Titanic Survival Prediction Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic survival prediction model\n",
    "#### by  Apoorva Tiwari "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleansing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</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",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of      PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
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       "      <td>Q</td>\n",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
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       "      <td>S</td>\n",
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       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
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       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>38.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>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": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2         1       3                             Heikkinen, Miss. Laina   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['PassengerId'],inplace=True,axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived      0\n",
      "Pclass        0\n",
      "Name          0\n",
      "Sex           0\n",
      "Age         177\n",
      "SibSp         0\n",
      "Parch         0\n",
      "Ticket        0\n",
      "Fare          0\n",
      "Cabin       687\n",
      "Embarked      2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2         1       3                             Heikkinen, Miss. Laina   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "5         0       3                                   Moran, Mr. James   \n",
       "6         0       1                            McCarthy, Mr. Timothy J   \n",
       "7         0       3                     Palsson, Master. Gosta Leonard   \n",
       "8         1       3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)   \n",
       "9         1       2                Nasser, Mrs. Nicholas (Adele Achem)   \n",
       "\n",
       "      Sex        Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.000000      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.000000      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.000000      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.000000      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.000000      0      0            373450   8.0500   NaN        S  \n",
       "5    male  29.699118      0      0            330877   8.4583   NaN        Q  \n",
       "6    male  54.000000      0      0             17463  51.8625   E46        S  \n",
       "7    male   2.000000      3      1            349909  21.0750   NaN        S  \n",
       "8  female  27.000000      0      2            347742  11.1333   NaN        S  \n",
       "9  female  14.000000      1      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].fillna(df['Age'].mean(),inplace=True)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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>Info unavailable</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>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>Info unavailable</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>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>Info unavailable</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2         1       3                             Heikkinen, Miss. Laina   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare             Cabin  \\\n",
       "0    male  22.0      1      0         A/5 21171   7.2500  Info unavailable   \n",
       "1  female  38.0      1      0          PC 17599  71.2833               C85   \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250  Info unavailable   \n",
       "3  female  35.0      1      0            113803  53.1000              C123   \n",
       "4    male  35.0      0      0            373450   8.0500  Info unavailable   \n",
       "\n",
       "  Embarked  \n",
       "0        S  \n",
       "1        C  \n",
       "2        S  \n",
       "3        S  \n",
       "4        S  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin'].replace(to_replace=np.nan,value='Info unavailable',inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     889\n",
       "unique      3\n",
       "top         S\n",
       "freq      644\n",
       "Name: Embarked, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since only 3 values are missing, the most common values are filled in place of null values\n",
    "df['Embarked']=df['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis and Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "testdf=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>417.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.627188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.907576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
       "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
       "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
       "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
       "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
       "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
       "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
       "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
       "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId      0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age             86\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             1\n",
      "Cabin          327\n",
      "Embarked         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(testdf.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since the age and cabin columns have many null values, they are replaced with suitable data \n",
    "testdf['Age'].fillna(testdf['Age'].mean(),inplace=True)\n",
    "testdf['Cabin'].replace(to_replace=np.nan,value='Info unavailable',inplace=True)\n",
    "testdf['Embarked']=testdf['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sex vs Survived\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "survived='Survived'\n",
    "not_survived='not survived'\n",
    "fig,axes=plt.subplots(nrows=1, ncols=2 , figsize=(20,4))\n",
    "female=df[df['Sex']=='female']\n",
    "male=df[df['Sex']=='male']\n",
    "ax=sns.distplot(female[female[survived]==1].Age.dropna(),bins=12,label=survived,ax=axes[0],kde=False)\n",
    "ax=sns.distplot(female[female[survived]==0].Age.dropna(),bins=30,label=not_survived,ax=axes[0],kde=False)\n",
    "ax.legend()\n",
    "ax.set_title('Female')\n",
    "\n",
    "ax=sns.distplot(male[male[survived]==1].Age.dropna(),bins=12,label=survived,ax=axes[1],kde=False)\n",
    "ax=sns.distplot(male[male[survived]==0].Age.dropna(),bins=30,label=not_survived,ax=axes[1],kde=False)\n",
    "ax.legend()\n",
    "_=ax.set_title('Male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1692d1507c8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 588.9x972 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Embarked vs Pclass vs Sex\n",
    "FacetGrid=sns.FacetGrid(df,row='Embarked',height=4.5,aspect=1.6)\n",
    "FacetGrid.map(sns.pointplot,'Pclass','Survived','Sex',palette=None,order=None,hue_order=None)\n",
    "FacetGrid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    61.616162\n",
       "1    38.383838\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Survived.value_counts()/len(df)*100"
   ]
  },
  {
   "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>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.069809</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.331339</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.069809</td>\n",
       "      <td>-0.331339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.232625</td>\n",
       "      <td>-0.179191</td>\n",
       "      <td>0.091566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.232625</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.179191</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.091566</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived    Pclass       Age     SibSp     Parch      Fare\n",
       "Survived  1.000000 -0.338481 -0.069809 -0.035322  0.081629  0.257307\n",
       "Pclass   -0.338481  1.000000 -0.331339  0.083081  0.018443 -0.549500\n",
       "Age      -0.069809 -0.331339  1.000000 -0.232625 -0.179191  0.091566\n",
       "SibSp    -0.035322  0.083081 -0.232625  1.000000  0.414838  0.159651\n",
       "Parch     0.081629  0.018443 -0.179191  0.414838  1.000000  0.216225\n",
       "Fare      0.257307 -0.549500  0.091566  0.159651  0.216225  1.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Correlations among different features of the dataset')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize =(8,6))\n",
    "sns.heatmap(df.corr(),annot=True,cmap=\"PiYG\")\n",
    "plt.title(\"Correlations among different features of the dataset\",fontsize=10)\n",
    "\n",
    "#It is observed that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    537\n",
       "0    354\n",
       "Name: not_alone, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SibSp and Parch\n",
    "data=[df,testdf]\n",
    "for dataset in data:\n",
    "    dataset['relatives']=dataset['SibSp']+dataset['Parch']\n",
    "    dataset.loc[dataset['relatives']>0,'not_alone']=0\n",
    "    dataset.loc[dataset['relatives']==0,'not_alone']=1\n",
    "    dataset['not_alone']=dataset['not_alone'].astype(int)\n",
    "df['not_alone'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4Ld8+XLdUkiRJUoNUVZAj4uiI+GhE3A78I/ALYG5dk0mSJEkNUO37IP8LcDnw4sxcXsc8kiRJUkONWpAjohX4fWZ+dgzySJIkSQ016hKLzBwE9o+IjjHII0mSJDVUtUss/gDcEBFXA5sf3pmZn6lLKkmSJKlBqi3IyytfLcCU+sWRJEmSGquqgpyZH6t3EEmSJKkZVFWQI+J6IHfdn5kvqHkiSZIkqYGqXWLx3uL2ROCVwEDt40iSJEmNVe0Si5t32XVDRPy0DnkkSZKkhqp2icWMYrMF6AEOqEsiSZIkqYGqXWJxM4+sQR4AlgJvrkcgSZIkqZH2WJAj4mTg/sycX9l+PcPrj5cCd9Y9nSRJkjTGRvskvS8B2wEi4jTgE8BlwHrgkvpGkyRJksbeaEssWjNzTeX2ecAlmfkt4FsR8ev6RpMkSZLG3mhnkFsj4uES/ULgx8V91a5fliRJksaN0Uru5cBPI2I1sAX4OUBEPJXhZRaSJEnSPmWPBTkz/yYi/gM4EPhhZj78ThYtwDvrHU6SJEkaa6MtsSAzb8zM72Tm5mLfbzNz0WiPjYgFEXFPRCyOiPfvYdyrIiIjoqf66JIkSVLtjVqQn6iIaAU+D5wJHANcEBHHjDBuCnAR8Mt6ZZEkSZKqVbeCDJwCLM7MJZm5HbgCWDjCuI8Dnwa21jGLJEmSVJV6FuSDgPuL7WWVfTtFxAnAwZl5TR1zSJIkSVWrZ0GOEfblzjsjWoC/B/581IkiLoyI3ojo7evrq2FESZIk6dHqWZCXAQcX23OB5cX2FOBY4CcRsRR4FnD1SBfqZeYlmdmTmT3d3d11jCxJkqQnu3oW5JuAwyNifkR0AOcDVz98Z2auz8yZmTkvM+cBNwLnZGZvHTNJkiRJe1S3gpyZA8A7gOuAu4ArM/OOiLg4Is6p1/8rSZIk7Y145LM/xoeenp7s7fUksyRJkvbaSNfM1XWJhSRJkjTuWJAlSZKkggVZkiRJKliQJUmSpIIFWZIkSSpYkCVJkqSCBVmSJEkqWJAlSZKkggVZkiRJKliQJUmSpIIFWZIkSSpYkCVJkqSCBVmSJEkqWJAlSZKkggVZkiRJKliQJUmSpIIFWZIkSSpYkCVJkqSCBVmSJEkqWJAlSZKkggVZkiRJKliQJUmSpIIFWZIkSSpYkCVJkqSCBVmSJEkqWJAlSZKkggVZkiRJKliQJUmSpIIFWZIkSSpYkCVJkqSCBVmSJEkqWJAlSZKkggVZkiRJKliQJUmSpIIFWZIkSSpYkCVJkqSCBVmSJEkqWJAlSZKkggVZkiRJKliQJUmSpIIFWZIkSSpYkCVJkqSCBVmSJEkqWJAlSZKkggVZkiRJKliQJUmSpEJdC3JELIiIeyJicUS8f4T73xMRd0bErRHxHxHxlHrmkSRJkkZTt4IcEa3A54EzgWOACyLimF2G3QL0ZOZxwDeBT9crjyRJklSNep5BPgVYnJlLMnM7cAWwsByQmddnZn9l80Zgbh3zSJIkSaOqZ0E+CLi/2F5W2bc7bwb+faQ7IuLCiOiNiN6+vr4aRpQkSZIerZ4FOUbYlyMOjHgN0AP87Uj3Z+YlmdmTmT3d3d01jChJkiQ9Wlsd514GHFxszwWW7zooIl4EfBB4XmZuq2MeSZIkaVT1PIN8E3B4RMyPiA7gfODqckBEnAB8CTgnM1fVMYskSZJUlboV5MwcAN4BXAfcBVyZmXdExMURcU5l2N8C+wFXRcSvI+Lq3UwnSZIkjYnIHHFZcNPq6enJ3t7eRseQJEnS+DfSNXN+kp4kSZJUsiBLkiRJBQuyJEmSVLAgS5IkSQULsiRJklSwIEuSJEkFC7IkSZJUsCBLkiRJBQuyJEmSVLAgS5IkSQULsiRJklSwIEuSJEkFC7IkSZJUsCBLkiRJBQuyJEmSVLAgS5IkSQULsiRJklSwIEuSJEkFC7IkSZJUsCBLkiRJBQuyJEmSVLAgS5IkSQULsiRJklSwIEuSJEkFC7IkSZJUsCBLkiRJBQuyJEmSVLAgS5IkSQULsiRJklSwIEuSJEkFC7IkSZJUsCBLkiRJBQuyJEmSVLAgS5IkSQULsiRJklSwIEuSJEkFC7IkSZJUsCBLkiRJBQuyJEmSVLAgS5IkSQULsiRJklSwIEuSJEkFC7IkSZJUsCBLkiRJBQuyJEmSVLAgS5IkSYW2ek4eEQuAzwKtwJcz85O73D8B+BpwEvAQcF5mLq1npscrM/mzr/2S3iUPMbEdNm2Frs4W1m4eYtMgDAKdQEcbdLTDtkHYsh0SmD4J/un1z+bEeTMa/CzGwI6tsGwRLF8EAzuGv/abAVMPhsnTYdODsOYP0DkDdmyDuSfBA4ug+wiYMBnmHN/oZzAmVmxawcrNK+lo6eD+9fezeXAzJ80+ia1DW1m7ZS2zJ89m+Ybl9A/0M7NzJlPapzB90nRaW1oZyiH2n7R/o5/CmBjcsIGWSZOI9naGtmyBTFo6OxlYu5aYNAkGBmjdbz+2P/AALV1dsH07LVOnMrRhA23Tpzc6/pjbtmWAzeu20t7RSktLsGP7IF2zJ7P6gU3sN60DAgYHksnTJjQ66pjr37CeCZMns7FvNdu39pNDSdcBB7Bh9WqmzJjB9q1bIZMJkyezdfMmJk2ZytoVK5h58CG0ttX1R2TTGNi0ncHN22ntHH6tALRObiciHjN2sH8HOTBE29Qn32sJYN26dUycOJGJEycyMDDAli1b6OjoYHBwkM7OzhEf09/fz6RJk0Y8nvuazGRgYB2trdMYHFxPW1sXEcHgYD8AER1s29ZHR8d0WlsnMjS0g8HB/srt7bS1TWnwM3hEZGZ9Jo5oBX4LnAEsA24CLsjMO4sxbwOOy8y3RsT5wMsz87w9zdvT05O9vb11ybyrC7/2S3545+qazHX7R1/MfhPbazJX0/n+u+Dmr+7dHNECf3wVHP6imkRqNvdtuI/zvn8emwY27dU8R04/kstfejntLfvma2lw/Xoe+B/vYfMvfkHr9OlMOuEZbP7PG8ihIVqnTmVwzRp4+IdMWyvsGHjkwW1tMDDAhKOOYu4//D0d8+Y15DmMpYHtg1z7T7dy/51rqxo/bdYkzvvgKbRPaK1zssbru28p1/zDp1jzwP1PeI4XvOFPOeHMs2uYqrkMPLSFvn++jcF12x5zX2vXBPZ/3TF0zNlv576+S29j2+/WDd8/YyKz33UCLROeHL9ErFu3ji9/+cts2jT8Pby7u5uHHnqIoaGhnWPmzJnDueeeS1dXFwCrVq3iqquuoq+vjxkzZvDKV76Sgw46qCH5x8LGjXdy+x3vor9/CRHtZO6gc9J8pkw9jlWr/p3MQSIgcxAI9t//eWzYcCs7dqxh+DxqMmvWAo45+u9obR3TX8BG/M2lnkssTgEWZ+aSzNwOXAEs3GXMQuCyyu1vAi+MJvoVq1blGODlX7ihZnM1lZW37305BsghuPJ1ez9Pk3rfz9631+UY4J619/D5Wz5fg0TNafUXvsjmX/wCgMG1a9n04+vJ7dthYGC4HANkDn+V5RhgYHh72913s/Lij49l7Ia59fplVZdjgPWrtvDz/3NPHRM1jx9d8r/3qhwD/PiyS9jWv7lGiZrPuqt/P2I5Bhhct4213/rdzu3Ntzy4sxwDDK7ZyrrvLK57xmbx7W9/e2c5Bujr63tUOQZYvnw511133c7ta665hr6+PgDWrFnDd7/73bEJ2yB33f0B+vuXAJC5A4D+Lffy4IPfY7gGDlbKMUDy0EM/qZRjGP57/BCrVl3LAw/861hHH1E9C/JBQPndaVll34hjMnMAWA885m/IEXFhRPRGRO/DL7bxZulD/Y2OUB8rb6vdXDv20WMELF2/tGZz/abvNzWbq9lsvfvuppqn2a1e9vh/6Vr1h411SNJ8+v6wdO8nyWTdyhV7P0+T2rZ8z6+fHSseuX/b4nWPuX/7in33l4ddVds9Vq5cOeLth+cYHBzc9SH7jE2bavN9d+OmO0cfNAbqWZBHOhO863qOasaQmZdkZk9m9nR3d9ck3Fj7o6fObHSE+pj33NrNNXkfPUbASbNPqtlcC+YtqNlczWbyc55Tm3lOPbUm8zS7g49+/OutD33G+Pwe+ng95bhn7PUcLa1tzDzkKTVI05wmHb7n18+Epz5yf+dJsx9z/8SjngTX11TMnz+/qnGHHXbYiLcB5s2bR2vrvru8acaM2nzfnTHjj2oyz96qZ0FeBhxcbM8Flu9uTES0AdOANTSJz7z6uJrME8BX3nhKTeZqOl0Hw8IvsNcvpUnT4cKf1SRSM/rUaZ/i2JnH7tUc7S3tvOywl3HeUXtcpj+u7f/mN7H/f38z7XPm0Hnyycy86CImHHEE7fPnM+nEE2mZPp2WKVNoO+AA2mbPhtZWaGsj9tuPtlmzaO3uZtrChRzwVx9q9FMZE0c9+0BOWvAUWkb4mRstj77d0hocdlI3J7+0uh/0492L//QijnzOaXRO66Kto2P0B+yyuq994iRe/eH/RWvbvrneH6DrnMOYcPQuJTeA1mDiUTOY8eojdu6eeGgXU06fS7S3DN//tBl0nfnkeC0BLFy4kHnz5tHS0kJ7ezvHH388XV1dtLS00NraysSJEznxxBM544wzdj7m7LPP5ulPfzpTp07l6KOP5hWveEUDn0H9HX30p5k16yw6OrqZMOFAOjpmMWvWWRxx+IeZPPlwJk16Ch0d3US00d4+g0MPfQ9dXc+kvX1/Ojq6mTTxEA479H0ceMDLGv1UgPpepNfG8EV6LwQeYPgivT/OzDuKMW8Hnl5cpPeKzDx3T/OO5UV6kiRJ2qeNeO1b3S4/zcyBiHgHcB3Dlyd+JTPviIiLgd7MvBq4FPh6RCxm+Mzx+fXKI0mSJFWjbmeQ68UzyJIkSaqRMX+bN0mSJGncsSBLkiRJBQuyJEmSVLAgS5IkSQULsiRJklSwIEuSJEkFC7IkSZJUsCBLkiRJBQuyJEmSVLAgS5IkSQULsiRJklSIzGx0hsclIvqAPzQ6xy5mAqsbHaLJeYyq43EanceoOh6n0XmMquNxGp3HqDrNeJxWZ+aCXXeOu4LcjCKiNzN7Gp2jmXmMquNxGp3HqDoep9F5jKrjcRqdx6g64+k4ucRCkiRJKliQJUmSpIIFuTYuaXSAccBjVB2P0+g8RtXxOI3OY1Qdj9PoPEbVGTfHyTXIkiRJUsEzyJIkSVLBgixJkiQVLMh7ISIWRMQ9EbE4It7f6DzNKCK+EhGrIuL2RmdpVhFxcERcHxF3RcQdEfGuRmdqRhExMSJ+FRG/qRynjzU6U7OKiNaIuCUirml0lmYVEUsj4raI+HVE9DY6TzOKiK6I+GZE3F35/vTsRmdqNhFxZOU19PDXhoh4d6NzNYORfv5HxIyI+FFE/K7y7/RGZtwT1yA/QRHRCvwWOANYBtwEXJCZdzY0WJOJiNOATcDXMvPYRudpRhFxIHBgZi6KiCnAzcDLfC09WkQEMDkzN0VEO/CfwLsy88YGR2s6EfEeoAeYmpkvbXSeZhQRS4GezGy2Dy1oGhFxGfDzzPxyRHQAnZm5rtG5mlWlFzwAPDMzm+0DzcbcSD//I+LTwJrM/GTlxOL0zPzLRubcHc8gP3GnAIszc0lmbgeuABY2OFPTycyfAWsanaOZZeaKzFxUub0RuAs4qLGpmk8O21TZbK98+Rv+LiJiLvAS4MuNzqLxKyKmAqcBlwJk5nbL8aheCPzecjxsNz//FwKXVW5fBrxsTEM9DhbkJ+4g4P5iexmWGu2liJgHnAD8srFJmlNl6cCvgVXAjzLT4/RY/wD8BTDU6CBNLoEfRsTNEXFho8M0oUOBPuBfKst1vhwRkxsdqsmdD1ze6BBNbnZmroDhk0PArAbn2S0L8hMXI+zzbJaesIjYD/gW8O7M3NDoPM0oMwcz8xnAXOCUiHDZTiEiXgqsysybG51lHDg1M08EzgTeXvlzsB7RBpwIfDEzTwA2A15rsxuVJSjnAFc1Ootqw4L8xC0DDi625wLLG5RF41xlTe23gG9k5rcbnafZVf7U+xNgQYOjNJtTgXMq62uvAF4QEf/a2EjNKTOXV/5dBXyH4WVzesQyYFnxV5pvMlyYNbIzgUWZ+WCjgzS5B9mBx3cAAAOXSURBVCvX3Tx8/c2qBufZLQvyE3cTcHhEzK/85ng+cHWDM2kcqlx8dilwV2Z+ptF5mlVEdEdEV+X2JOBFwN2NTdVcMvMDmTk3M+cx/D3px5n5mgbHajoRMblyQSyVZQMvBnynnUJmrgTuj4gjK7teCHjh8O5dgMsrqnE18PrK7dcD32tglj1qa3SA8SozByLiHcB1QCvwlcy8o8Gxmk5EXA48H5gZEcuAj2TmpY1N1XROBV4L3FZZXwvwPzPz2gZmakYHApdVrhRvAa7MTN/GTE/EbOA7w7+b0gb8W2b+oLGRmtI7gW9UTgItAd7Y4DxNKSI6GX5Hqz9tdJZmMtLPf+CTwJUR8WbgPuDVjUu4Z77NmyRJklRwiYUkSZJUsCBLkiRJBQuyJEmSVLAgS5IkSQULsiRJklSwIEvSOBERP4mInlHGvLvytlMPb1/78PtHS5KqY0GWpCYSw/bme/O7gZ0FOTPPqnzyoCSpShZkSWqwiJgXEXdFxBeARcBrI+K/ImJRRFwVEfuN8JgvRkRvRNwRER+r7LsImANcHxHXV/YtjYiZEfGpiHhb8fiPRsSfV26/LyJuiohbi7kmR8T/jYjfRMTtEXFe/Y+EJDUHC7IkNYcjga8x/IlcbwZelJknAr3Ae0YY/8HM7AGOA54XEcdl5ueA5cDpmXn6LuOvAMqSey5wVUS8GDgcOAV4BnBSRJwGLACWZ+bxmXks4CfNSXrSsCBLUnP4Q2beCDwLOAa4ofLR468HnjLC+HMjYhFwC/C0ymN2KzNvAWZFxJyIOB5Ym5n3AS+ufN3C8NnroxguzLcBL6qcef6jzFxfk2cpSeNAW6MDSJIA2Fz5N4AfZeYFuxsYEfOB9wInZ+baiPgqMLGK/+ObwKuAAxg+o/zw//eJzPzSCP/PScBZwCci4oeZeXG1T0aSxjPPIEtSc7kRODUingoQEZ0RccQuY6YyXKjXR8Rs4Mzivo3AlN3MfQVwPsMl+ZuVfdcBb3p4nXNEHBQRsyJiDtCfmf8K/B1w4t4/NUkaHzyDLElNJDP7IuINwOURMaGy+0PAb4sxv4mIW4A7gCXADcUUlwD/HhErdl2HnJl3RMQU4IHMXFHZ98OIOBr4r4gA2AS8Bngq8LcRMQTsAP6s9s9WkppTZGajM0iSJElNwyUWkiRJUsGCLEmSJBUsyJIkSVLBgixJkiQVLMiSJElSwYIsSZIkFSzIkiRJUuH/A/jfGRBYMmW0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes=sns.catplot('relatives','Survived',data=df,aspect=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1692f70d488>,\n",
       "  <matplotlib.axis.XTick at 0x1692f71a1c8>,\n",
       "  <matplotlib.axis.XTick at 0x1692f71ad48>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10,8))\n",
    "sns.kdeplot(df.loc[(df['Survived']==0),'Pclass'],shade=True,color='r',label='Not Survived')\n",
    "a=sns.kdeplot(df.loc[(df['Survived']==1),'Pclass'],shade=True,color='b',label='Survived')\n",
    "labels=['First','Second','Third']\n",
    "plt.xticks(sorted(df.Pclass.unique()),labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1692e6cc288>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(figsize=(10,8))\n",
    "sns.kdeplot(df.loc[(df['Survived']==0),'Age'],shade=True,color='r',label='Not Survived')\n",
    "sns.kdeplot(df.loc[(df['Survived']==1),'Age'],shade=True,color='b',label='Survived')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[df,testdf]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset['Fare']=dataset['Fare'].fillna(0)\n",
    "    dataset['Fare']=dataset['Fare'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "genders={'male':0,'female':1}\n",
    "data=[df,testdf]\n",
    "for dataset in data:\n",
    "    dataset['Sex']=dataset['Sex'].map(genders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        891\n",
       "unique       681\n",
       "top       347082\n",
       "freq           7\n",
       "Name: Ticket, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[df,testdf]\n",
    "for dataset in data:\n",
    "    dataset=dataset.drop(['Ticket'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "port={'S':0,'C':1,'Q':2}\n",
    "data=[df,testdf]\n",
    "for dataset in data:\n",
    "    dataset['Embarked']=dataset['Embarked'].map(port)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[df,testdf]\n",
    "for dataset in data:\n",
    "    dataset['Age']=dataset['Age'].astype(int)\n",
    "    dataset.loc[dataset['Age']<=11,'Age']=0\n",
    "    dataset.loc[(dataset['Age']>11)&(dataset['Age']<=18),'Age']=1\n",
    "    dataset.loc[(dataset['Age']>18)&(dataset['Age']<=22),'Age']=2\n",
    "    dataset.loc[(dataset['Age']>22)&(dataset['Age']<=27),'Age']=3\n",
    "    dataset.loc[(dataset['Age']>27)&(dataset['Age']<=33),'Age']=4\n",
    "    dataset.loc[(dataset['Age']>33)&(dataset['Age']<=40),'Age']=5\n",
    "    dataset.loc[(dataset['Age']>40)&(dataset['Age']<=66),'Age']=6\n",
    "    dataset.loc[(dataset['Age']>66),'Age']=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[df,testdf]\n",
    "for dataset in data:\n",
    "    dataset.loc[dataset['Fare']<=7.91,'Fare']=0\n",
    "    dataset.loc[(dataset['Fare']>7.91)&(dataset['Fare']<=14.454),'Fare']=1\n",
    "    dataset.loc[(dataset['Fare']>14.454)&(dataset['Fare']<=31),'Fare']=2\n",
    "    dataset.loc[(dataset['Fare']>31)&(dataset['Fare']<=99),'Fare']=3\n",
    "    dataset.loc[(dataset['Fare']>99)&(dataset['Fare']<=250),'Fare']=4\n",
    "    dataset.loc[(dataset['Fare']>250),'Fare']=5\n",
    "    dataset['Cabin'].replace(to_replace='Info unavailable',value=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
       "       'Fare', 'Cabin', 'Embarked', 'relatives', 'not alone', 'not_alone'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>relatives</th>\n",
       "      <th>not alone</th>\n",
       "      <th>not_alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>0</td>\n",
       "      <td>C85</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>0</td>\n",
       "      <td>C123</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  Sex  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris    0   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1   \n",
       "2         1       3                             Heikkinen, Miss. Laina    1   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1   \n",
       "4         0       3                           Allen, Mr. William Henry    0   \n",
       "\n",
       "   Age  SibSp  Parch            Ticket  Fare Cabin  Embarked  relatives  \\\n",
       "0    2      1      0         A/5 21171     0     0         0          1   \n",
       "1    5      1      0          PC 17599     0   C85         1          1   \n",
       "2    3      0      0  STON/O2. 3101282     0     0         0          0   \n",
       "3    5      1      0            113803     0  C123         0          1   \n",
       "4    5      0      0            373450     0     0         0          0   \n",
       "\n",
       "   not alone  not_alone  \n",
       "0        0.0          0  \n",
       "1        0.0          0  \n",
       "2        1.0          1  \n",
       "3        0.0          0  \n",
       "4        1.0          1  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.0\n",
       "mean       0.0\n",
       "std        0.0\n",
       "min        0.0\n",
       "25%        0.0\n",
       "50%        0.0\n",
       "75%        0.0\n",
       "max        0.0\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Fare.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>relatives</th>\n",
       "      <th>not_alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch  Fare  relatives  not_alone\n",
       "0         0       3    0    2      1      0     0          1          0\n",
       "1         1       1    1    5      1      0     0          1          0\n",
       "2         1       3    1    3      0      0     0          0          1\n",
       "3         1       1    1    5      1      0     0          1          0\n",
       "4         0       3    0    5      0      0     0          0          1"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "independent_var=['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'relatives', 'not_alone',]\n",
    "data=df[independent_var]\n",
    "label=df.Fare\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data.drop('Survived',axis=1)\n",
    "y=data['Survived']\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.55      0.67       154\n",
      "           1       0.59      0.89      0.71       114\n",
      "\n",
      "    accuracy                           0.69       268\n",
      "   macro avg       0.73      0.72      0.69       268\n",
      "weighted avg       0.75      0.69      0.69       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = regressor.predict(x_test)\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, predictions))\n",
    "acc_Logistic_Regression= round(regressor.score(x_train,y_train)* 100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.89      0.83       154\n",
      "           1       0.81      0.65      0.72       114\n",
      "\n",
      "    accuracy                           0.79       268\n",
      "   macro avg       0.79      0.77      0.77       268\n",
      "weighted avg       0.79      0.79      0.78       268\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AT\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC,LinearSVC\n",
    "linear_svc=LinearSVC()\n",
    "linear_svc.fit(x_train,y_train)\n",
    "y_pred=linear_svc.predict(x_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "acc_SVM= round(linear_svc.score(x_train,y_train)* 100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.90      0.84       154\n",
      "           1       0.83      0.68      0.74       114\n",
      "\n",
      "    accuracy                           0.80       268\n",
      "   macro avg       0.81      0.79      0.79       268\n",
      "weighted avg       0.81      0.80      0.80       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rand_forest=RandomForestClassifier(n_estimators=100)\n",
    "rand_forest.fit(x_train,y_train)\n",
    "y_predict=rand_forest.predict(x_test)\n",
    "print(classification_report(y_test, y_predict))\n",
    "acc_random_forest= round(rand_forest.score(x_train,y_train)* 100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.88      0.83       154\n",
      "           1       0.80      0.67      0.73       114\n",
      "\n",
      "    accuracy                           0.79       268\n",
      "   macro avg       0.79      0.77      0.78       268\n",
      "weighted avg       0.79      0.79      0.78       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn=KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(x_train,y_train)\n",
    "y_prediction=knn.predict(x_test)\n",
    "print(classification_report(y_test, y_prediction))\n",
    "acc_KNN= round(knn.score(x_train,y_train)* 100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.86      0.82       154\n",
      "           1       0.78      0.68      0.72       114\n",
      "\n",
      "    accuracy                           0.78       268\n",
      "   macro avg       0.78      0.77      0.77       268\n",
      "weighted avg       0.78      0.78      0.78       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gaussian=GaussianNB()\n",
    "gaussian.fit(x_train,y_train)\n",
    "y_pred=gaussian.predict(x_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "acc_gaussian= round(gaussian.score(x_train,y_train)* 100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.93      0.80       154\n",
      "           1       0.83      0.48      0.61       114\n",
      "\n",
      "    accuracy                           0.74       268\n",
      "   macro avg       0.77      0.71      0.71       268\n",
      "weighted avg       0.76      0.74      0.72       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Perceptron\n",
    "perceptron=Perceptron(max_iter=10)\n",
    "perceptron.fit(x_train,y_train)\n",
    "y_pred=perceptron.predict(x_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "acc_perceptron= round(perceptron.score(x_train,y_train)* 100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic Gradient Decent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.66      0.71       154\n",
      "           1       0.62      0.74      0.67       114\n",
      "\n",
      "    accuracy                           0.69       268\n",
      "   macro avg       0.70      0.70      0.69       268\n",
      "weighted avg       0.71      0.69      0.70       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "SGD=SGDClassifier(max_iter=5,tol=None)\n",
    "SGD.fit(x_train,y_train)\n",
    "y_pred=SGD.predict(x_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "acc_SGD= round(SGD.score(x_train,y_train)* 100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.90      0.82       154\n",
      "           1       0.81      0.61      0.70       114\n",
      "\n",
      "    accuracy                           0.78       268\n",
      "   macro avg       0.79      0.76      0.76       268\n",
      "weighted avg       0.78      0.78      0.77       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "decision=DecisionTreeClassifier()\n",
    "decision.fit(x_train,y_train)\n",
    "y_pred=decision.predict(x_test)\n",
    "print(classification_report(y_test, y_pred))\n",
    "acc_decision_tree= round(decision.score(x_train,y_train)* 100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86.84</th>\n",
       "      <td>Random Forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86.84</th>\n",
       "      <td>Decision Tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84.75</th>\n",
       "      <td>KNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80.74</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80.26</th>\n",
       "      <td>Naive Bayes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76.73</th>\n",
       "      <td>Perceptron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.63</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64.69</th>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model\n",
       "Score                            \n",
       "86.84               Random Forest\n",
       "86.84               Decision Tree\n",
       "84.75                         KNN\n",
       "80.74     Support Vector Machines\n",
       "80.26                 Naive Bayes\n",
       "76.73                  Perceptron\n",
       "70.63  Stochastic Gradient Decent\n",
       "64.69         Logistic Regression"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results=pd.DataFrame({\n",
    "    'Model':['Support Vector Machines','KNN','Logistic Regression','Random Forest','Naive Bayes','Perceptron',\n",
    "             'Stochastic Gradient Decent','Decision Tree'],\n",
    "    'Score':[acc_SVM,acc_KNN,acc_Logistic_Regression,acc_random_forest,acc_gaussian,acc_perceptron,\n",
    "             acc_SGD,acc_decision_tree]\n",
    "})\n",
    "\n",
    "result_df=results.sort_values(by='Score',ascending=False)\n",
    "result_df=result_df.set_index('Score')\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Therefore best model for this dataset = Random Forest "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': ['gini', 'entropy'],\n",
       " 'min_samples_leaf': [1, 5, 10],\n",
       " 'min_samples_split': [2, 4, 10, 12],\n",
       " 'n_estimators': [100, 400]}"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid= {\"criterion\":['gini','entropy'],'min_samples_leaf':[1,5,10],'min_samples_split':[2,4,10,12],\n",
    "             'n_estimators':[100,400]}\n",
    "param_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV , cross_val_score\n",
    "rf=RandomForestClassifier(n_estimators=100 ,max_features='auto',oob_score=True,random_state=1,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "classification=GridSearchCV(estimator=rf,param_grid=param_grid,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators=100, n_jobs=-1,\n",
       "                                              oob_score=True, random_state=1,\n",
       "                                              verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'min_samples_leaf': [1, 5, 10],\n",
       "                         'min_samples_split': [2, 4, 10, 12],\n",
       "                         'n_estimators': [100, 400]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'min_samples_leaf': 10,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest=RandomForestClassifier(criterion='entropy',min_samples_leaf=10,min_samples_split=2,n_estimators=100,\n",
    "                                    max_features='auto',oob_score=True,random_state=1,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='entropy', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=10, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=-1, oob_score=True, random_state=1, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8298555377207063"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oob score: 82.34 %\n"
     ]
    }
   ],
   "source": [
    "print('oob score:',round(random_forest.oob_score_,4)*100,'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1692fc67fc8>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "plot_confusion_matrix(rand_forest,x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.91      0.84       154\n",
      "           1       0.84      0.67      0.75       114\n",
      "\n",
      "    accuracy                           0.81       268\n",
      "   macro avg       0.82      0.79      0.79       268\n",
      "weighted avg       0.81      0.81      0.80       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_predict=random_forest.predict(x_test)\n",
    "print(classification_report(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores=random_forest.predict_proba(x_train)\n",
    "y_scores=y_scores[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "precision,recall,threshold=precision_recall_curve(y_train,y_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_precision_recall(precision,recall,threshold):\n",
    "    plt.plot(threshold,precision[:-1],'r-',label='Precision',linewidth=5)\n",
    "    plt.plot(threshold,recall[:-1],'b',label='Recall',linewidth=5)\n",
    "    plt.xlabel('threshold',fontsize=20)\n",
    "    plt.legend()\n",
    "    plt.ylim([0,1])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "plot_precision_recall(precision,recall,threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x1692fc6df08>"
      ]
     },
     "execution_count": 199,
     "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": [
    "from sklearn.metrics import plot_precision_recall_curve\n",
    "plot_precision_recall_curve(random_forest,x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1692fe7d648>"
      ]
     },
     "execution_count": 200,
     "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": [
    "from sklearn.metrics import plot_roc_curve\n",
    "plot_roc_curve(random_forest,x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}