Titanic Survival Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "\n",
    "from sklearn import ensemble\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn import neighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "from sklearn import naive_bayes\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV, KFold, cross_val_predict\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.simplefilter('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv('train.csv')\n",
    "test=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</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",
       "      <td>0</td>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "\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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performing Data Cleaning and Analysis\n",
    "<!-- #### 1. Understanding meaning of each column: -->\n",
    "<!-- <br>Data Dictionary: -->\n",
    "<br>**Variable        Description**</br>\n",
    "1. Survived\t- Survived (1) or died (0)\n",
    "2. Pclass -\tPassenger’s class (1 = 1st, 2 = 2nd, 3 = 3rd)\n",
    "3. Name\t- Passenger’s name\n",
    "4. Sex -\tPassenger’s sex\n",
    "5. Age\t- Passenger’s age\n",
    "6. SibSp -\tNumber of siblings/spouses aboard\n",
    "7. Parch -\tNumber of parents/children aboard (Some children travelled only with a nanny, therefore parch=0 for them.)\n",
    "8. Ticket -\tTicket number\n",
    "9. Fare -\tFare\n",
    "10. Cabin -\tCabin\n",
    "11. Embarked -\tPort of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Deleting the columns not required for determining the survival of a person\n",
    "\"\"\"\n",
    "\n",
    "del train['PassengerId']\n",
    "del train['Ticket']\n",
    "del train['Fare']\n",
    "del train['Cabin']\n",
    "del train['Name']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch Embarked\n",
       "0         0       3    male  22.0      1      0        S\n",
       "1         1       1  female  38.0      1      0        C\n",
       "2         1       3  female  26.0      0      0        S\n",
       "3         1       1  female  35.0      1      0        S\n",
       "4         0       3    male  35.0      0      0        S"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 7)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived      Pclass         Age       SibSp       Parch\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch Embarked\n",
       "0          892       3    male  34.5      0      0        Q\n",
       "1          893       3  female  47.0      1      0        S\n",
       "2          894       2    male  62.0      0      0        Q\n",
       "3          895       3    male  27.0      0      0        S\n",
       "4          896       3  female  22.0      1      1        S"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Since there are ONLY 2 rows whose Embarked data is not known, \n",
    "therefore we can neglect those 2 rows as they will not make much of a difference\"\"\"\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Similarly, deleting the columns not required for determining the survival of a person for the data stored in test.csv\n",
    "\"\"\"\n",
    "\n",
    "del test['Ticket']\n",
    "del test['Fare']\n",
    "del test['Cabin']\n",
    "del test['Name']\n",
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId      Pclass         Age       SibSp       Parch\n",
       "count   418.000000  418.000000  332.000000  418.000000  418.000000\n",
       "mean   1100.500000    2.265550   30.272590    0.447368    0.392344\n",
       "std     120.810458    0.841838   14.181209    0.896760    0.981429\n",
       "min     892.000000    1.000000    0.170000    0.000000    0.000000\n",
       "25%     996.250000    1.000000   21.000000    0.000000    0.000000\n",
       "50%    1100.500000    3.000000   27.000000    0.000000    0.000000\n",
       "75%    1204.750000    3.000000   39.000000    1.000000    0.000000\n",
       "max    1309.000000    3.000000   76.000000    8.000000    9.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0\n",
       "Pclass          0\n",
       "Sex             0\n",
       "Age            86\n",
       "SibSp           0\n",
       "Parch           0\n",
       "Embarked        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Gender</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age  SibSp  Parch Embarked Gender\n",
       "0          892       3    male  34.5      0      0        Q      1\n",
       "1          893       3  female  47.0      1      0        S      2\n",
       "2          894       2    male  62.0      0      0        Q      1\n",
       "3          895       3    male  27.0      0      0        S      1\n",
       "4          896       3  female  22.0      1      1        S      2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def getNum(str):\n",
    "    if str=='male':\n",
    "        return '1'\n",
    "    if str=='female':\n",
    "        return '2'\n",
    "train[\"Gender\"]=train[\"Sex\"].apply(getNum)\n",
    "#We have created a new column called \"Gender\" and \n",
    "#filling it with values 1 ,2 based on the values of sex column\n",
    "train.head()\n",
    "\n",
    "test[\"Gender\"]=test[\"Sex\"].apply(getNum)\n",
    "#We have created a new column called \"Gender\" and \n",
    "#filling it with values 1 ,2 based on the values of sex column\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "del train['Sex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "del test['Sex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch Embarked Gender\n",
       "0         0       3  22.0      1      0        S      1\n",
       "1         1       1  38.0      1      0        C      2\n",
       "2         1       3  26.0      0      0        S      2\n",
       "3         1       1  35.0      1      0        S      2\n",
       "4         0       3  35.0      0      0        S      1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <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",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass   Age  SibSp  Parch Embarked Gender\n",
       "0          892       3  34.5      0      0        Q      1\n",
       "1          893       3  47.0      1      0        S      2\n",
       "2          894       2  62.0      0      0        Q      1\n",
       "3          895       3  27.0      0      0        S      1\n",
       "4          896       3  22.0      1      1        S      2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Pclass          \n",
       "3       0.242363\n",
       "2       0.472826\n",
       "1       0.629630"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Pclass','Survived']].groupby(['Pclass']).mean().sort_values(by='Survived',ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Gender          \n",
       "1       0.188908\n",
       "2       0.742038"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Gender','Survived']].groupby(['Gender']).mean().sort_values(by='Survived',ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "SibSp          \n",
       "5      0.000000\n",
       "8      0.000000\n",
       "4      0.166667\n",
       "3      0.250000\n",
       "0      0.345395\n",
       "2      0.464286\n",
       "1      0.535885"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['SibSp','Survived']].groupby(['SibSp']).mean().sort_values(by='Survived', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "Parch          \n",
       "4      0.000000\n",
       "6      0.000000\n",
       "5      0.200000\n",
       "0      0.343658\n",
       "2      0.500000\n",
       "1      0.550847\n",
       "3      0.600000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Parch','Survived']].groupby(['Parch']).mean().sort_values(by='Survived', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing Data by Visualization\n",
    "\n",
    "Inorder to understand who would have had a better probability of survival, we should visualize the patients who survived based on age, passenger class and etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cdb0dd668>"
      ]
     },
     "execution_count": 22,
     "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['Survived'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x22cdb08e668>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_hist = sns.FacetGrid(train, col='Survived')\n",
    "age_hist.map(plt.hist, 'Age')\n",
    "age_hist.set_ylabels('Number')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among those who didn't survive, most people were of the age 20-30 years whereas among those who survived, \n",
    "the number of infants are much higher than teenagers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of people who survived over age and passenger class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x22cdd2bf860>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468.8x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pclass_age_grid = sns.FacetGrid(train, col='Survived', row='Pclass', height=2.0, aspect=1.6)\n",
    "pclass_age_grid.map(plt.hist, 'Age', alpha=0.5, bins=20)\n",
    "pclass_age_grid.add_legend()\n",
    "pclass_age_grid.set_ylabels('Number')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling the NULL values in the column Age\n",
    "\n",
    "#### We have NULL value inplace of Age for some of the people in both training and testing data.\n",
    "#### So, one way is to fill them with the mean values i.e. fill the ones who have survived with the mean age of the survived people and similarly fill those who haven't survived with the mean age of all non-survived people.\n",
    "\n",
    "#### But, this will only solve the problem for the training data and not for the testing data as we have to predict their survival status.\n",
    "\n",
    "#### So, we can tackle the issue by creating an array that contains random numbers, which are computed based on the mean age value in regards to the standard deviation and is_null."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.343689655172415"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding mean survived age\n",
    "mean_sur= train[train.Survived==1]['Age'].mean()\n",
    "mean_sur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.62617924528302"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the mean age of \"Not Survived\" people\n",
    "mean_nsur=train[train['Survived']==0]['Age'].mean()\n",
    "mean_nsur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [train, test]\n",
    "\n",
    "for dataset in data:\n",
    "    mean = train[\"Age\"].mean()\n",
    "    std = test[\"Age\"].std()\n",
    "    is_null = dataset[\"Age\"].isnull().sum()\n",
    "    \n",
    "    # compute random numbers between the mean, std and is_null\n",
    "    rand_age = np.random.randint(mean - std, mean + std, size = is_null)\n",
    "    \n",
    "    # fill NaN values in Age column with random values generated\n",
    "    age_slice = dataset[\"Age\"].copy()\n",
    "    age_slice[np.isnan(age_slice)] = rand_age\n",
    "    dataset[\"Age\"] = age_slice\n",
    "    dataset[\"Age\"] = train[\"Age\"].astype(int)\n",
    "    \n",
    "    \n",
    "train[\"Age\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Age  SibSp  Parch Embarked Sex\n",
       "0         0       3   22      1      0        S   1\n",
       "1         1       1   38      1      0        C   2\n",
       "2         1       3   26      0      0        S   2\n",
       "3         1       1   35      1      0        S   2\n",
       "4         0       3   35      0      0        S   1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Renaming \"gender\" column\n",
    "train.rename(columns={'Gender':'Sex'}, inplace=True)\n",
    "\n",
    "test.rename(columns={'Gender':'Sex'}, inplace=True)\n",
    "\n",
    "#Removing the 2 rows having null value for Embarked column\n",
    "train.dropna(inplace=True)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 7 columns):\n",
      "Survived    889 non-null int64\n",
      "Pclass      889 non-null int64\n",
      "Age         889 non-null int32\n",
      "SibSp       889 non-null int64\n",
      "Parch       889 non-null int64\n",
      "Embarked    889 non-null object\n",
      "Sex         889 non-null object\n",
      "dtypes: int32(1), int64(4), object(2)\n",
      "memory usage: 52.1+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combining 'Parch' & 'SibSp' as 'Realtives'\n",
    "train['Family_Members']=train['Parch']+train['SibSp'] + 1\n",
    "test['Family_Members']=test['Parch']+test['SibSp'] + 1\n",
    "\n",
    "del train['SibSp']\n",
    "del train['Parch']\n",
    "\n",
    "del test['SibSp']\n",
    "del test['Parch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "      <td>38</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>35</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass  Age Embarked Sex  Family_Members\n",
       "0         0       3   22        S   1               2\n",
       "1         1       1   38        C   2               2\n",
       "2         1       3   26        S   2               1\n",
       "3         1       1   35        S   2               2\n",
       "4         0       3   35        S   1               1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>35</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>35</td>\n",
       "      <td>S</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   PassengerId  Pclass  Age Embarked Sex  Family_Members\n",
       "0          892       3   22        Q   1               1\n",
       "1          893       3   38        S   2               2\n",
       "2          894       2   26        Q   1               1\n",
       "3          895       3   35        S   1               1\n",
       "4          896       3   35        S   2               3"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping the Age data\n",
    "\n",
    "#### The age groups need to be converted into different sub-groups so that better prediction model can be formed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    166\n",
       "2    155\n",
       "1    153\n",
       "3    136\n",
       "5    115\n",
       "6    100\n",
       "0     64\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=[train,test]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset.loc[ dataset['Age'] <= 10, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 10) & (dataset['Age'] <= 20), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 20) & (dataset['Age'] <= 25), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 25) & (dataset['Age'] <= 30), 'Age'] = 3\n",
    "    dataset.loc[(dataset['Age'] > 30) & (dataset['Age'] <= 37), 'Age'] = 4\n",
    "    dataset.loc[(dataset['Age'] > 37) & (dataset['Age'] <= 45), 'Age'] = 5\n",
    "    dataset.loc[ dataset['Age'] > 45 , 'Age'] = 6\n",
    "\n",
    "train['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>S</td>\n",
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       "      <td>S</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Age Embarked Sex  Family_Members\n",
       "0         0       3    2        S   1               2\n",
       "1         1       1    5        C   2               2\n",
       "2         1       3    3        S   2               1\n",
       "3         1       1    4        S   2               2\n",
       "4         0       3    4        S   1               1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "   PassengerId  Pclass  Age Embarked Sex  Family_Members\n",
       "0          892       3    2        Q   1               1\n",
       "1          893       3    5        S   2               2\n",
       "2          894       2    3        Q   1               1\n",
       "3          895       3    4        S   1               1\n",
       "4          896       3    4        S   2               3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Age  Embarked Sex  Family_Members\n",
       "0         0       3    2         1   1               2\n",
       "1         1       1    5         0   2               2\n",
       "2         1       3    3         1   2               1\n",
       "3         1       1    4         1   2               2\n",
       "4         0       3    4         1   1               1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=[train,test]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset['Embarked']=dataset['Embarked'].map({'C': 0, 'S': 1, 'Q': 2}).astype(int)\n",
    "    \n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     Survived\n",
       "Age          \n",
       "2    0.335484\n",
       "5    0.356522\n",
       "3    0.360294\n",
       "1    0.366013\n",
       "6    0.370000\n",
       "4    0.403614\n",
       "0    0.593750"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Age','Survived']].groupby(['Age']).mean().sort_values(by='Survived', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived\n",
       "Embarked          \n",
       "1         0.336957\n",
       "2         0.389610\n",
       "0         0.553571"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Embarked','Survived']].groupby(['Embarked']).mean().sort_values(by='Survived', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.626168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Pclass          \n",
       "3       0.242363\n",
       "2       0.472826\n",
       "1       0.626168"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Pclass','Survived']].groupby(['Pclass']).mean().sort_values(by='Survived', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cdd5a7160>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1SXrhe8Ku/y1jbbuH+KnjULa+PYtaI/oB4Dp6nC0vfsrfT08NReSnkAjhqpG3MvmWF3m10xAaXXkxVepWz5CmRZ92pOw7xEttB7NscjzdHr0egIjICK4bcw//e3wyo+OGMr7vM6QdP/k0XsPOzTl6+Eie5idLBWDgo3xVOHfv3pmpH/0fACt/+JFy5csRE1PllHQHDhwEoEiRIkRFRWX493/1lRE8+tgoAjDcatA1a3Q+5cqWCXUYfrv8ijg+/uh/AKxatYZy5coQE1P5lHQZz1vRsDpHZRrX5chfSRz9Zyd6PJVdM5cT3bl5hjRpB1PSX0eULJb+2nX4KAd+WI/r6LE8i/d0zmhUl/+2JLH7352kHU/jpznfc15cswxpGsQ1JWHGUgB+jl9J3YudX6n1L72A7ev/Yfvv/wBweO9B1N2+G1WyGJfe3o2Fb36eh7nJRmqa90uY8mUOwToiUsz9uq2I3Cci5YMX2qmqV4sh8d+TM8RsTdxO9WpZT2MYP/cjtm/9iQMHDjJjxlwArriiE1u3bmfdOpvAJS9Vq1aVxMST523b1iRiszlvs2ZN4e8tqzl44BCffx6fZZpQiIqN5ti2/9LXj21Pplhs9CnpYm7tQpMVb1HzyZvY/PjkvAzRa+WqVmDftuT09X3bkylbtUKmNNHpaVxpLo4cOEzJCmWoVDsWVaX/lEe5f+5ztBnYPf2Yzg/1YemkLzh+5GjeZOR01OX9EqZ8qTnPANLcQ4VOBmoBH2eX2HPSRJfrkJ9hpr/nKduyq111u+IGapzZhGLFomjfrjUlShTnsUfvY8TTrwQkFuM9X85bjx43U6d2C6KKRdG27cXBDs17XuYh6b15/NjqXv5+9kPOGNw7LyLzXRZ5OWXK0izTKBGREdRqfjaf3P827/QeQcPOzah78XnENjiLimdV5df5CUEJ2Wcu9X4JU74Uzi73lC1XAa+p6mCcYUSzpKoTVLWZqjaLiCiV6wDvurMfCasWkLBqAdu2J1HjjGrp+6rXiGXb9h3ZHnv06FHmzP2K7t07U6dOTWrWPJMfE75i08YV1KgRy6qV86la9dSf18Z/AwbexPcr4vl+RTzbt++gRo2T561a9RiScjhv8V98zeVXdMqLUL1ybFsyUdVO3hSLiq3IsaQ92ab/b+Zyors0z3Z/KO1L2k25ahXT18vFVmT/zj2Z0iSnp4mIjKB4mZIc3nuQfUm72bzydw7vOcDxI8dYv3gt1RvW4qwm9ahxfm0e/fYN7po+gkq1Yhn46ZN5mi9P6nJ5vYQrXwrn4yJyHdAPmOveVvQ06QNi7LgPaNY8jmbN45g9ez433eDURlq2aML+fftJSso4wW2pUiXT26EjIyPp2qU9GzZs4pdf1lOtxoXUrd+KuvVbkZi4neYtO7Njh9/zMJosTBg/lYtadeOiVt2YM2cB19/QC4DmzRuzf/8BkpIy/rs75835ooyMjCSuczs2bvwzz+POzoG1myhRO5ZiZ1ZBihahcs/W7F6wKkOa4rVONtVU6NiEI38l5XWYXkn86U8q1YyhQo3KRBaN5MLuF/HbV6szpPntq9U0u/oyAM7v1pJN3/0KwMZv1hF7zpkULR5FRGQEtVuey44/trLiw695tuXdvHDJfYy9ZgT//bWd8X2fyfO8pSsANWdf+jnfCtwJjFLVv0SkFvBhcMLKWvyXC+nSpT0bfl/O4ZQUbr/9wfR9CasW0Kx5HKVKleTz/71HsWJRREZGsnjxcsZPCI+75L4aOvwFVq1Zx969++nQ80bu7n8TV3fvHOqwfDZ/3mI6d27Hz798Q8rhFAbeOTR93/cr4rmoVTdKlSrJZ9MnUSwqiojISL755jsmTfwohFFnkuZi82OTOO+TJyAygp2fLCJlQyJnPnwtB9f+ye4FCcTe1pXyl12A63gqafsOsfG+N9MPb7rqHSJLlyAiqgjRXVrwa99nSNkYmh5DrjQXs556n9unDCMiMoJVny1hxx+JxA3uTeLPf/Hb16tZ9dkS+o6+m4eXjOHw3oN8PMjJS8r+QyydFM+g2aNAlfWL17J+8ZqQ5OO0wrgt2VuSmzviIlIBOENV13mTvkhU9fD9esqllG3LQh1CUJQ/s32oQwi4BWWb5ZwoH5pVPOg/XEPipb8/yaLB2zeHRlzndZlTaoT/nxcMvvTWWCIiZUUkGvgJeE9ERgcvNGOMyaUC0KzhS5tzOVXdD/QC3lPVpkDH4IRljDF+KGRd6YqISCzQh5M3BI0xJvwEsOYsIl1EZIOIbBKRR7NJ00dEfhORX0Uk2y7GvvDlhuBIYD7wraquEpHawB+BCMIYYwIpUF3kRCQSeBvoBCQCq0Rktqr+5pGmHjAMaK2qe0Tk1MeWc8HrwllVpwPTPdY3A1cHIghjjAmo1IA1V7QANrnLO0TkU6AH4PmY8R3A26q6B0BVd57yLrngdeEsIsWB/sB5QPET21X1tkAEYowxARO4tuTqwL8e64lAy0xp6gOIyHIgEhihqvP8/WBf2pynAjFAZ+AboAZwwN8AjDEm4Hxoc/YcasK9DPB4p6y62WVuqC4C1APaAtcBkwIx7pAvbc51VfUaEemhqh+4G73n+xuAMcYEmvrQRU5VJwATstmdCJzhsV4D2JZFmhWqehz4S0Q24BTWq/CDT49vu/+/V0QaAuWAmv58uDHGBEXgemusAuqJSC0RiQL6ArMzpZkJtAMQkUo4zRyb/c2CLzXnCe4nA590B1caeMrfAIwxJuAC1FtDVVNF5F6cVoJI4F1V/VVERgIJqjrbvS9ORH4D0oChqpqc/bt6x5feGpPcL78Bavv7wcYYEzSB662BqsYD8Zm2PeXxWoEH3UvA5Fg4i8hpP1BV7RFuY0xYCadZdHLLm5pz/p8nyRhTuITxmBneyrFwVtWn8yIQY4wJmAJQOPsyKt0Hnn33RKSCiLwbnLCMMSb31KVeL+HKl94aF6jq3hMr7mfIGwchJmOM8U8YF7re8qVwjhCRCieeH3eP6+zL8cYYkyc0tXAVzq8C34vIdJzHF/sAo4ISlTHG+KMw1ZxVdYqIJADtcZ437+U5bJ4xxoSN8B1D32ve9HMujjOxa13gZ2CcqqYGOzBjjMmtcL7R5y1vas4f4IyrsQzoCpwLPBDMoIwxxi+FoeYMNFDV8wFEZDLwQ3BDMsYY/xSWG4InRqM7MQiIzx/SM7apz8eEu/Jntg91CEGx959FoQ4h4G5uGtAhD8JGwqG/Qh1CULwUgPcI43lbveZN4XyhiOx3vxaghHtdcMb8KBu06IwxJjcKQ+GsqpF5EYgxxgRKYak5G2NM/mKFszHGhB+rORtjTBhyFYAnMaxwNsYUPOp7r7JwY4WzMabAsWYNY4wJQ+qymrMxxoQdqzkbY0wYcqVZzdkYY8KONWsYY0wY0vw/7pH3E7waY0x+oS7xesmJiHQRkQ0isklEHj1Nut4ioiLSLBB5sJqzMabACVSzhohEAm8DnYBEYJWIzM48C5SIlAHuA1YG5IOxmrMxpgBS9X7JQQtgk6puVtVjwKdAjyzSPYMz2umRQOXBCmdjTIHjSovweslBdeBfj/VE97Z0ItIYOENV5wYyD9asYYwpcHzp5ywiA4ABHpsmqOqEE7uzenuPYyOAMcAtPgeZAyucjTEFjsuHsTXcBfGEbHYnAmd4rNcAtnmslwEaAkvcs0TFALNF5EpVTfAl5syscDbGFDgauIGPVgH1RKQWsBXoC1x/8nN0H1DpxLqILAGG+FswgxXOxpgCKFC9Ndzzpt4LzAcigXdV9VcRGQkkqOrsgHxQFqxwNsYUOIF8CEVV44H4TNueyiZt20B9rhXOxpgCJy3nXhhhL+xzcNuIO3jzm/G8Ou8NajWsnWWa2g3r8Or8N3jzm/HcNuKOU/ZfOaAn/7dlNmUqlMmwvc4FdZm2+XNadbs4KLHnxsuvDGfdz0tYufJLGjU6L8s0M2d9wIoVX7IqYQGvvzGKiIiwP40ZPPHcaC67vC89b7wz1KF4pd+I2xnzzVhenPcaNbO5Bms1rMOL819nzDdj6Tfi9vTt1zx0PS/Oe43n48cwbOoIKlSpAEC1OtV5+vMXmLJxOpcPyKrbbN568rmhfP3DTOYs+ZQGF5xzyv7iJYoz8ePXmffdDOKXfcaQJwel72t+UWNmLvyI37evpEv3DnkZdrZUxeslXIX1X3Xjdk2JrVWNQW0GMm7Y2wx49q4s090x6i7GD3ubQW0GElurGo3bNknfVzG2Ehdc0ohdiTszHBMREcGNw27hp6VrgpoHX3Tu3Ja6dWtxwfltuffex3jt9VFZprvpxnto1aorzZvFUalSNL16XZ7HkfqnZ7dOjBv9bKjD8Eqjdk2JqRXL4DZ3MXHYO/R/NusvlNtGDWTSsHcY3OYuYmrFcqH7Gpw7/nMe6fIAw7oN5seFq+h1/7UAHNx7kA+GT2LuxJl5lpfstOnYmrNqn0HHFj158qFnGfnSsCzTTXp7Kl0uvpoe7a+nSYsLuayDU6nZlpjEI4OGM2fGvLwM+7QC+BBKyIR14dy8U0uWzFgMwB9rNlCybCnKu2seJ5SvUoGSpUuy8ccNACyZsZjmca3S99/yVH+mPv8+muksdL3lClZ++R37/tsX5Fx47/Ir4vj4o/8BsGrVGsqVK0NMTOVT0h04cBCAIkWKEBVV9JS8hbtmjc6nXNkyOScMA007tWDZjCUAbFqzMdtrsETpkvzhvgaXzVhCs7iWAKQcTElPV7xk8fRztT95H5vXbSLteFoe5OL0OnZpw8xpXwCwdvUvlClXmspVK2VIcyTlCCuXOx0Qjh9P5bd164mJrQrA1n+3s+G3TWF1HbpUvF7ClVeFs4hUFZHJIvKle72BiPQPbmhQMaYiydt2pa/vTkqmYtWKGdNUrUhy0n8n02z/j4oxTppmHVuwOymZLb//neGY6KrRtOjcigUfhs83PUC1alVJTDzZhXLb1iRiq8VkmXbWrCn8vWU1Bw8c4vPP47NMY/wXHRNN8jaP6yspmeiq0RnTVI1md1Jy+nry9mSiY06m6TP0Bt76fhKte17G9NGfBD9oH1WNrcL2bTvS15O27aRqFpWCE8qULU37uEv5ftkPeRFerhSmZo33cbqSVHOvbwQeCEZAGWTx73bKt3M2aaKKR3H1vdcwbfTHp+y/dfgdfPjCB7hc4TVdgrsTewbZ1UZ69LiZOrVbEFUsirZtw6fNvKDJ+pyckui0aT57+SPuveh2ls9cSud+3QIcof98ue4iIyMZM+E5pkz6lH+3bA12aLmW5hKvl3DlbW+NSqr6mYgMg/S+f6f9Peb5SGTj6AuoXfosrz6oy83d6NA3DoA/1/1BxWqVgd8BiI6pyO6duzOkT05KpmLMyZ9g0bGV2L1jNzFnxVLljKq88uXrgNP2/NIXrzGsx0PUvqAug98cAkCZ6LI0adeUtNQ0Vi0I2IBSXhsw8CZuvfU6AFav/okaNaql76tWPYak7TuyO5SjR48S/8XXXH5FJxYt+jbosRYWnW7uSnv3Nbh53R9UrOZxfcVUZE+ma3B3UjLRMSd/0VWMrcieHRnTACyftZSH33uC/xvzaZAi994Nt13DtTddBcC6Nb8RW61q+r6YalXYueO/LI97dvTjbNn8L++PD79fAJ7CuUbsLW8L50MiUhH3M+Ui0go4bWOt5yORvc+60uvGqHlT4pk3xfmZ3qR9M7r2u5zls5dSr/HZHD5wmL0792RIv3fnHlIOpVCv8dn8sWYDba9uR/z7c/lnwxb6N705Pd07307kke4PcmDPAe655GSPjnteuZ/Vi1aFpGAGmDB+KhPGTwWgc5d23HlnP6ZPn03z5o3Zv/8ASUm7MqQvVaokZcqUIilpF5GRkcR1bsd334Xvz8v86KspX/LVlC8BaNy+KXH9uvHd7GXUbVyfwwcOZXkNHjmUQt3G9dm0ZiOXXt2W+e8713BMzViS/t4OOO3X2/4Mj9rmR+9O56N3pwPQttMl3Ni/D3M/n0+jpg05sP8gu7IonAcPu4syZUvz2APP5HW4PgvntmRveVs4PwjMBuqIyHKgMtA7aFG5/bgogSbtmvLW0vEcTTnKO0PeSN/3cvxrDO3mtKxMfHws97x6P1HFo1iz5EfWLF4d7HzmDPkAACAASURBVNCCYv68xXTu3I6ff/mGlMMpDLxzaPq+71fEc1GrbpQqVZLPpk+iWFQUEZGRfPPNd0ya+FEIo/bd0OEvsGrNOvbu3U+Hnjdyd/+buLp751CHlaU1i1bTqF1TXls6jqMpRxnvcQ0+Hz+GYd0GA/Du4+O489X7iCpejLVLVrPWfQ32ffRmqtWuhrqUXVt3MfmxsQCUq1yeUXNeoUTpkqhL6Xpbd4Z2HJThBmJeWfLVt7Tp2JqFP8wiJeUIj943In3f7MUfc2W764mJrcLdD97Onxv/YtYi53qbOvkzpn84k/MbNeCdD16hbLmytIu7lPseHki3S/vkeT48hc+tydwTb++wikgR4GycVt4Nqnrc2w/xpeacX3y5a12oQwiKvf8sCnUIAXdz0wdDHUJQJBz+N+dE+dAfu1b7Xe39LvZqr8uci7fPCMtqtlc1ZxHplWlTfRHZB/ysqjuzOsYYY0KlMLU59wcuAha719sCK3AK6ZGqOjUIsRljTK6kZTkMc/7ibeHsAs5V1R3g9HsGxgItgaWAFc7GmLDhKgANqd4WzjVPFMxuO4H6qrpbRLxuezbGmLzgKkQ152UiMheY7l6/GlgqIqWAvUGJzBhjckkLUeF8D9ALuMS9/gMQq6qHgHbBCMwYY3IrvJ79zR2vHt9Wp7/dn8Bx4CqgAyce2zPGmDCjiNdLuDptzVlE6uPMmXUdkAxMw+kbbbVlY0zYSg11AAGQU7PGemAZ0F1VNwGIyOCgR2WMMX4I5xqxt3Jq1rgaSAIWi8hEEelAluPAGWNM+HCJ90u4Om3hrKqfq+q1wDnAEmAwUFVExopIXB7EZ4wxPnMhXi/hytsbgodU9SNVvQKoAawFHg1qZMYYk0vqwxKufJ59W1V3A+PdizHGhJ3ULCYQyG/Ceg5BY4zJjUDWnEWki4hsEJFNInJKi4GIPCgiv4nIOhFZKCLezSySAyucjTEFjsuH5XREJBJ4G+gKNACuE5EGmZKtAZqp6gXA/wEvBSIPVjgbYwqcAPbWaAFsUtXNqnoM+BTo4ZlAVRer6mH36gqc+3J+87nN2Rhjwl0Ae2FUBzxnNUjEGY0zO/2BLwPxwVY4G2MKHF96YXhORu02wT0HKmT9XEeWby8iNwLNgDY+fHy2rHA2xhQ4qT5UnD0no85CInCGx3oNYFvmRCLSEXgcaKOqR73/9OxZm7MxpsAJYG+NVUA9EaklIlE4Yw3N9kwgIo1xuhZfGchp+6zmbIwpcAL1WLaqporIvcB8IBJ4V1V/FZGRQIKqzgZeBkoD08XpX/2Pql7p72db4WyMKXACOZ6zqsYD8Zm2PeXxumMAPy6dFc7GmAKnIAy2nyeF847UQ3nxMXlqQdlmoQ4hKG5u+mCoQwi4KatHhzqEoBjY7OFQhxC2NP8/vW01Z2NMwVMYBts3xph8J5xHm/OWFc7GmAInnAfR95YVzsaYAsduCBpjTBiywtkYY8JQmjVrGGNM+LGaszHGhCHrrWGMMWHIVQCKZyucjTEFjjVrGGNMGMr/9WYrnI0xBZAvg+2HKyucjTEFjrU5G2NMGMr/RbMVzsaYAshuCBpjTBiyZg1jjAlDaaEOIACscDbGFDhWczbGmDCU/4tmK5yNMQWQ3RA0xpgwpAWg7pyvCuf7R95Dq/YtOZpylOcGv8TGX/44Jc0dj9xG596dKFOuDJ3rX5G+vcdNV3BVvx64XC5SDqXw8sNj+PuPLXkZfpbKt2tE7WduhcgIdny0kK1vzcywP+bmOGJu7YymuXAdOsKmoeNJ2ZhIkQqlOWfSEEo3qsPOaUvY/NjkEOUgo34jbqdRu6YcSznK2CFv8Pcvm09JU6thHe589T6iikexdvFqPhgxCYBrHrqeZp1a4HIp+5P3Me6h19mzcw/V6lRn4CuDqHVeHaa98iFfTJiV19nyyhPPjWbp8h+IrlCemR+OC3U4p2jYphHXP3UrEhnBsmkLiR+b8VorElWE20cP4qyGtTm09yBj7x1NcuIuALrdfRWX9mmPprn46Ol3+XXpTxQpVpRHp42kaLGiRERGkvDl98wa81mG97x+xG1cck077j7vpjzLJxSMmnNEqAPwVqv2LahRqwbXXXIzLz0ymoeevz/LdMu/+p6Bl99zyvavPl/ELR3v4La4gXz8zjTuHX5nsEPOWUQEtZ+/nV+vH8WaywZT+apLKFG/RoYku/63jLXtHuKnjkPZ+vYsao3oB4Dr6HG2vPgpfz89NRSRZ6lRu6bE1IplcJu7mDjsHfo/m/W/8W2jBjJp2DsMbnMXMbViubBtEwDmjv+cR7o8wLBug/lx4Sp63X8tAAf3HuSD4ZOYO3Fmlu8XLnp268S40c+GOowsSUQEN468nTG3jOKJToNpeeUlVKub8Vq7tE8HDu07xLC2g1gweS7XPHojANXq1qBl99Y8GTeY0f1GcdMzdyAREaQePc7L1z/N8K5DGNFtCOe3aUztxvXS36/m+XUoWbZUnubzhDTU6yUnItJFRDaIyCYReTSL/cVEZJp7/0oRqRmIPOSbwvmSzq2Z938LAPjtx98pXa40FatEn5Lutx9/J3nn7lO2Hz54OP118ZLF0TD41VOmcV2O/JXE0X92osdT2TVzOdGdm2dIk3YwJf11RMli6a9dh49y4If1uI4ey7N4c9K0UwuWzVgCwKY1GylZthTlq1TIkKZ8lQqUKF2SP37cAMCyGUtoFtcSgBSPvDrnyDlJ+5P3sXndJtKOh3cHqWaNzqdc2TKhDiNLtRvVZeeWJHb9u5O046msnLOcRnEZr7XGcc35zn3+EuK/59yLzwegUVxzVs5ZTuqxVP5L3MnOLUnUblQXgKOHjwAQWSSSyCKR6XfiJCKCax67ienPh6by4EK9Xk5HRCKBt4GuQAPgOhFpkClZf2CPqtYFxgAvBiIP+aZZo3JMJXZu25W+vmv7LirFVMqyIM7OVf16cO2A3hSJKsIDfYYEI0yfRMVGc2zbf+nrx7YnU6ZJvVPSxdzahWoDryCiaBF+6T0iDyP0TXRMNMke+dmdlEx01Wj27txzMk3VaHYnJaevJ29PJjrm5Jdsn6E3cFmvdhw+cIhn+j6ZN4EXAuWrRrPb49zs2Z5M7Ub1sk3jSnORcuAwpSuUoULVaP5cszHDseWrOudMIiIYPvdFqpwVw6Kp89m81mlq7NCvC2u/TmDfrr3BzlqWAtis0QLYpKqbAUTkU6AH8JtHmh7ACPfr/wPeEhFR9a8KmGPNWUTmiMjs7BZ/PtwXksUoU77m/fMPZtG39U2MGzWRm++/MUCR+SGLTGWVp6T35vFjq3v5+9kPOWNw77yILFcky/yckui0aT57+SPuveh2ls9cSud+3QIcYeGV9blRr9Jktf3ESVOXixHdhvLQRQOpdWFdqtc/g/JVKtC820UsfD8+MMHngvrwXw6qA/96rCe6t2WZRlVTgX1ARX/z4E2zxivAq8BfQAow0b0cBH7J7iARGSAiCSKSkHRoa66Cu6pfD95dMJ53F4znv6RkqlSrnL6vcmxlknckn+bo7C2ctZhLO1+cq2MD6di2ZKKqVUpfj4qtyLGkPdmm/2/mcqK7NM92fyh0urkrz8eP4fn4MezZsZuKHvmJjqnInky/bHYnJRMdc/K6rRhbkT07Tv31s3zWUlp0vSh4gRcye5KSifY4NxViK2b4RZM5TURkBCXKlOTQ3oPOOcvh2JT9h9mw4lcatmnMmefVokrNGF745i1e+vYdokoU4/klbwYxd6dy+bB4llXuZYDHW2U1+OgpVQ4v0vgsx8JZVb9R1W+Axqp6rarOcS/XA5ec5rgJqtpMVZvFlMr8ReOdzz+YxW1xA7ktbiDL5i+nS+84ABo0OZeD+w/51KRRo9bJGC7q2IrEv3L3hRFIB9ZuokTtWIqdWQUpWoTKPVuze8GqDGmK14pJf12hYxOO/JWU12Ge1ldTvmRYt8EM6zaYhAUrufTqtgDUbVyfwwcOnfJHvHfnHo4cSqFu4/oAXHp1W1Z/9QMAMTVj09M17dSCbX+G/hwVFH/9tImqNWOpVKMKkUWL0LJ7a9Z+lfFaW/tVAhe7z1+zbhex/rtf3NtX0bJ7a4pEFaFSjSpUrRnL5rWbKBNdlhJlSwJQtFgUDVpfQNKfW1m3+EcGN7+Dhy+5m4cvuZtjKUcZ1nZQnubXl5qzZ1nlXiZ4vFUicIbHeg1gW6aPS08jIkWAcoD3hVM2fGlzriwitT3aXmoBlXM4JmC+X7iSVu1b8unyqRxJOcLzD76cvu/dBeO5LW4gAHc9PoCOV7WneIlizEj4lLkfx/Pe6Cn0uqUnzS5tQmpqKgf2HWTUAwFps/dPmovNj03ivE+egMgIdn6yiJQNiZz58LUcXPsnuxckEHtbV8pfdgGu46mk7TvExvtO1kCarnqHyNIliIgqQnSXFvza9xlSNiaGLDtrFq2mUbumvLZ0HEdTjjJ+yBvp+56PH8OwboMBePfxce6udMVYu2Q1axevBqDvozdTrXY11KXs2rqLyY+NBaBc5fKMmvMKJUqXRF1K19u6M7TjoAw3EMPB0OEvsGrNOvbu3U+Hnjdyd/+buLp751CHBThtyB8+NYkHpzxBRGQE3362iG1/JNJz8LX8/fOfrP06gaWfLeSO0ffx/JI3ObT3IOMHjQFg2x+JrJr7Hc9+9Rqu1DQ+fGoS6nJRrkoF+r96LxEREUiEsOqL7/hp0eoQ59SRGrg7/quAeu7ybivQF7g+U5rZQD/ge6A3sMjf9mYA8fY9RKQLMAE40XG1JjBQVefndOyl1TuEQd+IwHohrULOifKht4qlhjqEgJuyenSoQwiKgc0eDnUIQfHu3//n9zwmN57Vy+sy58Mt/zvt54lIN+A1IBJ4V1VHichIIEFVZ4tIcWAq0Binxtz3RCXWH17XnFV1nojUA85xb1qvqkf9DcAYYwItkAMfqWo8EJ9p21Mer48A1wTsA9287ucsIiWBocC9qvoTcKaIXJHDYcYYk+cC2FsjZHx5COU94Bhw4hZ6IhCej0MZYwo1X3prhCtfCuc6qvoScBxAVVPIuguJMcaEVBour5dw5UtvjWMiUgJ3/z0RqQNYm7MxJuyEb5HrPV8K5+HAPOAMEfkIaA3cEoygjDHGHwHoyRZyvvTW+EpEfgRa4TRn3K+q/+VwmDHG5LmCME2VL701Rqpqsqp+oapzgd3uGrQxxoSVwnZD8EwRGQbO+KXATODU0e6NMSbECkJXOl/anG8FPnIX0O2AL1V1THDCMsaY3EvTcK4TeyfHwllEmnisvg6MB5YD34hIE1X9MVjBGWNMbuT/otm7mvOrmdb34MwI8CpOt7r2gQ7KGGP8Ec7NFd7KsXBW1XYiEgFco6rT8iAmY4zxS6HpraGqLuDUWVONMSYMqarXS7jy5YbgVyIyBJgGHDqxUVX9HlTaGGMCqSDUnH0pnG9z/9+zBq1A7cCFY4wx/isUvTVOUNVawQzEGGMCJf/Xm32rOSMiDXF6ahQ/sU1VpwQ6KGOM8UehatYQkeFAW5zCOR7oCnwLWOFsjAkrBaFw9uXx7d5AByBJVW8FLgSKBSUqY4zxQ2HrrZGiqi4RSRWRssBO7GagMSYMhfMg+t7ypXBOEJHywERgNXAQ+CEoURljjB/CuUbsLV96a9ztfjlOROYBZVV1XXDCMsaY3CsIbc6+9tboBVyC01PlW8CrwrlB0WjfIwtzs4r69E+XbyQc+ivUIQTcwGYPhzqEoBif8FKoQwhbharmLCLvAHWBT9ybBopIR1W1x7qNMWGlsNWc2wAN1f2VJCIfAD8HJSpjjPFDXo1KJyLROENa1AT+Bvqo6p5MaRoBY4GyQBowyptB5HzpSrcBONNj/Qy8bNYwxpi8lKYurxc/PQosVNV6wEL3emaHgZtV9TygC/Cau3PFaXkz2P4cnDbmcsDvIvKDe70l8J3XWTDGmDziyrs25x44D+cBfAAsAR7xTKCqGz1ebxORnUBlYO/p3tibZo1XfAjUGGNCzpdmDREZAAzw2DRBVSd4eXhVVd0OoKrbRaRKDp/VAogC/szpjb0ZbP+bTG9e1pvjjDEmVHypObsL4mwLYxH5GojJYtfjvsQkIrHAVKCfe4z80/Klt8YA4BkgBWeKLsGGDDXGhKFA3hBU1Y7Z7RORHSIS6641x+I8OZ1VurLAF8ATqrrCm8/1pQY8FDhPVf/z4RhjjMlzedjmPBvoB7zg/v+szAlEJAr4HJiiqtO9fWNfemv8iXPX0RhjwppL07xe/PQC0ElE/gA6udcRkWYiMsmdpg9wGXCLiKx1L41yemNfas7DgO9EZCVw9MRGVb3Ph/cwxpigy6uHUFQ1GWe0zszbE4Db3a8/BD709b19KZzHA4twHjzJ/0M+GWMKrEL1+DaQqqoPBi0SY4wJkML2+PZid4+NOWRs1rDZt40xYaWw1Zyvd/9/mMc260pnjAk7Nvu2McaEoYJQc86xK52IPOzx+ppM+54LRlDGGOMPF+r1Eq686efc1+P1sEz7ugQwFmOMCYjCMsGrZPM6q3VjjAm5PHxCMGi8KZw1m9dZrRtjTMiFc43YW94UzheKyH6cWnIJ92vc68WDFpkxxuRSoeitoaqReRGIMcYESmFp1jDGmHwlr+YQDKawLpzPa9OIPk/dSkRkBN9OW8j8sTMz7C8SVYRbRw/izIa1ObT3ABPvHUNy4i5KlS/NwLEPcdYFdfn+/5bw6fDJ6cc0v7I1Xe/uhaqyb+ceJj/wBof2HMjTfNVvcyE9nroZiYzgh2mLWTJ2dob9kVFF6Dv6bqo3rMXhvQf56N7X2ZPojNQac86ZXP1cf4qVLom6XLzZ4wlSjx5PP/aWiUOIPrMKozs/TKg9+dxQ2nRsTcrhIzxy3wh+W7c+w/7iJYrz5uQXOaNmDVxpaSxasIxXnnkTgOYXNebxZ4dwdoO6DB7wGPPmLMyzuBu2acT1T92KREawbNpC4rO47m4fPYizGtbm0N6DjL13NMmJuwDodvdVXNqnPZrm4qOn3+XXpT9RpFhRHp02kqLFihIRGUnCl98za8xnGd7z+hG3cck17bj7vJvyLJ/eeOK50Sxd/gPRFcoz88NxoQ7HawWh5uzLkKF5SiIiuG5kf968ZRQjOg2m+ZWtia1bI0Oa1n3ac2jfQZ5sO4ivJ8+l16M3AnD86HFmvTqNGc9NyZA+IjKCPk/dyqvXjeCZrkNI/H0L7frlbW9AiRCuGnkrk295kVc7DaHRlRdTpW71DGla9GlHyr5DvNR2MMsmx9Pt0evT479uzD387/HJjI4byvi+z5B2PDX9uIadm3P08JE8zU922nRszVm1z6Bji548+dCzjHwpcy9Mx6S3p9Ll4qvp0f56mrS4kMs6XAzAtsQkHhk0nDkz5uVl2EhEBDeOvJ0xt4ziiU6DaXnlJVTLdN1d2qcDh/YdYljbQSyYPJdr3Nddtbo1aNm9NU/GDWZ0v1Hc9MwdSEQEqUeP8/L1TzO86xBGdBvC+W0aU7txvfT3q3l+HUqWLZWn+fRWz26dGDf62VCH4bOC0JUubAvnWo3qsnNLEv/9u5O046kkzFnOhXHNMqS5MK45K2Y4s2j9GL+Ccy5uCMCxlKP8mbCe4x41SgBEEBGKlSwGQPEyJdi3Yw956YxGdflvSxK7/91J2vE0fprzPedlyleDuKYkzFgKwM/xK6nrzlf9Sy9g+/p/2P77PwAc3nsQdTkXV1TJYlx6ezcWvvl5HuYmex27tGHmtC8AWLv6F8qUK03lqpUypDmScoSVyxMAOH48ld/WrScmtioAW//dzobfNuX5H09t93W3y33drZyznEZxzTOkaRzXnO9mLAEgIf57zr34fAAaxTVn5ZzlpB5L5b/EnezckkTtRnUB0r80I4tEElkkMr2fk0REcM1jNzH9+al5k0EfNWt0PuXKlgl1GD5zqcvrJVx5XTiLyDUiUsb9+gkR+Z+INAlWYOWrRrNnW3L6+p7tuylfteIpaXZvc37uu9JcpBw4TKkK2V9IrtQ0Pn5iIk/Ne5WXfphAtbo1+HbaouBkIBvlqlZgn0e+9m1PpmzVCpnSRKencaW5OHLgMCUrlKFS7VhUlf5THuX+uc/RZmD39GM6P9SHpZO+4PiRo4SDqrFV2L5tR/p60radVI2pnG36MmVL0z7uUr5f9kNehJctz2sKYM/2ZCpUjc42zYnrrnSFMlTI4tjy7mMlIoIR8S/z2urJ/PrtOjav/QOADv26sPbrBPbtOu1EzMZHha3m/KSqHhCRS4DOONOAjw1OWGT9eEumf0iRLBKd5h87okgkbW6M49nLH+bhFgNIXP8PXe/u6WegPsoyZm/SKBGREdRqfjaf3P827/QeQcPOzah78XnENjiLimdV5df5CUEJOTeyOjfZ/SFERkYyZsJzTJn0Kf9u2Rrs0E7Lm7izS3O661FdLkZ0G8pDFw2k1oV1qV7/DMpXqUDzbhex8P34wARv0qkPS9jy4dtljfv/zwPXe27LJv0AIMG9DPDlm8y9XKSq8z3Wh7kXzzTz3elQ1SKq+l9ERITnZ92iqm95rDdX1YUe65epanwuYvNn8Tlfhw8fPqCqoqp9VfV9j3RPqupQVb1LVbep6t+qmqiqx1R1SR7nC1W9R1XXupeJqnqdx74Nqhrrmd7junhXVd/I5j3fV9Xe4Xx+VPU/9/kZpqrDPPLlmc5zGa6qQ1T1clVNcp+3v1XVpaqbQnDeTrvUr1+/Zp06dbaGOo7CtvhSc94qIuNx5sOKF5FinKbmraoTVLWZe8l22vHTWAXUA2oBUThjfMzOlObE5IoAvYFFLpdrwOnyADQATvy+7gT8novY/OFzvhYvXnwc50t+PnABUBKnp00b4DecXzDVgJrAJcBGoG0Q85Cdt4FG7mUmcDPOb6BWwD5ge6b0A4BngXLAA3kX5mnl6rrDOT+zgb7Fixcf6D6+HvADzvVW3p2+BNARWI8zG3MMznmriTNHZ93AZ8l/aWlp0TmnMgHlbSmOUyD0Auq512OBuCB/e3RT1Y2q+qeqPu7eNlJVr3S/Lq6q09WpbfygqrWBBPe+v1V1t6oeVKc22cC9/U5V/V1V16nqHFWtGIJvRZ/yde65567zOPZGVf1VVX9R1ZeyeO+a7n2h/uYXVX3bncef1fmSPrFvrapSu3btn9Txu56scd/uTtPcfd4OqWqyO89heX5UtbbHsY//888/R9T5pdDVve0CVV2jzjX3i6o+lc3nHgyD85ZhqV+//if169ffXq9ePVf9+vUT69ev3z/UMRWWRVRzbnURkQhgnao2DN7XRGCISIKqNss5Zf5REPMElq/8pCDmKdx51ayhqi7gJxE5M8jxBEJumlDCXUHME1i+8pOCmKew5lXNGUBEFgHNcdrQDp3YrqpXBic0Y4wpvHx5fPvpoEVhjDEmA69rzgAichbODcGvRaQkEKmqeTswhTHGFAK+PCF4B/B/wHj3puo43aWMMcYEmC/NGvcALYCVAKr6h4hUCUpUBhGpAySq6lERaYvTv3mKqubb53xFpCrwHFBNVbuKSAPgIlWdnMOhYUtE5nCaB83y+z0Z96TO89R5OvgJoAnwrKr+GOLQCjxfHkI5qqrHTqyISBFC/PSjiBwQkf3ZLaGMLQBmAGkiUheYjPNQw8ehDclv7+M8SFPNvb6R8Hn4JLdeAV4F/gJSgInu5SDwSwjjCpS8HbbBpPOl5vyNiDyGM1VVJ+BuYE5wwvKOqp4YiGkkkARMxXki7QYg/w2llZFLVVNF5CrgNVV9U0TWhDooP1VS1c9EZBiAO39poQ7KH6r6DYCIPKOql3nsmiMiS0MUViCdOD+XA2NVdZaIjAhhPIWGLzXnR4FdwM/AQCAeeCIYQeVCZ1V9R1UPqOp+VR0LXB3qoPx0XESuw3lMeK57W9EQxhMIh0SkIu5fXCJy4rHugqCyiNQ+sSIitTg5TEB+5tOwDSZwvK45q6pLRD7AaXNWYIP60tUjuNJE5AbgU5zYruPkN35+dStwJzBKVf9y/7F/GOKY/PUgzvgTdURkOU7h1Tu0IQXMYGCJiGx2r9fEqcTkd32ALsArqrpXRGKBoSGOqVDw5SGUy4FxwJ84TQe1gIGq+mXwwvOOiNQEXgda4xTOy4EHVPXv0EUVOCJSAThDVdeFOhZ/ue9VnI1zDW1Q1eM5HJJvuGuV57hX16tqeAyunUv5adiGgsiXwnk9cIWqbnKv1wG+UNVzTn+kyQ0RWQJcifPrZi1Ok9I3qvpgKOPyh4j0ymLzPuBnVd2Z1/EEkrvf/4PAWap6h4jUA85W1bk5HBrWROQjYJiq/hPqWAobX24I7jxRMLttBsLiD0pE6uPcQa6qqg1F5ALgSlXNf5OfnVROVfeLyO3Ae6o6XETye825P3ARsNi93hZYAdQXkZGqGp5zNXnnPWA1Tv4AEoHpnLxfkF/FAr+KiA3bkMdyLJw9aju/ikg88BlO08E1OGPfhoOJOO1g4wFUdZ2IfIwzVnB+VcTdvtcHeDzUwQSICzhXVXdAer/nsUBLYClOb5v8qo6qXuu+iYuqpkiWU6PkOzZsQ4h4U3Pu7vF6B84A7+D8zK5wavKQKKmqP2T6W0jNLnE+MRKnT/C3qrrK3RPgjxDH5K+aJwpmt51AfVXdLSL5ve35mIiU4GRPlDpAvm5zBqerYFbDNoQ6rsIgx8JZVW/Ni0D89J/7j+HEH0ZvTp11I19R1ek4P4tPrG8m/3cPXCYiczmZr6uBpSJSCsi3Tz66DQfmAWe422lbA7eENKIAcA/bMACIBurgDNswDugQyrgKA19uCNYCBuF0EUov1MOh7cldq5wAXAzswXla6wZV3RLSwPwgIsVx2mjPA4qf2K6qt4UsKD+5f+b3wplKCyAZiFXVe0IXVeC4+3C3wumJskJV/8vhkLAnImtxD9ugqo3d235W1fNDG1nB58sNwZk4AaWgNQAACs9JREFUjxHPwWk7DCdbVLWjuwYWUUBGypuKM89cZ5wmjhvI+/kOA0pVVUT+xGlj7oPzJTojtFEFhvuG5lM48wIiIhEi8pGq3hDi0Px1VFWPnWgyDIdhGwoLXwrnI6r6RtAi8c9fIjIPmIYz2WZBUFdVrxGRHqr6gfsG5/xQB5Ub7t40fXEeDkrGOU+iqu1CGlhgnSkiw1T1eXd/5+lAQRgcKOyGbSgsfGnWuB5nNuEFeNzoCIfRqdw3YrrjFABNcLovfaqq34Y0MD+IyA+q2sI9PsPdOGOHnJjENl8RERewDOjv0U9+c37MS3bcTTYf4Qxv0A74UlXHhDYq/7kfROkPxOE018wHJoXR08EFli+F8/PATThPCJ5o1lBVbR+k2HLF/TTd6zhtzvn2rrK7f/MMnKFC3wNKA0+p6riQBpYL7sGb+uLcE5iH85j9JFWtFdLAAkBEmnisFsXpzrkcpwkwLCov/hKRKJwnH08M23Ash0NMAPj6hOAF4XpiRKQNcC3QFaf/9TRVLRDtmQWF+55AT5zmjfY4w09+rqoLQhqYH0Rk8Wl2h13lxVfhPGxDQedL4TwNGBSOj9mKyF84jzh/BsxW1UM5HBK2ROS0j2er6ui8iiWYRCQa50GmawtAARYBXKOq/9/evcXIWdZxHP/+Wm0walvgAojhoKKhhbagNa2JEa1owgUXCtqEIMbEEOO5mvRCY5poPNXghSBaFFFEKpQFpBcmJCJGxPQENkgtKU0aSSAaU6MWNNLy9+J5p53Zzu7O2513nvfw+yRNZ6a7m/92dp955zn8/nfnrmXcHNuQT5kFwbOA/ZJ2MTjnnH0rHbAqIpoert/T9BzqkUTEYdIUwJa5PrbuisTGT5IWOtumtrENbVfmyvnyYY/3wsZzkLQxIjZLuokh23si4jMZyrIOkvRlUieUuxnMoDicrah56ItteC9wPoOxDU9HxBdy1dYVZfKc63iMs7fvd3fWKipQZGd/ttczsFjovLHJh1Barve89B+oCaCpO1KaENvQamWunI8f44yINxaRiD+IiOzHOCVdFhFNb+E0QNITvRNZsz1mZu3Ulu7b3ykS3LaR9jc/lbugMVgg6fSI+AccX0Ar83zZhEm6BFjO4HH7O/JVNH91jm1ouzK/7LU9xhkR75Z0NulI8K2SFpO20jU5MvRG4A+StpH+nz8EfC1vSTYTSZtI+dTLSf01rwQeBRo9OFPv2IZWKzOtsZmUHHY96ZX0E8C+iKhV1rCkFcBG0hatRbnrmQ9Jy0n7gQX8OiL2ZS7JZiDpSWAV8ERErCqyqn8UEVfN8am1JmlHRKzJXUcXlRmca3uMU9Iy0gGUa0jZDb8Apuq4J3suRRrdx4ELSUeBb4uIpmdTt17fcfs9pOPb/wb+FBEXZy5tXuoc29B2pbpvkzqO/LC6ck7Z7cBW4H0R8VzuYubpp8BLpCyKK4FlwOeyVmSj2C1pKen3Yw9wBNiZt6SxWEGKbVhHX2xDcd8qNOeV81x96yJi5VgrKknSQuCOFkQzAoNZucW8/s6IeMscn2Y1otQNfnG0o1t6rWMb2myUK+eXSa+Ud5EWBf5TaUUlRcQxSWdKWtSSH6Dj7Zoi4mg72tB1Q3Fw4x2k35dHgcYPzsBeYCk+FThxI805S7qIFFZzFbCPNFA/VJe5UElbSFGhDzJ4OqtxORSSjnHiexDwKuDF4nZExOJctdnMJN1CWifYWjy0HjjY9C4vkh4hJSPWMbah1UZeEDz+CdJ64HvAtyLi25VUVVKxjekkEeHOwTYRkp4CLuktkBcL6E+2YEGwdrENXTHSgqCk15HyeN9P6tG3Abi/wrpK8SBsNfA0cB7Q61t5Li2Y1qhpbEMnzDk4S/otKSntHlI34V6QyyJJZ9Qh2KXI1B0WfOQVZauUpO2kn70lwJ8l7SzurwEey1nbOLj7dj6j7NY4xImBr/+De3Og2YNdJL217+5pwNXA0YjYmKkk64iZ3vb3NP3tv7tv5zPnlXNEXDDKF5J0ca5Mi4jYM+2h3xdX/GaVmj74FtEBbcpAqW1sQ9uN84foZ6QdExNXhAL1LABWA2fnqMW6SdINwFdJW01fpnhnSXMjQ3vcfTuT0rs1ZvxCGeMsizZVvW/kKHAI+EqTu29bs0g6ALw9Iv6eu5ZxqnNsQ9uN88p54k+WpLcBz/a6OEv6CGm++RBpP7bZpBwk7UdvlZrHNrTaOK+cH5/0MWNJjwNXRMRhSe8kBR59GrgUWBYR10yyHusuSZeRMl52MHhYo5Gt0uoe29AF47xyznF0emHfVr71wK0RMQVMFavMZpOyBXiYlCTYhtzjWsc2dMHIg7OkKeDHwK+KtzoDImLtOAsb0UJJryiOkb+HtB+zp00r5lZ/RyPi87mLGJeIuLQvtuEuahjb0HYLSnzs94FrgQOSvlk8cbltJa0m/5L0yv47AEkXAv/MWZh1zm8k3SDpHEln9P7kLmo+ImJ/RGwqpiu3k7q6bMhcVmecSrbGEtKr6ZeAZ0kLBXdGxEuzfmJFJK0FziG9or9QPPZm4DUOBLdJKXYMTVeLQ1qnakhswz3A/RFxJGthHVFqcJZ0JnAdKXz7OeDnpIjEFRHxrioKNLPJmxbbcC8nYhsAqENsQ9uVaVN1H3AR6bDJTyLi+b5/2x0Rq6sp0ay+JG2MiM3F7Q9GxLa+f/t6RHwxX3WnrgmxDW1XZnBeFxEPV1yPWaP0byGdvp00x/bSScsZ29B2o6TSfWDY7Z6IuG/cRZk1iGa4Pex+G2WLbWi7UbabzdbaPQAPztZlMcPtYffbqAsvQFmM7YSgWRf1tRXrbylGcf+0iHhlrtomoQtTN7mMMq1xXUTcKWnoBvsm9ukzG5eIcFcQq8Qo0xqvLv5+bZWFmFkjtaHjfS15WsPMZjRXbINVp8xWuteTEt8uoO+K2y3SzdpL0hXAR4G1wDbSGYf9eavqhjKD817gNqalbjW9R5qZza1usQ1dUGZw3hERayqux8xqxrENeZQZnK8F3gQ8xGCYuMOFzFrKsQ35lBmcv0F65TzIiWmNiIh1FdVmZpk5tiGfMoPzfmBlRHjrjFnLDYtq6OfYhuqV6RayF1gK/K2iWsysPhzbkFmZK+dHgJXALgbnnL2VzsxszMpcOW+qrAozqxXHNuQ38uDs/cxmneLYhszKTGusBW4ClgGLgIXACxGxuLryzMy6qcy0xs2kZo/bgNXA9aR9z2bWUo5tyKfM4ExEPCNpYUQcA26X9FhFdZlZPTxAim3YTl9sg1WvzOD8oqRFwB8lbQae58S8lJm1038j4ru5i+iiMnPO5wN/Jc03bwCWALdExDPVlWdmOTm2IZ85B2dJ50XEXyZUj5nViGMb8hllcO5v/T4VEVdPpDIzy86xDfksGOFj+rvrvqGqQsyslnqxDTZhoywIztb63cza7SxgvyTHNkzYKNMas7V+Dx9CMWsvSZcPe9wnhqvnBq9mZjU0ypyzmXWUpLWSdkk6Iul/ko5J+lfuurrAg7OZzeZmUmPXA6RpzY8Vj1nFSh3fNrPucWxDHh6czWw2jm3IxNMaZjabD5PGiU+Rdm2dC/gg2gR4t4aZncSxDfn5ytnMhnmgd0PSVM5CusqDs5kN49iGzDw4m9kwjm3IzHPOZnYSxzbk58HZzKyGPK1hZlZDHpzNzGrIg7OZWQ15cDYzqyEPzmZmNfR/lX9U4u1PK/UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cdd411a20>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(test.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Family_Members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>888</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  Age  Embarked Sex  Family_Members\n",
       "0           0       3    2         1   1               2\n",
       "1           1       1    5         0   2               2\n",
       "2           1       3    3         1   2               1\n",
       "3           1       1    4         1   2               2\n",
       "4           0       3    4         1   1               1\n",
       "..        ...     ...  ...       ...  ..             ...\n",
       "886         0       2    3         1   1               1\n",
       "887         1       1    1         1   2               1\n",
       "888         0       3    4         1   2               4\n",
       "889         1       1    3         0   1               1\n",
       "890         0       3    4         2   1               1\n",
       "\n",
       "[889 rows x 6 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>413</td>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>414</td>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>415</td>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>416</td>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>417</td>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass  Age  Embarked Sex  Family_Members\n",
       "0            892       3    2         2   1               1\n",
       "1            893       3    5         1   2               2\n",
       "2            894       2    3         2   1               1\n",
       "3            895       3    4         1   1               1\n",
       "4            896       3    4         1   2               3\n",
       "..           ...     ...  ...       ...  ..             ...\n",
       "413         1305       3    4         1   1               1\n",
       "414         1306       1    5         0   2               1\n",
       "415         1307       3    1         1   1               1\n",
       "416         1308       3    4         1   1               1\n",
       "417         1309       3    1         0   1               3\n",
       "\n",
       "[418 rows x 6 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Models for Prediction\n",
    "\n",
    "#### Now we will train multiple Machine Learning algorithms over the training data to predict the survival on our testing data and analyze the results thus obtained.\n",
    "\n",
    "#### We might also use use cross-validation in the end.\n",
    "\n",
    "####  We know that since the survival is represented as either 0 or 1, therefore it is a Classification problem. The algorithms used for the same are:\n",
    "##### Logistic Regression\n",
    "##### Support Vector Machines\n",
    "##### KNN or K-Nearest Neighbors\n",
    "##### Decision Trees\n",
    "##### Random Forest\n",
    "##### Stochastic Gradient descent (SGD)\n",
    "##### Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 5)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train= train.drop(['Survived'], axis =1)\n",
    "y_train= train['Survived']\n",
    "\n",
    "X_test=test.drop('PassengerId', axis=1).copy()\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1\n",
      " 1 0 0 0 0 0 1 1 0 0 0 1 1 1 0 1 1 0 0 0 0 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1\n",
      " 1 1 0 1 0 1 0 0 1 0 0 0 1 1 1 0 1 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0\n",
      " 1 1 1 1 0 0 1 1 1 1 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0\n",
      " 0 0 1 0 0 0 0 0 1 1 0 1 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1\n",
      " 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0\n",
      " 1 0 1 1 0 1 0 0 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1\n",
      " 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0\n",
      " 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0\n",
      " 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0\n",
      " 0 1 1 0 1 1 1 0 1 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0\n",
      " 1 1 1 1 1 1 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(X_train, y_train)\n",
    "\n",
    "y_pred = log_reg.predict(X_test)\n",
    "print(y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(y_pred), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79.64 %\n"
     ]
    }
   ],
   "source": [
    "# Checking accuracy of the linear regressor model\n",
    "\n",
    "log_reg_acc = round(log_reg.score(X_train,y_train)*100, 2)\n",
    "print(log_reg_acc,'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can find the correlation between the different features within the data inorder to analyze the survival of the people onboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Sex</td>\n",
       "      <td>2.644919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Family_Members</td>\n",
       "      <td>-0.181393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Embarked</td>\n",
       "      <td>-0.190135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Age</td>\n",
       "      <td>-0.200540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Pclass</td>\n",
       "      <td>-1.042129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Feature  Correlation\n",
       "3             Sex     2.644919\n",
       "4  Family_Members    -0.181393\n",
       "2        Embarked    -0.190135\n",
       "1             Age    -0.200540\n",
       "0          Pclass    -1.042129"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_coeff= pd.DataFrame(train.columns.delete(0))\n",
    "df_coeff.columns = ['Feature']\n",
    "df_coeff['Correlation'] = pd.Series(log_reg.coef_[0])\n",
    "\n",
    "df_coeff.sort_values(by='Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From this we can conclude that Females (Sex=2) have a higher probability of surviving."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-Validation\n",
    "#### Cross-Validation protects against overfitting.\n",
    "#### It is a resampling method which tells us how well our model would generalize to unseen data. This is achieved by fixing a number of partitions of the dataset called folds, predicting each fold separately, and averaging the predictions in the end.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [74.15730337 80.8988764  75.28089888 78.65168539 77.52808989 76.40449438\n",
      " 77.52808989 75.28089888 85.39325843 81.81818182] %\n",
      "Mean:  78.29417773237999 %\n",
      "Standard Deviation:  3.3127923441226788 %\n",
      "\n",
      "Confusion Matrix: \n",
      " [[468  81]\n",
      " [112 228]] \n",
      "\n",
      "Precision:  73.79 %\n",
      "Recall:  67.06 %\n",
      "F1 Score:  70.26 %\n"
     ]
    }
   ],
   "source": [
    "# Set our robust cross-validation scheme!\n",
    "kf = KFold(n_splits = 10, random_state = 2)\n",
    "\n",
    "# Print CV accuracy estimate:\n",
    "#print(cross_val_score(logisticRegression, X_test, y_test, cv = kf).mean())\n",
    "scores= cross_val_score(log_reg, X_train, y_train, cv = kf, scoring='accuracy')\n",
    "\n",
    "mean_acc_log = scores.mean()*100\n",
    "\n",
    "print('Scores: ', scores*100, '%')\n",
    "print('Mean: ', mean_acc_log, '%')\n",
    "print('Standard Deviation: ', scores.std()*100, '%\\n')\n",
    "\n",
    "pred= cross_val_predict(log_reg, X_train, y_train, cv=kf)\n",
    "print('Confusion Matrix: \\n' ,confusion_matrix(y_train, pred),'\\n')\n",
    "\n",
    "print(\"Precision: \", round(precision_score(y_train, pred)*100, 2),'%')\n",
    "print(\"Recall: \", round(recall_score(y_train, pred)*100, 2), '%')\n",
    "print('F1 Score: ', round(f1_score(y_train, pred)*100, 2), '%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The first row is about the not-survived-predictions: 470 passengers were correctly classified as not survived (called true negatives) and 79 where wrongly classified as not survived (false positives).\n",
    "### The second row is about the survived-predictions: 110 passengers where wrongly classified as survived (false negatives) and 230 where correctly classified as survived (true positives)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 1 0 1 1 1 0 0 0 1 0 1 1 0 0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 1\n",
      " 1 0 0 0 0 0 1 1 0 0 0 1 1 1 0 1 1 0 0 0 0 1 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0\n",
      " 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 1 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0\n",
      " 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0\n",
      " 0 0 1 0 0 1 0 0 1 1 0 1 1 0 1 0 0 1 0 0 1 1 0 0 1 0 0 1 1 0 1 1 0 1 1 0 1\n",
      " 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0\n",
      " 1 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 1\n",
      " 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0\n",
      " 1 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0\n",
      " 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0\n",
      " 0 1 0 0 1 1 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0\n",
      " 1 1 1 1 1 1 0 1 0 0 0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(418,)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train, y_train)\n",
    "\n",
    "y_pred = svc.predict(X_test)\n",
    "print(y_pred)\n",
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.37"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(y_pred), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83.01 %\n"
     ]
    }
   ],
   "source": [
    "# Checking accuracy of the support vector model\n",
    "\n",
    "svc_acc = round(svc.score(X_train,y_train)*100, 2)\n",
    "print(svc_acc,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [79.7752809  86.51685393 78.65168539 84.26966292 82.02247191 82.02247191\n",
      " 79.7752809  77.52808989 88.76404494 85.22727273] %\n",
      "Mean:  82.45531154239019 %\n",
      "Standard Deviation:  3.4750078757718716 %\n",
      "\n",
      "Confusion Matrix: \n",
      " [[491  58]\n",
      " [ 98 242]] \n",
      "\n",
      "Precision:  80.67 %\n",
      "Recall:  71.18 %\n",
      "F1 Score:  75.62 %\n"
     ]
    }
   ],
   "source": [
    "# Print CV accuracy estimate:\n",
    "#print(cross_val_score(SVC(), X_test, y_test, cv = kf).mean())\n",
    "\n",
    "scores= cross_val_score(SVC(), X_train, y_train, cv = kf, scoring='accuracy')\n",
    "\n",
    "mean_acc_svc = scores.mean()*100\n",
    "\n",
    "print('Scores: ', scores*100, '%')\n",
    "print('Mean: ', mean_acc_svc, '%')\n",
    "print('Standard Deviation: ', scores.std()*100, '%\\n')\n",
    "\n",
    "pred= cross_val_predict(svc, X_train, y_train, cv=kf)\n",
    "print('Confusion Matrix: \\n' ,confusion_matrix(y_train, pred),'\\n')\n",
    "\n",
    "print(\"Precision: \", round(precision_score(y_train, pred)*100, 2),'%')\n",
    "print(\"Recall: \", round(recall_score(y_train, pred)*100, 2), '%')\n",
    "print('F1 Score: ', round(f1_score(y_train, pred)*100, 2), '%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The first row is about the not-survived-predictions: 493 passengers were correctly classified as not survived (called true negatives) and 56 where wrongly classified as not survived (false positives).\n",
    "### The second row is about the survived-predictions: 100 passengers where wrongly classified as survived (false negatives) and 240 where correctly classified as survived (true positives)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbor (KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 1 1 1 0 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0\n",
      " 1 0 0 0 1 0 1 1 0 1 0 1 1 1 0 1 1 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 1 1 0 1 0\n",
      " 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0\n",
      " 1 1 1 1 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0\n",
      " 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 0 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 1 0 1 1 0 1\n",
      " 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 1 1 1 1 0 1 0\n",
      " 1 0 1 1 0 1 0 0 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 1\n",
      " 0 0 0 1 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0\n",
      " 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 1 0 0\n",
      " 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 1 1 1 0 0 1 0 1 1 0 1 0 0 0 1 0\n",
      " 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 1 0 0\n",
      " 1 1 1 1 1 0 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "y_pred= knn.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(y_pred), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83.8 %\n"
     ]
    }
   ],
   "source": [
    "# Checking accuracy of the knn model\n",
    "\n",
    "knn_acc = round(knn.score(X_train,y_train)*100, 2)\n",
    "print(knn_acc,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [75.28089888 79.7752809  78.65168539 82.02247191 86.51685393 75.28089888\n",
      " 79.7752809  76.40449438 83.14606742 78.40909091] %\n",
      "Mean:  79.52630234933606 %\n",
      "Standard Deviation:  3.4078007011732017 %\n",
      "\n",
      "Confusion Matrix: \n",
      " [[481  68]\n",
      " [114 226]] \n",
      "\n",
      "Precision:  76.87 %\n",
      "Recall:  66.47 %\n",
      "F1 Score:  71.29 %\n"
     ]
    }
   ],
   "source": [
    "# Print CV accuracy estimate:\n",
    "#print(cross_val_score(KNeighborsClassifier(), X_test, y_test, cv = kf).mean())\n",
    "\n",
    "scores= cross_val_score(KNeighborsClassifier(), X_train, y_train, cv = kf, scoring='accuracy')\n",
    "\n",
    "mean_acc_knn = scores.mean()*100\n",
    "\n",
    "print('Scores: ', scores*100, '%')\n",
    "print('Mean: ', mean_acc_knn, '%')\n",
    "print('Standard Deviation: ', scores.std()*100, '%\\n')\n",
    "\n",
    "pred= cross_val_predict(knn, X_train, y_train, cv=kf)\n",
    "print('Confusion Matrix: \\n' ,confusion_matrix(y_train, pred), '\\n')\n",
    "\n",
    "print(\"Precision: \", round(precision_score(y_train, pred)*100, 2),'%')\n",
    "print(\"Recall: \", round(recall_score(y_train, pred)*100, 2), '%')\n",
    "print('F1 Score: ', round(f1_score(y_train, pred)*100, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The first row is about the not-survived-predictions: 484 passengers were correctly classified as not survived (called true negatives) and 65 where wrongly classified as not survived (false positives).\n",
    "### The second row is about the survived-predictions: 107 passengers where wrongly classified as survived (false negatives) and 233 where correctly classified as survived (true positives)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 1 0 1 1 1 0 1 0 1 0 1 1 1 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 1 1 0 0 1\n",
      " 1 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0\n",
      " 1 1 1 1 0 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1\n",
      " 0 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 1 1 0 0 1 1 0 1 1 0 0 1 1 0 1 1 0 1 0 1 0\n",
      " 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0\n",
      " 1 0 1 1 0 1 0 0 0 1 0 1 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1\n",
      " 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 1 0 1 0 1 0 1 0 0 0 0 0 1 1 0 1 0 0\n",
      " 1 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0\n",
      " 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 1 0\n",
      " 0 1 0 0 0 1 1 0 0 0 0 1 0 1 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 1 0 0 0\n",
      " 1 1 0 1 1 0 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier()\n",
    "tree.fit(X_train, y_train)\n",
    "\n",
    "y_pred = tree.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(y_pred), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.727 %\n"
     ]
    }
   ],
   "source": [
    "#Checking accuracy of Decision Tree model\n",
    "\n",
    "tree_acc = round(tree.score(X_train, y_train)*100, 3)\n",
    "print(tree_acc,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [73.03370787 82.02247191 79.7752809  76.40449438 80.8988764  76.40449438\n",
      " 76.40449438 74.15730337 86.51685393 76.13636364] %\n",
      "Mean:  78.17543411644536 %\n",
      "Standard Deviation:  3.8794954123243657 %\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print CV accuracy estimate:\n",
    "#print(cross_val_score(DecisionTreeClassifier(), X_test, y_test, cv = kf).mean())\n",
    "\n",
    "scores= cross_val_score(DecisionTreeClassifier(), X_train, y_train, cv = kf, scoring='accuracy')\n",
    "\n",
    "mean_acc_tree = scores.mean()*100\n",
    "\n",
    "print('Scores: ', scores*100, '%')\n",
    "print('Mean: ', mean_acc_tree, '%')\n",
    "print('Standard Deviation: ', scores.std()*100, '%\\n')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 1 0 1 1 1 0 1 0 1 0 1 1 1 0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0\n",
      " 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 0 1 0 1 1 0 0 1 1 0 0 1\n",
      " 1 0 0 1 1 1 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0\n",
      " 1 1 1 1 0 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1\n",
      " 0 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 1 1 0 0 1 1 0 1 1 0 0 1 1 1 1 1 0 1 1 0 1\n",
      " 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 1 1 1 0 1 0\n",
      " 1 0 1 1 0 1 0 0 0 1 0 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 1\n",
      " 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 1 0 1 0 1 0 1 0 0 0 0 0 1 1 0 1 0 0\n",
      " 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0\n",
      " 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 1 1 1 0 0 0 0 1 1 0 1 0 0 0 1 0\n",
      " 0 1 0 0 1 1 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 1 0 0 0\n",
      " 1 1 0 1 1 0 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, y_train)\n",
    "\n",
    "y_pred = random_forest.predict(X_test)\n",
    "print(y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(y_pred),2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.727 %\n"
     ]
    }
   ],
   "source": [
    "# Checking accuracy of the Random Forest Classifier\n",
    "\n",
    "random_forest.score(X_train, y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, y_train) * 100, 3)\n",
    "print(acc_random_forest,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [74.15730337 77.52808989 82.02247191 75.28089888 82.02247191 78.65168539\n",
      " 78.65168539 74.15730337 87.64044944 80.68181818] %\n",
      "Mean:  79.07941777323799 %\n",
      "Standard Deviation:  3.985445770810656 %\n",
      "\n",
      "Confusion Matrix: \n",
      " [[477  72]\n",
      " [115 225]] \n",
      "\n",
      "Precision:  75.76 %\n",
      "Recall:  66.18 %\n",
      "F1 Score:  70.64 %\n"
     ]
    }
   ],
   "source": [
    "# Print CV accuracy estimate:\n",
    "#print(cross_val_score(RandomForestClassifier(), X_test, y_test, cv = kf).mean())\n",
    "\n",
    "scores= cross_val_score(RandomForestClassifier(), X_train, y_train, cv = kf, scoring='accuracy')\n",
    "\n",
    "mean_acc_ranfor = scores.mean()*100\n",
    "\n",
    "print('Scores: ', scores*100, '%')\n",
    "print('Mean: ', mean_acc_ranfor, '%')\n",
    "print('Standard Deviation: ', scores.std()*100, '%\\n')\n",
    "\n",
    "pred= cross_val_predict(random_forest, X_train, y_train, cv=kf)\n",
    "print('Confusion Matrix: \\n' ,confusion_matrix(y_train, pred), '\\n')\n",
    "\n",
    "print(\"Precision: \", round(precision_score(y_train, pred)*100, 2),'%')\n",
    "print(\"Recall: \", round(recall_score(y_train, pred)*100, 2), '%')\n",
    "print('F1 Score: ', round(f1_score(y_train, pred)*100, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The first row is about the not-survived-predictions: 479 passengers were correctly classified as not survived (called true negatives) and 70 where wrongly classified as not survived (false positives).\n",
    "### The second row is about the survived-predictions: 115 passengers where wrongly classified as survived (false negatives) and 225 where correctly classified as survived (true positives)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic Gradient Descent (SGD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 1 0 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 0 1 1 1 0 1 0 1 0 0 0 1 1 1 0 1\n",
      " 1 0 0 0 1 0 1 1 0 1 0 1 1 1 0 1 1 0 0 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 0 1 1\n",
      " 1 1 0 1 0 1 0 1 1 0 0 0 1 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0\n",
      " 1 1 1 1 0 0 1 1 1 1 0 1 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 1 0 1 1 1 0 1 0 1 0\n",
      " 1 0 1 0 0 1 0 0 1 1 1 1 1 0 1 0 0 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 0 1 1 0 1\n",
      " 0 1 0 1 0 0 1 0 0 0 0 1 1 0 1 1 0 1 1 0 1 1 0 1 0 0 0 0 1 1 1 1 1 1 0 1 0\n",
      " 1 0 1 1 0 1 0 0 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 1 1 0 1 1 1 0 1 0 0 0 0 0 1\n",
      " 0 0 0 1 1 0 0 1 0 1 0 1 0 1 1 0 1 0 0 0 0 1 0 1 1 1 0 0 1 0 0 1 1 0 1 0 0\n",
      " 1 0 1 0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 1 0\n",
      " 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 1 1 1 0 0 1 0 1 1 0 1 1 0 1 1 0\n",
      " 0 1 1 0 1 1 1 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 1 0 0\n",
      " 1 1 1 1 1 1 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "sgd = SGDClassifier(max_iter=5, tol=None)\n",
    "sgd.fit(X_train, y_train)\n",
    "\n",
    "y_pred = sgd.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(y_pred), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75.03 %\n"
     ]
    }
   ],
   "source": [
    "#Checking accuracy of the SGD model\n",
    "\n",
    "sgd.score(X_train, y_train)\n",
    "acc_sgd = round(sgd.score(X_train, y_train) * 100, 2)\n",
    "print(acc_sgd,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [67.41573034 77.52808989 66.29213483 74.15730337 74.15730337 69.66292135\n",
      " 78.65168539 71.91011236 78.65168539 80.68181818] %\n",
      "Mean:  73.9108784473953 %\n",
      "Standard Deviation:  4.756912892690448 %\n",
      "\n",
      "Confusion Matrix: \n",
      " [[458  91]\n",
      " [167 173]] \n",
      "\n",
      "Precision:  65.53 %\n",
      "Recall:  50.88 %\n",
      "F1 Score:  57.28 %\n"
     ]
    }
   ],
   "source": [
    "# Print CV accuracy estimate:\n",
    "#print(cross_val_score(SGDClassifier(max_iter=5, tol=None), X_test, y_test, cv = kf).mean())\n",
    "\n",
    "scores= cross_val_score(SGDClassifier(max_iter=5, tol=None), X_train, y_train, cv = kf, scoring='accuracy')\n",
    "\n",
    "mean_acc_sgd = scores.mean()*100\n",
    "\n",
    "print('Scores: ', scores*100, '%')\n",
    "print('Mean: ', mean_acc_sgd, '%')\n",
    "print('Standard Deviation: ', scores.std()*100, '%\\n')\n",
    "\n",
    "pred= cross_val_predict(sgd, X_train, y_train, cv=kf)\n",
    "print('Confusion Matrix: \\n' ,confusion_matrix(y_train, pred), '\\n')\n",
    "\n",
    "print(\"Precision: \", round(precision_score(y_train, pred)*100, 2),'%')\n",
    "print(\"Recall: \", round(recall_score(y_train, pred)*100, 2), '%')\n",
    "print('F1 Score: ', round(f1_score(y_train, pred)*100, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The first row is about the not-survived-predictions: 458 passengers were correctly classified as not survived (called true negatives) and 91 where wrongly classified as not survived (false positives).\n",
    "### The second row is about the survived-predictions: 16 passengers where wrongly classified as survived (false negatives) and 224 where correctly classified as survived (true positives)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 0 1 1 1 0 1 1 1 0 1 0 0 0 0 0 1 1 1 0 1\n",
      " 1 0 0 0 0 0 1 1 0 1 0 1 1 1 0 1 1 0 0 0 0 0 1 0 0 0 1 0 1 1 0 1 1 1 0 1 1\n",
      " 1 1 0 1 0 1 0 0 0 0 0 0 1 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0\n",
      " 1 1 1 1 0 0 1 1 1 1 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0\n",
      " 0 0 1 0 0 1 0 0 1 1 0 1 1 0 1 0 0 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 0 1 1 0 1\n",
      " 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0\n",
      " 1 0 1 1 0 1 0 0 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 0 1 0 1 1 1 0 1 0 0 0 0 0 1\n",
      " 0 0 0 1 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0\n",
      " 1 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0\n",
      " 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0\n",
      " 0 1 0 0 1 1 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0\n",
      " 1 1 1 1 1 1 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "gaussian = GaussianNB() \n",
    "gaussian.fit(X_train, y_train)  \n",
    "\n",
    "y_pred = gaussian.predict(X_test)\n",
    "print(y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(y_pred), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79.98 %\n"
     ]
    }
   ],
   "source": [
    "#Checking accuracy for the Gaussian Naive Bayes model\n",
    "\n",
    "acc_gaussian = round(gaussian.score(X_train, y_train) * 100, 2)\n",
    "print(acc_gaussian,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores:  [75.28089888 77.52808989 77.52808989 82.02247191 77.52808989 78.65168539\n",
      " 79.7752809  80.8988764  83.14606742 81.81818182] %\n",
      "Mean:  79.41777323799796 %\n",
      "Standard Deviation:  2.3912633037949353 %\n",
      "\n",
      "Confusion Matrix: \n",
      " [[464  85]\n",
      " [ 98 242]] \n",
      "\n",
      "Precision:  74.01 %\n",
      "Recall:  71.18 %\n",
      "F1 Score:  72.56 %\n"
     ]
    }
   ],
   "source": [
    "# Print CV accuracy estimate:\n",
    "#print(cross_val_score(GaussianNB() , X_test, y_test, cv = kf).mean())\n",
    "\n",
    "scores= cross_val_score(GaussianNB() , X_train, y_train, cv = kf, scoring='accuracy')\n",
    "\n",
    "mean_acc_gau = scores.mean()*100\n",
    "\n",
    "print('Scores: ', scores*100, '%')\n",
    "print('Mean: ', mean_acc_gau, '%')\n",
    "print('Standard Deviation: ', scores.std()*100, '%\\n')\n",
    "\n",
    "#Confusion Matrix\n",
    "pred= cross_val_predict(gaussian, X_train, y_train, cv=kf)\n",
    "print('Confusion Matrix: \\n' ,confusion_matrix(y_train, pred), '\\n')\n",
    "\n",
    "print(\"Precision: \", round(precision_score(y_train, pred)*100, 2),'%')\n",
    "print(\"Recall: \", round(recall_score(y_train, pred)*100, 2), '%')\n",
    "print('F1 Score: ', round(f1_score(y_train, pred)*100, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The first row is about the not-survived-predictions: 464 passengers were correctly classified as not survived (called true negatives) and 85 where wrongly classified as not survived (false positives).\n",
    "### The second row is about the survived-predictions: 98 passengers where wrongly classified as survived (false negatives) and 242 where correctly classified as survived (true positives)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding the Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>Score</th>\n",
       "      <th>Mean Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>83.010</td>\n",
       "      <td>82.455312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>KNN</td>\n",
       "      <td>83.800</td>\n",
       "      <td>79.526302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Gaussian Naive Bayes</td>\n",
       "      <td>79.980</td>\n",
       "      <td>79.417773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.727</td>\n",
       "      <td>79.079418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>79.640</td>\n",
       "      <td>78.294178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>86.727</td>\n",
       "      <td>78.175434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>75.030</td>\n",
       "      <td>73.910878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Score  Mean Score\n",
       "Model                                         \n",
       "Support Vector Machines     83.010   82.455312\n",
       "KNN                         83.800   79.526302\n",
       "Gaussian Naive Bayes        79.980   79.417773\n",
       "Random Forest               86.727   79.079418\n",
       "Logistic Regression         79.640   78.294178\n",
       "Decision Tree               86.727   78.175434\n",
       "Stochastic Gradient Decent  75.030   73.910878"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({\n",
    "    'Model': ['Logistic Regression', 'Support Vector Machines', 'KNN',  \n",
    "              'Decision Tree','Random Forest',\n",
    "              'Stochastic Gradient Decent', \n",
    "              'Gaussian Naive Bayes'],\n",
    "    'Score': [log_reg_acc, svc_acc, knn_acc,  \n",
    "              tree_acc, acc_random_forest, acc_sgd,\n",
    "              acc_gaussian],\n",
    "    'Mean Score': [mean_acc_log, mean_acc_svc, mean_acc_knn,\n",
    "                   mean_acc_tree, mean_acc_ranfor, \n",
    "                   mean_acc_sgd, mean_acc_gau]})\n",
    "df_result = results.sort_values(by='Mean Score', ascending=False)\n",
    "df_result = df_result.set_index('Model')\n",
    "df_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  As we can see, the Decision Tree Classifer & Random Forest Classifier coincidently outputs an equal amount of 86.73% accuracy on the test set and are both the most successful models for the set of features and the model that we have built.\n",
    "### But, we also know that just calculating the accuracy without cross-validating could result in Overfitting that we can easily see in our models. So, after performing cross-validation, we find out the mean accuracy of each of the model which correctly identifies Support Vector Machines (SVC) as the best model with an accuracy of 82.45% for the given dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Importance\n",
      "Features                  \n",
      "Sex                  0.416\n",
      "Age                  0.184\n",
      "Pclass               0.177\n",
      "Family_Members       0.160\n",
      "Embarked             0.063\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cdd7fa940>"
      ]
     },
     "execution_count": 75,
     "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": [
    "importances=pd.DataFrame({'Features': X_train.columns, 'Importance':np.round(random_forest.feature_importances_, 3)})\n",
    "importances = importances.sort_values('Importance',ascending=False).set_index('Features')\n",
    "print(importances.head())\n",
    "importances.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Importance\n",
      "Features                  \n",
      "Sex                  0.485\n",
      "Pclass               0.175\n",
      "Family_Members       0.169\n",
      "Age                  0.135\n",
      "Embarked             0.036\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22cdd798b38>"
      ]
     },
     "execution_count": 76,
     "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": [
    "importances=pd.DataFrame({'Features': X_train.columns, 'Importance':np.round(tree.feature_importances_, 3)})\n",
    "importances = importances.sort_values('Importance',ascending=False).set_index('Features')\n",
    "print(importances.head())\n",
    "importances.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Embarked</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Family_Members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>413</td>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>414</td>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>415</td>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>416</td>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>417</td>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass  Age  Embarked Sex  Family_Members\n",
       "0            892       3    2         2   1               1\n",
       "1            893       3    5         1   2               2\n",
       "2            894       2    3         2   1               1\n",
       "3            895       3    4         1   1               1\n",
       "4            896       3    4         1   2               3\n",
       "..           ...     ...  ...       ...  ..             ...\n",
       "413         1305       3    4         1   1               1\n",
       "414         1306       1    5         0   2               1\n",
       "415         1307       3    1         1   1               1\n",
       "416         1308       3    4         1   1               1\n",
       "417         1309       3    1         0   1               3\n",
       "\n",
       "[418 rows x 6 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SUBMISSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        'PassengerId': test['PassengerId'],\n",
    "        'Survived': y_pred\n",
    "    })\n",
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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