Survive_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "292f7310",
   "metadata": {},
   "source": [
    "<img src=\"https://storage.googleapis.com/kaggle-media/welcome/screen1.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5980a34",
   "metadata": {},
   "source": [
    "# Hamda Hmida ®"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdba043c",
   "metadata": {},
   "source": [
    "## SETUP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "176ffb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba9a176",
   "metadata": {},
   "source": [
    "## Loading DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a92e180",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv(\"train.csv\")\n",
    "test=pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6fdf52b8",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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",
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       "      <td>1</td>\n",
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       "      <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",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>9</th>\n",
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       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "641cc847",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(891, 12)"
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       "      <td>27.0</td>\n",
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       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7538</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330972</td>\n",
       "      <td>7.6292</td>\n",
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       "      <th>7</th>\n",
       "      <td>899</td>\n",
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       "      <td>Caldwell, Mr. Albert Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>248738</td>\n",
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       "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
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       "      <td>Davies, Mr. John Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
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       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "5          897       3                    Svensson, Mr. Johan Cervin    male   \n",
       "6          898       3                          Connolly, Miss. Kate  female   \n",
       "7          899       2                  Caldwell, Mr. Albert Francis    male   \n",
       "8          900       3     Abrahim, Mrs. Joseph (Sophie Halaut Easu)  female   \n",
       "9          901       3                       Davies, Mr. John Samuel    male   \n",
       "\n",
       "    Age  SibSp  Parch     Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0     330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0     363272   7.0000   NaN        S  \n",
       "2  62.0      0      0     240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0     315154   8.6625   NaN        S  \n",
       "4  22.0      1      1    3101298  12.2875   NaN        S  \n",
       "5  14.0      0      0       7538   9.2250   NaN        S  \n",
       "6  30.0      0      0     330972   7.6292   NaN        Q  \n",
       "7  26.0      1      1     248738  29.0000   NaN        S  \n",
       "8  18.0      0      0       2657   7.2292   NaN        C  \n",
       "9  21.0      2      0  A/4 48871  24.1500   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f94b17fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea3590fd",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
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       "      <td>891.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7742a5c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>417.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.627188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.907576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
       "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
       "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
       "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
       "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
       "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
       "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
       "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
       "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054c5d16",
   "metadata": {},
   "source": [
    "## DATA Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "068c8b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "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(x='Sex', data=train, hue='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f16b1051",
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Sex', 'Survived']].groupby(['Sex']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "626f2585",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "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(x='Pclass', data=train, hue='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "53283c03",
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Pclass          \n",
       "1       0.629630\n",
       "2       0.472826\n",
       "3       0.242363"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Pclass', 'Survived']].groupby(['Pclass']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dcffb9be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Embarked', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rknmSz+qACcB04AFJA7r3dczM7EQUGwr/HZgXERURMZJcSCw51oCIaIuI55PpN4HN5I5HzARWJN1WANcm0zOB+og4EBHbgW34qmkzs5IqNhQmRcTvO2ci4g3gY8WuRFJV0v9Z4NyIaEuW0waMTLqNBnbkDWtN2g5f1nxJjZIa29vbiy3BzMyKUGwonHLYvv9hFH819FDgUeBLEfGHY3Ut0HbEVdMRsSwiaiOitqLCx7rNzHpSsVc03w08LenvyP2hng0s7WqQpEHkAuHhiPhp0rxL0qiIaJM0CtidtLcCY/KGVwI7i6zPzMx6QFFbChHxA+CvgF1AO/DZiPjhscZIEvAgsDkivpX30WpgXjI9D1iV114nabCkamAcsKHYL2JmZieu2C0FIqIFaOnGsi8DrgdelLQxafsKcCfQIOkm4DVgVrL8ZkkNyToOAgsioqMb6zMzsxNUdCh0V0Q8SeHjBABXHmXMUorYLWVmZtk4rucpmJnZycmhYGZmKYeCmZmlHApmZpZyKJiZWcqhYGZmKYeCmZmlHApmZpZyKJiZWcqhYGZmKYeCmZmlHApmZpZyKJiZWcqhYGZmKYeCmZmlMgsFSQ9J2i3ppby2JZJel7QxeV2d99liSdskbZE0Lau6zMzs6LLcUlgOTC/Q/u2IqElePweQNB6oAyYkYx6QNCDD2szMrIDMQiEifgO8UWT3mUB9RByIiO3ANmBKVrWZmVlh5TimcKukTcnupXOSttHAjrw+rUnbESTNl9QoqbG9vT3rWs3M+pVSh8J3gA8BNUAbcHfSXuhZzlFoARGxLCJqI6K2oqIikyLNzPqrkoZCROyKiI6IOAR8j/d2EbUCY/K6VgI7S1mbmZmVOBQkjcqb/QzQeWbSaqBO0mBJ1cA4YEMpazMzMxiY1YIlrQSmAiMktQK3A1Ml1ZDbNfQqcDNARDRLagBagIPAgojoyKo2MzMrLLNQiIi5BZofPEb/pcDSrOoxM7Ou+YpmMzNLORTMzCzlUDAzs5RDwczMUg4FMzNLORTMzCzlUDAzs5RDwczMUg4FMzNLORTMzCzlUDAzs5RDwczMUg4FMzNLORTMzCzlUDAzs1RmoSDpIUm7Jb2U1zZM0uOStibv5+R9tljSNklbJE3Lqi4zMzu6LLcUlgPTD2tbBKyLiHHAumQeSeOBOmBCMuYBSQMyrM3MzArILBQi4jfAG4c1zwRWJNMrgGvz2usj4kBEbAe2AVOyqs3MzAor9TGFcyOiDSB5H5m0jwZ25PVrTdqOIGm+pEZJje3t7ZkWa2bW3/SWA80q0BaFOkbEsoiojYjaioqKjMsyM+tfSh0KuySNAkjedyftrcCYvH6VwM4S12Zm1u+VOhRWA/OS6XnAqrz2OkmDJVUD44ANJa7NzKzfG5jVgiWtBKYCIyS1ArcDdwINkm4CXgNmAUREs6QGoAU4CCyIiI6sajMrhde+PrHcJXTL2NteLHcJ1gtkFgoRMfcoH115lP5LgaVZ1WNmZl3rLQeazcysF3AomJlZyqFgZmYph4KZmaUcCmZmlnIomJlZKrNTUs160sV/84Nyl9Btj51R7grMus9bCmZmlnIomJlZyqFgZmYph4KZmaUcCmZmlnIomJlZyqFgZmYph4KZmaXKcvGapFeBN4EO4GBE1EoaBjwCVAGvArMj4vflqM/MrL8q55bCJyKiJiJqk/lFwLqIGAesS+bNzKyEetPuo5nAimR6BXBt+UoxM+ufyhUKAfxKUpOk+UnbuRHRBpC8jyxTbWZm/Va5boh3WUTslDQSeFzSy8UOTEJkPsDYsWOzqs/MrF8qSyhExM7kfbekx4ApwC5JoyKiTdIoYPdRxi4DlgHU1tZGqWo2s+PX1+5y2/TNG8pdQtmUfPeRpNMlndE5DVwFvASsBuYl3eYBq0pdm5lZf1eOLYVzgcckda7/xxGxVtJzQIOkm4DXgFllqM3MrF8reShExCvARQXa9wJXlroeMzN7T286JdXMzMrMoWBmZimHgpmZpRwKZmaWKtfFa2ZmvdZrX59Y7hK6bextL/bIcrylYGZmKYeCmZmlHApmZpZyKJiZWcqhYGZmKYeCmZmlHApmZpZyKJiZWcqhYGZmKYeCmZmlHApmZpbqdaEgabqkLZK2SVpU7nrMzPqTXhUKkgYA9wOfBsYDcyWNL29VZmb9R68KBWAKsC0iXomId4B6YGaZazIz6zcUEeWuISXpOmB6RHwhmb8euCQibs3rMx+Yn8x+GNhS8kJLZwSwp9xF2HHz79d3ney/3QcioqLQB73teQoq0Pa+1IqIZcCy0pRTXpIaI6K23HXY8fHv13f159+ut+0+agXG5M1XAjvLVIuZWb/T20LhOWCcpGpJpwJ1wOoy12Rm1m/0qt1HEXFQ0q3AL4EBwEMR0VzmssqpX+wmO4n59+u7+u1v16sONJuZWXn1tt1HZmZWRg4FMzNLORR6KUlfldQsaZOkjZIuKXdNVhxJ50mql/RbSS2Sfi7pwnLXZV2TVClplaStkl6RdJ+kweWuq5QcCr2QpEuBa4A/iYhJwCeBHeWtyoohScBjwPqI+FBEjAe+Apxb3sqsK8lv91Pg7yNiHDAOOA34RlkLK7FedfaRpUYBeyLiAEBEnMxXVp5sPgG8GxF/29kQERvLV451wxXA2xHxfYCI6JD0X4DfSfpqROwvb3ml4S2F3ulXwBhJ/yLpAUn/vtwFWdE+CjSVuwg7LhM47LeLiD8ArwIXlKOgcnAo9ELJ/0guJnePp3bgEUmfL2tRZic/cdhtdfLa+w2HQi8VER0RsT4ibgduBf6q3DVZUZrJBbr1Pc3A++53JOlMcseDTuYbb76PQ6EXkvRhSePymmqA35WpHOueXwODJf2nzgZJk70LsE9YB/w7STdA+nyXu4H7IuLfylpZCTkUeqehwIrkdMZN5B44tKS8JVkxIneLgM8An0pOSW0m99v5xo69XN5vd52krcBe4FBELC1vZaXl21yYmRUg6c+AlcBnI6LfnDzgUDAzs5R3H5mZWcqhYGZmKYeCmZmlHApmZpZyKFi/JakjuQNt52tRN8ZOlbTmBNe/XtJxPRxe0nJJ153I+s0K8Q3xrD/7t4ioKceKkwujzHodbymYHUbSq5L+p6RnJDVK+hNJv0wuRvvPeV3PlPRYcpHh30o6JRn/nWRcs6Q7DlvubZKeBGbltZ8iaYWk/yFpgKRvSnoueZbGzUkfJff2b5H0D8DIEv1zWD/jLQXrz06TtDFv/n9FxCPJ9I6IuFTSt4HlwGXAEHL3x+m8LfYUcleb/w5YC3wW+DvgqxHxRrI1sE7SpIjYlIx5OyL+HCAJmIHAw8BLEbFU0nxgX0RMTh7u8pSkXwEfAz4MTCR3L54W4KEe/vcwcyhYv3as3Uerk/cXgaER8SbwpqS3JZ2dfLYhIl4BkLQS+HNyoTA7+eM+kNyzMcYDnaHQGTqdvgs05N1K4SpgUt7xgrPIPezl48DKiOgAdkr69fF8YbOuePeRWWEHkvdDedOd853/mTr8dgAhqRr4r8CVyVPz/oHcFkantw4b8zTwCUmdfQT8dUTUJK/qiPjVUdZn1uMcCmbHb4qk6uRYwhzgSeBMcn/490k6F/h0F8t4EPg58BNJA4FfAl+UNAhA0oWSTgd+A9QlxxxGkXvCm1mP8+4j688OP6awNiKKPi0VeAa4k9x+/t8Aj0XEIUn/TO7YwyvAU10tJCK+Jeks4IfAfwCqgOeTZwa3A9eSe+7zFeR2Z/0L8EQ36jQrmm+IZ2ZmKe8+MjOzlEPBzMxSDgUzM0s5FMzMLOVQMDOzlEPBzMxSDgUzM0v9f9jILdn3IpVVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Embarked', data=train, hue='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "00f13b03",
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived\n",
       "Embarked          \n",
       "C         0.553571\n",
       "Q         0.389610\n",
       "S         0.336957"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Embarked', 'Survived']].groupby(['Embarked']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4b3bef6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "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(x='SibSp', data=train, hue='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b6647f56",
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "SibSp          \n",
       "0      0.345395\n",
       "1      0.535885\n",
       "2      0.464286\n",
       "3      0.250000\n",
       "4      0.166667\n",
       "5      0.000000\n",
       "8      0.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['SibSp', 'Survived']].groupby(['SibSp']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "01e33d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Parch', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "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(x='Parch', data=train, hue='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e6376f37",
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "Parch          \n",
       "0      0.343658\n",
       "1      0.550847\n",
       "2      0.500000\n",
       "3      0.600000\n",
       "4      0.000000\n",
       "5      0.200000\n",
       "6      0.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Parch', 'Survived']].groupby(['Parch']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb9cba20",
   "metadata": {},
   "source": [
    "## Training Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c883ebdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\"]\n",
    "X = pd.get_dummies(train[features])\n",
    "y = train[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "56a4745b",
   "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=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8c21e7",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9a013c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e8934aca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ks = 10\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "\n",
    "for n in range(1,Ks):\n",
    "    \n",
    "    #Train Model and Predict  \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "    yhat=neigh.predict(X_test)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
    "\n",
    "    \n",
    "    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n",
    "\n",
    "plt.plot(range(1,Ks),mean_acc,'g')\n",
    "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
    "plt.fill_between(range(1,Ks),mean_acc - 3 * std_acc,mean_acc + 3 * std_acc, alpha=0.10,color=\"green\")\n",
    "plt.legend(('Accuracy ', '+/- 1xstd','+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Neighbors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bff80ed6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.8047752808988764\n",
      "Test set Accuracy:  0.8156424581005587\n"
     ]
    }
   ],
   "source": [
    "#the best k is 2\n",
    "k = 2\n",
    "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
    "yhat = neigh.predict(X_test)\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d67b4c76",
   "metadata": {},
   "source": [
    "### Decision Trees Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1f9f29fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6c32aaef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy:  0.7988826815642458\n"
     ]
    }
   ],
   "source": [
    "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n",
    "drugTree.fit(X_train,y_train)\n",
    "predTree = drugTree.predict(X_test)\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_test, predTree))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990f5bda",
   "metadata": {},
   "source": [
    "### Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4a888c5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a67a9d43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest's Accuracy:  0.8268156424581006\n"
     ]
    }
   ],
   "source": [
    "forest = RandomForestClassifier(n_estimators=100, max_depth=3, random_state=2)\n",
    "forest.fit(X_train, y_train)\n",
    "predForest = forest.predict(X_test)\n",
    "print(\"RandomForest's Accuracy: \", metrics.accuracy_score(y_test, predForest))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc9ba4d3",
   "metadata": {},
   "source": [
    "### SVM (Support Vector Machines) Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f030d5d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e701ce4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM's Accuracy:  0.8268156424581006\n"
     ]
    }
   ],
   "source": [
    "clf = svm.SVC(kernel='rbf')\n",
    "clf.fit(X_train, y_train) \n",
    "yhat = clf.predict(X_test)\n",
    "print(\"SVM's Accuracy: \", metrics.accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2b31b69",
   "metadata": {},
   "source": [
    "### Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f59e0c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b035755b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR's Accuracy:  0.8212290502793296\n"
     ]
    }
   ],
   "source": [
    "LR = LogisticRegression(C=0.01, solver='liblinear')\n",
    "LR.fit(X_train,y_train)\n",
    "yhat = LR.predict(X_test)\n",
    "print(\"LR's Accuracy: \", metrics.accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1009cd4",
   "metadata": {},
   "source": [
    "### Models Evaluation\n",
    "A we can see the model with the best accuracy is the Random Forest in draw with SVM so we can work with one of them .\n",
    "Let's work with the Random Forest Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d9e359",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ec2e461a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_to_pred = pd.get_dummies(test[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "06627f88",
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = RandomForestClassifier(n_estimators=100, max_depth=3, random_state=2)\n",
    "forest.fit(X, y)\n",
    "predictions = forest.predict(X_test_to_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bbdc87c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame({'PassengerId': test.PassengerId, 'Survived': predictions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "37349ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "output.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85de9247",
   "metadata": {},
   "source": [
    "this work is evaluated in Kaggle with 0.777 Score"
   ]
  }
 ],
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