       'Random clustering' : [rf_acc2],\n",
    "       'Optimum clustering' : [rf_acc3]\n",
    "     \n",
    "    }) \n",
    "\n",
    "print(rf_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I combined all the accuracy values of models in a table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Model  Accuracy  Acc random clustering  Acc optimum clustering\n",
      "0  Random Forest  0.821229               0.826816                0.832402\n",
      "1            SVM  0.810056               0.821229                0.826816\n"
     ]
    }
   ],
   "source": [
    "table = pd.DataFrame( \n",
    "    {  'Model' : ['Random Forest', 'SVM'], \n",
    "       'Accuracy' : [rf_acc1, svm_acc1], \n",
    "       'Acc random clustering' : [rf_acc2, svm_acc2],\n",
    "       'Acc optimum clustering' : [rf_acc3, svm_acc3]\n",
    "     \n",
    "    }) \n",
    "\n",
    "table = table.sort_values(by='Accuracy', ascending=False) #sorting\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A better accuracy was achieved thanks to clustering. Random Forest has better accuracy values than SVM."
   ]
  }
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