Traincsv- EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</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": [
       "   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": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean=data[\"Age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median=data[\"Age\"].median()\n",
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mode=data[\"Age\"].mode()\n",
    "mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate count of ticket columns\n",
    "count=data[\"Ticket\"].count()\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.693428597180905"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate standard deviation of Fare\n",
    "std=data[\"Fare\"].std()\n",
    "std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80.0\n",
      "0.42\n"
     ]
    }
   ],
   "source": [
    "#calculate maximun and minimum value of age\n",
    "max=data[\"Age\"].max()\n",
    "print(max)\n",
    "min= data[\"Age\"].min()\n",
    "print(min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>562 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                            Name   Sex  \\\n",
       "0              1         0       3         Braund, Mr. Owen Harris  male   \n",
       "4              5         0       3        Allen, Mr. William Henry  male   \n",
       "5              6         0       3                Moran, Mr. James  male   \n",
       "6              7         0       1         McCarthy, Mr. Timothy J  male   \n",
       "7              8         0       3  Palsson, Master. Gosta Leonard  male   \n",
       "..           ...       ...     ...                             ...   ...   \n",
       "883          884         0       2   Banfield, Mr. Frederick James  male   \n",
       "884          885         0       3          Sutehall, Mr. Henry Jr  male   \n",
       "886          887         0       2           Montvila, Rev. Juozas  male   \n",
       "889          890         1       1           Behr, Mr. Karl Howell  male   \n",
       "890          891         0       3             Dooley, Mr. Patrick  male   \n",
       "\n",
       "      Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "4    35.0      0      0            373450   8.0500   NaN        S  \n",
       "5     NaN      0      0            330877   8.4583   NaN        Q  \n",
       "6    54.0      0      0             17463  51.8625   E46        S  \n",
       "7     2.0      3      1            349909  21.0750   NaN        S  \n",
       "..    ...    ...    ...               ...      ...   ...      ...  \n",
       "883  28.0      0      0  C.A./SOTON 34068  10.5000   NaN        S  \n",
       "884  25.0      0      0   SOTON/OQ 392076   7.0500   NaN        S  \n",
       "886  27.0      0      0            211536  13.0000   NaN        S  \n",
       "889  26.0      0      0            111369  30.0000  C148        C  \n",
       "890  32.0      0      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[562 rows x 12 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare_m =data[\"Sex\"]==\"male\"\n",
    "fare=data[\"Fare\"]\n",
    "cost_m=data.loc[fare_m & fare]\n",
    "cost_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14727.2865"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_m.sum().Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13966.6628"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare_f =data[\"Sex\"]==\"female\"\n",
    "fare=data[\"Fare\"]\n",
    "cost_f=data.loc[fare_f & fare]\n",
    "cost_f\n",
    "cost_f.sum().Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Braund, Mr. Owen Harris',\n",
       "       'Cumings, Mrs. John Bradley (Florence Briggs Thayer)',\n",
       "       'Heikkinen, Miss. Laina',\n",
       "       'Futrelle, Mrs. Jacques Heath (Lily May Peel)',\n",
       "       'Allen, Mr. William Henry', 'Moran, Mr. James',\n",
       "       'McCarthy, Mr. Timothy J', 'Palsson, Master. Gosta Leonard',\n",
       "       'Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)',\n",
       "       'Nasser, Mrs. Nicholas (Adele Achem)',\n",
       "       'Sandstrom, Miss. Marguerite Rut', 'Bonnell, Miss. Elizabeth',\n",
       "       'Saundercock, Mr. William Henry', 'Andersson, Mr. Anders Johan',\n",
       "       'Vestrom, Miss. Hulda Amanda Adolfina',\n",
       "       'Hewlett, Mrs. (Mary D Kingcome) ', 'Rice, Master. Eugene',\n",
       "       'Williams, Mr. Charles Eugene',\n",
       "       'Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)',\n",
       "       'Masselmani, Mrs. Fatima', 'Fynney, Mr. Joseph J',\n",
       "       'Beesley, Mr. Lawrence', 'McGowan, Miss. Anna \"Annie\"',\n",
       "       'Sloper, Mr. William Thompson', 'Palsson, Miss. Torborg Danira',\n",
       "       'Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)',\n",
       "       'Emir, Mr. Farred Chehab', 'Fortune, Mr. Charles Alexander',\n",
       "       'O\\'Dwyer, Miss. Ellen \"Nellie\"', 'Todoroff, Mr. Lalio',\n",
       "       'Uruchurtu, Don. Manuel E',\n",
       "       'Spencer, Mrs. William Augustus (Marie Eugenie)',\n",
       "       'Glynn, Miss. Mary Agatha', 'Wheadon, Mr. Edward H',\n",
       "       'Meyer, Mr. Edgar Joseph', 'Holverson, Mr. Alexander Oskar',\n",
       "       'Mamee, Mr. Hanna', 'Cann, Mr. Ernest Charles',\n",
       "       'Vander Planke, Miss. Augusta Maria',\n",
       "       'Nicola-Yarred, Miss. Jamila',\n",
       "       'Ahlin, Mrs. Johan (Johanna Persdotter Larsson)',\n",
       "       'Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)',\n",
       "       'Kraeff, Mr. Theodor', 'Laroche, Miss. Simonne Marie Anne Andree',\n",
       "       'Devaney, Miss. Margaret Delia', 'Rogers, Mr. William John',\n",
       "       'Lennon, Mr. Denis', \"O'Driscoll, Miss. Bridget\",\n",
       "       'Samaan, Mr. Youssef',\n",
       "       'Arnold-Franchi, Mrs. Josef (Josefine Franchi)',\n",
       "       'Panula, Master. Juha Niilo', 'Nosworthy, Mr. Richard Cater',\n",
       "       'Harper, Mrs. Henry Sleeper (Myna Haxtun)',\n",
       "       'Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)',\n",
       "       'Ostby, Mr. Engelhart Cornelius', 'Woolner, Mr. Hugh',\n",
       "       'Rugg, Miss. Emily', 'Novel, Mr. Mansouer',\n",
       "       'West, Miss. Constance Mirium',\n",
       "       'Goodwin, Master. William Frederick', 'Sirayanian, Mr. Orsen',\n",
       "       'Icard, Miss. Amelie', 'Harris, Mr. Henry Birkhardt',\n",
       "       'Skoog, Master. Harald', 'Stewart, Mr. Albert A',\n",
       "       'Moubarek, Master. Gerios', 'Nye, Mrs. (Elizabeth Ramell)',\n",
       "       'Crease, Mr. Ernest James', 'Andersson, Miss. Erna Alexandra',\n",
       "       'Kink, Mr. Vincenz', 'Jenkin, Mr. Stephen Curnow',\n",
       "       'Goodwin, Miss. Lillian Amy', 'Hood, Mr. Ambrose Jr',\n",
       "       'Chronopoulos, Mr. Apostolos', 'Bing, Mr. Lee',\n",
       "       'Moen, Mr. Sigurd Hansen', 'Staneff, Mr. Ivan',\n",
       "       'Moutal, Mr. Rahamin Haim', 'Caldwell, Master. Alden Gates',\n",
       "       'Dowdell, Miss. Elizabeth', 'Waelens, Mr. Achille',\n",
       "       'Sheerlinck, Mr. Jan Baptist', 'McDermott, Miss. Brigdet Delia',\n",
       "       'Carrau, Mr. Francisco M', 'Ilett, Miss. Bertha',\n",
       "       'Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)',\n",
       "       'Ford, Mr. William Neal', 'Slocovski, Mr. Selman Francis',\n",
       "       'Fortune, Miss. Mabel Helen', 'Celotti, Mr. Francesco',\n",
       "       'Christmann, Mr. Emil', 'Andreasson, Mr. Paul Edvin',\n",
       "       'Chaffee, Mr. Herbert Fuller', 'Dean, Mr. Bertram Frank',\n",
       "       'Coxon, Mr. Daniel', 'Shorney, Mr. Charles Joseph',\n",
       "       'Goldschmidt, Mr. George B', 'Greenfield, Mr. William Bertram',\n",
       "       'Doling, Mrs. John T (Ada Julia Bone)', 'Kantor, Mr. Sinai',\n",
       "       'Petranec, Miss. Matilda', 'Petroff, Mr. Pastcho (\"Pentcho\")',\n",
       "       'White, Mr. Richard Frasar', 'Johansson, Mr. Gustaf Joel',\n",
       "       'Gustafsson, Mr. Anders Vilhelm', 'Mionoff, Mr. Stoytcho',\n",
       "       'Salkjelsvik, Miss. Anna Kristine', 'Moss, Mr. Albert Johan',\n",
       "       'Rekic, Mr. Tido', 'Moran, Miss. Bertha',\n",
       "       'Porter, Mr. Walter Chamberlain', 'Zabour, Miss. Hileni',\n",
       "       'Barton, Mr. David John', 'Jussila, Miss. Katriina',\n",
       "       'Attalah, Miss. Malake', 'Pekoniemi, Mr. Edvard',\n",
       "       'Connors, Mr. Patrick', 'Turpin, Mr. William John Robert',\n",
       "       'Baxter, Mr. Quigg Edmond', 'Andersson, Miss. Ellis Anna Maria',\n",
       "       'Hickman, Mr. Stanley George', 'Moore, Mr. Leonard Charles',\n",
       "       'Nasser, Mr. Nicholas', 'Webber, Miss. Susan',\n",
       "       'White, Mr. Percival Wayland', 'Nicola-Yarred, Master. Elias',\n",
       "       'McMahon, Mr. Martin', 'Madsen, Mr. Fridtjof Arne',\n",
       "       'Peter, Miss. Anna', 'Ekstrom, Mr. Johan', 'Drazenoic, Mr. Jozef',\n",
       "       'Coelho, Mr. Domingos Fernandeo',\n",
       "       'Robins, Mrs. Alexander A (Grace Charity Laury)',\n",
       "       'Weisz, Mrs. Leopold (Mathilde Francoise Pede)',\n",
       "       'Sobey, Mr. Samuel James Hayden', 'Richard, Mr. Emile',\n",
       "       'Newsom, Miss. Helen Monypeny', 'Futrelle, Mr. Jacques Heath',\n",
       "       'Osen, Mr. Olaf Elon', 'Giglio, Mr. Victor',\n",
       "       'Boulos, Mrs. Joseph (Sultana)', 'Nysten, Miss. Anna Sofia',\n",
       "       'Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)',\n",
       "       'Burke, Mr. Jeremiah', 'Andrew, Mr. Edgardo Samuel',\n",
       "       'Nicholls, Mr. Joseph Charles',\n",
       "       'Andersson, Mr. August Edvard (\"Wennerstrom\")',\n",
       "       'Ford, Miss. Robina Maggie \"Ruby\"',\n",
       "       'Navratil, Mr. Michel (\"Louis M Hoffman\")',\n",
       "       'Byles, Rev. Thomas Roussel Davids', 'Bateman, Rev. Robert James',\n",
       "       'Pears, Mrs. Thomas (Edith Wearne)', 'Meo, Mr. Alfonzo',\n",
       "       'van Billiard, Mr. Austin Blyler', 'Olsen, Mr. Ole Martin',\n",
       "       'Williams, Mr. Charles Duane', 'Gilnagh, Miss. Katherine \"Katie\"',\n",
       "       'Corn, Mr. Harry', 'Smiljanic, Mr. Mile',\n",
       "       'Sage, Master. Thomas Henry', 'Cribb, Mr. John Hatfield',\n",
       "       'Watt, Mrs. James (Elizabeth \"Bessie\" Inglis Milne)',\n",
       "       'Bengtsson, Mr. John Viktor', 'Calic, Mr. Jovo',\n",
       "       'Panula, Master. Eino Viljami',\n",
       "       'Goldsmith, Master. Frank John William \"Frankie\"',\n",
       "       'Chibnall, Mrs. (Edith Martha Bowerman)',\n",
       "       'Skoog, Mrs. William (Anna Bernhardina Karlsson)',\n",
       "       'Baumann, Mr. John D', 'Ling, Mr. Lee',\n",
       "       'Van der hoef, Mr. Wyckoff', 'Rice, Master. Arthur',\n",
       "       'Johnson, Miss. Eleanor Ileen', 'Sivola, Mr. Antti Wilhelm',\n",
       "       'Smith, Mr. James Clinch', 'Klasen, Mr. Klas Albin',\n",
       "       'Lefebre, Master. Henry Forbes', 'Isham, Miss. Ann Elizabeth',\n",
       "       'Hale, Mr. Reginald', 'Leonard, Mr. Lionel',\n",
       "       'Sage, Miss. Constance Gladys', 'Pernot, Mr. Rene',\n",
       "       'Asplund, Master. Clarence Gustaf Hugo',\n",
       "       'Becker, Master. Richard F', 'Kink-Heilmann, Miss. Luise Gretchen',\n",
       "       'Rood, Mr. Hugh Roscoe',\n",
       "       'O\\'Brien, Mrs. Thomas (Johanna \"Hannah\" Godfrey)',\n",
       "       'Romaine, Mr. Charles Hallace (\"Mr C Rolmane\")',\n",
       "       'Bourke, Mr. John', 'Turcin, Mr. Stjepan', 'Pinsky, Mrs. (Rosa)',\n",
       "       'Carbines, Mr. William',\n",
       "       'Andersen-Jensen, Miss. Carla Christine Nielsine',\n",
       "       'Navratil, Master. Michel M',\n",
       "       'Brown, Mrs. James Joseph (Margaret Tobin)',\n",
       "       'Lurette, Miss. Elise', 'Mernagh, Mr. Robert',\n",
       "       'Olsen, Mr. Karl Siegwart Andreas',\n",
       "       'Madigan, Miss. Margaret \"Maggie\"',\n",
       "       'Yrois, Miss. Henriette (\"Mrs Harbeck\")',\n",
       "       'Vande Walle, Mr. Nestor Cyriel', 'Sage, Mr. Frederick',\n",
       "       'Johanson, Mr. Jakob Alfred', 'Youseff, Mr. Gerious',\n",
       "       'Cohen, Mr. Gurshon \"Gus\"', 'Strom, Miss. Telma Matilda',\n",
       "       'Backstrom, Mr. Karl Alfred', 'Albimona, Mr. Nassef Cassem',\n",
       "       'Carr, Miss. Helen \"Ellen\"', 'Blank, Mr. Henry', 'Ali, Mr. Ahmed',\n",
       "       'Cameron, Miss. Clear Annie', 'Perkin, Mr. John Henry',\n",
       "       'Givard, Mr. Hans Kristensen', 'Kiernan, Mr. Philip',\n",
       "       'Newell, Miss. Madeleine', 'Honkanen, Miss. Eliina',\n",
       "       'Jacobsohn, Mr. Sidney Samuel', 'Bazzani, Miss. Albina',\n",
       "       'Harris, Mr. Walter', 'Sunderland, Mr. Victor Francis',\n",
       "       'Bracken, Mr. James H', 'Green, Mr. George Henry',\n",
       "       'Nenkoff, Mr. Christo', 'Hoyt, Mr. Frederick Maxfield',\n",
       "       'Berglund, Mr. Karl Ivar Sven', 'Mellors, Mr. William John',\n",
       "       'Lovell, Mr. John Hall (\"Henry\")', 'Fahlstrom, Mr. Arne Jonas',\n",
       "       'Lefebre, Miss. Mathilde',\n",
       "       'Harris, Mrs. Henry Birkhardt (Irene Wallach)',\n",
       "       'Larsson, Mr. Bengt Edvin', 'Sjostedt, Mr. Ernst Adolf',\n",
       "       'Asplund, Miss. Lillian Gertrud',\n",
       "       'Leyson, Mr. Robert William Norman',\n",
       "       'Harknett, Miss. Alice Phoebe', 'Hold, Mr. Stephen',\n",
       "       'Collyer, Miss. Marjorie \"Lottie\"',\n",
       "       'Pengelly, Mr. Frederick William', 'Hunt, Mr. George Henry',\n",
       "       'Zabour, Miss. Thamine', 'Murphy, Miss. Katherine \"Kate\"',\n",
       "       'Coleridge, Mr. Reginald Charles', 'Maenpaa, Mr. Matti Alexanteri',\n",
       "       'Attalah, Mr. Sleiman', 'Minahan, Dr. William Edward',\n",
       "       'Lindahl, Miss. Agda Thorilda Viktoria',\n",
       "       'Hamalainen, Mrs. William (Anna)', 'Beckwith, Mr. Richard Leonard',\n",
       "       'Carter, Rev. Ernest Courtenay', 'Reed, Mr. James George',\n",
       "       'Strom, Mrs. Wilhelm (Elna Matilda Persson)',\n",
       "       'Stead, Mr. William Thomas', 'Lobb, Mr. William Arthur',\n",
       "       'Rosblom, Mrs. Viktor (Helena Wilhelmina)',\n",
       "       'Touma, Mrs. Darwis (Hanne Youssef Razi)',\n",
       "       'Thorne, Mrs. Gertrude Maybelle', 'Cherry, Miss. Gladys',\n",
       "       'Ward, Miss. Anna', 'Parrish, Mrs. (Lutie Davis)',\n",
       "       'Smith, Mr. Thomas', 'Asplund, Master. Edvin Rojj Felix',\n",
       "       'Taussig, Mr. Emil', 'Harrison, Mr. William', 'Henry, Miss. Delia',\n",
       "       'Reeves, Mr. David', 'Panula, Mr. Ernesti Arvid',\n",
       "       'Persson, Mr. Ernst Ulrik',\n",
       "       'Graham, Mrs. William Thompson (Edith Junkins)',\n",
       "       'Bissette, Miss. Amelia', 'Cairns, Mr. Alexander',\n",
       "       'Tornquist, Mr. William Henry',\n",
       "       'Mellinger, Mrs. (Elizabeth Anne Maidment)',\n",
       "       'Natsch, Mr. Charles H', 'Healy, Miss. Hanora \"Nora\"',\n",
       "       'Andrews, Miss. Kornelia Theodosia',\n",
       "       'Lindblom, Miss. Augusta Charlotta', 'Parkes, Mr. Francis \"Frank\"',\n",
       "       'Rice, Master. Eric', 'Abbott, Mrs. Stanton (Rosa Hunt)',\n",
       "       'Duane, Mr. Frank', 'Olsson, Mr. Nils Johan Goransson',\n",
       "       'de Pelsmaeker, Mr. Alfons', 'Dorking, Mr. Edward Arthur',\n",
       "       'Smith, Mr. Richard William', 'Stankovic, Mr. Ivan',\n",
       "       'de Mulder, Mr. Theodore', 'Naidenoff, Mr. Penko',\n",
       "       'Hosono, Mr. Masabumi', 'Connolly, Miss. Kate',\n",
       "       'Barber, Miss. Ellen \"Nellie\"',\n",
       "       'Bishop, Mrs. Dickinson H (Helen Walton)',\n",
       "       'Levy, Mr. Rene Jacques', 'Haas, Miss. Aloisia',\n",
       "       'Mineff, Mr. Ivan', 'Lewy, Mr. Ervin G', 'Hanna, Mr. Mansour',\n",
       "       'Allison, Miss. Helen Loraine', 'Saalfeld, Mr. Adolphe',\n",
       "       'Baxter, Mrs. James (Helene DeLaudeniere Chaput)',\n",
       "       'Kelly, Miss. Anna Katherine \"Annie Kate\"', 'McCoy, Mr. Bernard',\n",
       "       'Johnson, Mr. William Cahoone Jr', 'Keane, Miss. Nora A',\n",
       "       'Williams, Mr. Howard Hugh \"Harry\"',\n",
       "       'Allison, Master. Hudson Trevor', 'Fleming, Miss. Margaret',\n",
       "       'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)',\n",
       "       'Abelson, Mr. Samuel', 'Francatelli, Miss. Laura Mabel',\n",
       "       'Hays, Miss. Margaret Bechstein', 'Ryerson, Miss. Emily Borie',\n",
       "       'Lahtinen, Mrs. William (Anna Sylfven)', 'Hendekovic, Mr. Ignjac',\n",
       "       'Hart, Mr. Benjamin', 'Nilsson, Miss. Helmina Josefina',\n",
       "       'Kantor, Mrs. Sinai (Miriam Sternin)', 'Moraweck, Dr. Ernest',\n",
       "       'Wick, Miss. Mary Natalie',\n",
       "       'Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)',\n",
       "       'Dennis, Mr. Samuel', 'Danoff, Mr. Yoto',\n",
       "       'Slayter, Miss. Hilda Mary',\n",
       "       'Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)',\n",
       "       'Sage, Mr. George John Jr', 'Young, Miss. Marie Grice',\n",
       "       'Nysveen, Mr. Johan Hansen', 'Ball, Mrs. (Ada E Hall)',\n",
       "       'Goldsmith, Mrs. Frank John (Emily Alice Brown)',\n",
       "       'Hippach, Miss. Jean Gertrude', 'McCoy, Miss. Agnes',\n",
       "       'Partner, Mr. Austen', 'Graham, Mr. George Edward',\n",
       "       'Vander Planke, Mr. Leo Edmondus',\n",
       "       'Frauenthal, Mrs. Henry William (Clara Heinsheimer)',\n",
       "       'Denkoff, Mr. Mitto', 'Pears, Mr. Thomas Clinton',\n",
       "       'Burns, Miss. Elizabeth Margaret', 'Dahl, Mr. Karl Edwart',\n",
       "       'Blackwell, Mr. Stephen Weart', 'Navratil, Master. Edmond Roger',\n",
       "       'Fortune, Miss. Alice Elizabeth', 'Collander, Mr. Erik Gustaf',\n",
       "       'Sedgwick, Mr. Charles Frederick Waddington',\n",
       "       'Fox, Mr. Stanley Hubert', 'Brown, Miss. Amelia \"Mildred\"',\n",
       "       'Smith, Miss. Marion Elsie',\n",
       "       'Davison, Mrs. Thomas Henry (Mary E Finck)',\n",
       "       'Coutts, Master. William Loch \"William\"', 'Dimic, Mr. Jovan',\n",
       "       'Odahl, Mr. Nils Martin', 'Williams-Lambert, Mr. Fletcher Fellows',\n",
       "       'Elias, Mr. Tannous', 'Arnold-Franchi, Mr. Josef',\n",
       "       'Yousif, Mr. Wazli', 'Vanden Steen, Mr. Leo Peter',\n",
       "       'Bowerman, Miss. Elsie Edith', 'Funk, Miss. Annie Clemmer',\n",
       "       'McGovern, Miss. Mary', 'Mockler, Miss. Helen Mary \"Ellie\"',\n",
       "       'Skoog, Mr. Wilhelm', 'del Carlo, Mr. Sebastiano',\n",
       "       'Barbara, Mrs. (Catherine David)', 'Asim, Mr. Adola',\n",
       "       \"O'Brien, Mr. Thomas\", 'Adahl, Mr. Mauritz Nils Martin',\n",
       "       'Warren, Mrs. Frank Manley (Anna Sophia Atkinson)',\n",
       "       'Moussa, Mrs. (Mantoura Boulos)', 'Jermyn, Miss. Annie',\n",
       "       'Aubart, Mme. Leontine Pauline', 'Harder, Mr. George Achilles',\n",
       "       'Wiklund, Mr. Jakob Alfred', 'Beavan, Mr. William Thomas',\n",
       "       'Ringhini, Mr. Sante', 'Palsson, Miss. Stina Viola',\n",
       "       'Meyer, Mrs. Edgar Joseph (Leila Saks)',\n",
       "       'Landergren, Miss. Aurora Adelia', 'Widener, Mr. Harry Elkins',\n",
       "       'Betros, Mr. Tannous', 'Gustafsson, Mr. Karl Gideon',\n",
       "       'Bidois, Miss. Rosalie', 'Nakid, Miss. Maria (\"Mary\")',\n",
       "       'Tikkanen, Mr. Juho',\n",
       "       'Holverson, Mrs. Alexander Oskar (Mary Aline Towner)',\n",
       "       'Plotcharsky, Mr. Vasil', 'Davies, Mr. Charles Henry',\n",
       "       'Goodwin, Master. Sidney Leonard', 'Buss, Miss. Kate',\n",
       "       'Sadlier, Mr. Matthew', 'Lehmann, Miss. Bertha',\n",
       "       'Carter, Mr. William Ernest', 'Jansson, Mr. Carl Olof',\n",
       "       'Gustafsson, Mr. Johan Birger', 'Newell, Miss. Marjorie',\n",
       "       'Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)',\n",
       "       'Johansson, Mr. Erik', 'Olsson, Miss. Elina',\n",
       "       'McKane, Mr. Peter David', 'Pain, Dr. Alfred',\n",
       "       'Trout, Mrs. William H (Jessie L)', 'Niskanen, Mr. Juha',\n",
       "       'Adams, Mr. John', 'Jussila, Miss. Mari Aina',\n",
       "       'Hakkarainen, Mr. Pekka Pietari', 'Oreskovic, Miss. Marija',\n",
       "       'Gale, Mr. Shadrach', 'Widegren, Mr. Carl/Charles Peter',\n",
       "       'Richards, Master. William Rowe',\n",
       "       'Birkeland, Mr. Hans Martin Monsen', 'Lefebre, Miss. Ida',\n",
       "       'Sdycoff, Mr. Todor', 'Hart, Mr. Henry', 'Minahan, Miss. Daisy E',\n",
       "       'Cunningham, Mr. Alfred Fleming', 'Sundman, Mr. Johan Julian',\n",
       "       'Meek, Mrs. Thomas (Annie Louise Rowley)',\n",
       "       'Drew, Mrs. James Vivian (Lulu Thorne Christian)',\n",
       "       'Silven, Miss. Lyyli Karoliina', 'Matthews, Mr. William John',\n",
       "       'Van Impe, Miss. Catharina', 'Gheorgheff, Mr. Stanio',\n",
       "       'Charters, Mr. David', 'Zimmerman, Mr. Leo',\n",
       "       'Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)',\n",
       "       'Rosblom, Mr. Viktor Richard', 'Wiseman, Mr. Phillippe',\n",
       "       'Clarke, Mrs. Charles V (Ada Maria Winfield)',\n",
       "       'Phillips, Miss. Kate Florence (\"Mrs Kate Louise Phillips Marshall\")',\n",
       "       'Flynn, Mr. James', 'Pickard, Mr. Berk (Berk Trembisky)',\n",
       "       'Bjornstrom-Steffansson, Mr. Mauritz Hakan',\n",
       "       'Thorneycroft, Mrs. Percival (Florence Kate White)',\n",
       "       'Louch, Mrs. Charles Alexander (Alice Adelaide Slow)',\n",
       "       'Kallio, Mr. Nikolai Erland', 'Silvey, Mr. William Baird',\n",
       "       'Carter, Miss. Lucile Polk',\n",
       "       'Ford, Miss. Doolina Margaret \"Daisy\"',\n",
       "       'Richards, Mrs. Sidney (Emily Hocking)', 'Fortune, Mr. Mark',\n",
       "       'Kvillner, Mr. Johan Henrik Johannesson',\n",
       "       'Hart, Mrs. Benjamin (Esther Ada Bloomfield)', 'Hampe, Mr. Leon',\n",
       "       'Petterson, Mr. Johan Emil', 'Reynaldo, Ms. Encarnacion',\n",
       "       'Johannesen-Bratthammer, Mr. Bernt', 'Dodge, Master. Washington',\n",
       "       'Mellinger, Miss. Madeleine Violet', 'Seward, Mr. Frederic Kimber',\n",
       "       'Baclini, Miss. Marie Catherine', 'Peuchen, Major. Arthur Godfrey',\n",
       "       'West, Mr. Edwy Arthur', 'Hagland, Mr. Ingvald Olai Olsen',\n",
       "       'Foreman, Mr. Benjamin Laventall', 'Goldenberg, Mr. Samuel L',\n",
       "       'Peduzzi, Mr. Joseph', 'Jalsevac, Mr. Ivan',\n",
       "       'Millet, Mr. Francis Davis', 'Kenyon, Mrs. Frederick R (Marion)',\n",
       "       'Toomey, Miss. Ellen', \"O'Connor, Mr. Maurice\",\n",
       "       'Anderson, Mr. Harry', 'Morley, Mr. William', 'Gee, Mr. Arthur H',\n",
       "       'Milling, Mr. Jacob Christian', 'Maisner, Mr. Simon',\n",
       "       'Goncalves, Mr. Manuel Estanslas', 'Campbell, Mr. William',\n",
       "       'Smart, Mr. John Montgomery', 'Scanlan, Mr. James',\n",
       "       'Baclini, Miss. Helene Barbara', 'Keefe, Mr. Arthur',\n",
       "       'Cacic, Mr. Luka', 'West, Mrs. Edwy Arthur (Ada Mary Worth)',\n",
       "       'Jerwan, Mrs. Amin S (Marie Marthe Thuillard)',\n",
       "       'Strandberg, Miss. Ida Sofia', 'Clifford, Mr. George Quincy',\n",
       "       'Renouf, Mr. Peter Henry', 'Braund, Mr. Lewis Richard',\n",
       "       'Karlsson, Mr. Nils August', 'Hirvonen, Miss. Hildur E',\n",
       "       'Goodwin, Master. Harold Victor',\n",
       "       'Frost, Mr. Anthony Wood \"Archie\"', 'Rouse, Mr. Richard Henry',\n",
       "       'Turkula, Mrs. (Hedwig)', 'Bishop, Mr. Dickinson H',\n",
       "       'Lefebre, Miss. Jeannie',\n",
       "       'Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)',\n",
       "       'Kent, Mr. Edward Austin', 'Somerton, Mr. Francis William',\n",
       "       'Coutts, Master. Eden Leslie \"Neville\"',\n",
       "       'Hagland, Mr. Konrad Mathias Reiersen', 'Windelov, Mr. Einar',\n",
       "       'Molson, Mr. Harry Markland', 'Artagaveytia, Mr. Ramon',\n",
       "       'Stanley, Mr. Edward Roland', 'Yousseff, Mr. Gerious',\n",
       "       'Eustis, Miss. Elizabeth Mussey',\n",
       "       'Shellard, Mr. Frederick William',\n",
       "       'Allison, Mrs. Hudson J C (Bessie Waldo Daniels)',\n",
       "       'Svensson, Mr. Olof', 'Calic, Mr. Petar', 'Canavan, Miss. Mary',\n",
       "       \"O'Sullivan, Miss. Bridget Mary\", 'Laitinen, Miss. Kristina Sofia',\n",
       "       'Maioni, Miss. Roberta',\n",
       "       'Penasco y Castellana, Mr. Victor de Satode',\n",
       "       'Quick, Mrs. Frederick Charles (Jane Richards)',\n",
       "       'Bradley, Mr. George (\"George Arthur Brayton\")',\n",
       "       'Olsen, Mr. Henry Margido', 'Lang, Mr. Fang',\n",
       "       'Daly, Mr. Eugene Patrick', 'Webber, Mr. James',\n",
       "       'McGough, Mr. James Robert',\n",
       "       'Rothschild, Mrs. Martin (Elizabeth L. Barrett)',\n",
       "       'Coleff, Mr. Satio', 'Walker, Mr. William Anderson',\n",
       "       'Lemore, Mrs. (Amelia Milley)', 'Ryan, Mr. Patrick',\n",
       "       'Angle, Mrs. William A (Florence \"Mary\" Agnes Hughes)',\n",
       "       'Pavlovic, Mr. Stefo', 'Perreault, Miss. Anne', 'Vovk, Mr. Janko',\n",
       "       'Lahoud, Mr. Sarkis',\n",
       "       'Hippach, Mrs. Louis Albert (Ida Sophia Fischer)',\n",
       "       'Kassem, Mr. Fared', 'Farrell, Mr. James', 'Ridsdale, Miss. Lucy',\n",
       "       'Farthing, Mr. John', 'Salonen, Mr. Johan Werner',\n",
       "       'Hocking, Mr. Richard George', 'Quick, Miss. Phyllis May',\n",
       "       'Toufik, Mr. Nakli', 'Elias, Mr. Joseph Jr',\n",
       "       'Peter, Mrs. Catherine (Catherine Rizk)', 'Cacic, Miss. Marija',\n",
       "       'Hart, Miss. Eva Miriam', 'Butt, Major. Archibald Willingham',\n",
       "       'LeRoy, Miss. Bertha', 'Risien, Mr. Samuel Beard',\n",
       "       'Frolicher, Miss. Hedwig Margaritha', 'Crosby, Miss. Harriet R',\n",
       "       'Andersson, Miss. Ingeborg Constanzia',\n",
       "       'Andersson, Miss. Sigrid Elisabeth', 'Beane, Mr. Edward',\n",
       "       'Douglas, Mr. Walter Donald', 'Nicholson, Mr. Arthur Ernest',\n",
       "       'Beane, Mrs. Edward (Ethel Clarke)', 'Padro y Manent, Mr. Julian',\n",
       "       'Goldsmith, Mr. Frank John', 'Davies, Master. John Morgan Jr',\n",
       "       'Thayer, Mr. John Borland Jr', 'Sharp, Mr. Percival James R',\n",
       "       \"O'Brien, Mr. Timothy\", 'Leeni, Mr. Fahim (\"Philip Zenni\")',\n",
       "       'Ohman, Miss. Velin', 'Wright, Mr. George',\n",
       "       'Duff Gordon, Lady. (Lucille Christiana Sutherland) (\"Mrs Morgan\")',\n",
       "       'Robbins, Mr. Victor', 'Taussig, Mrs. Emil (Tillie Mandelbaum)',\n",
       "       'de Messemaeker, Mrs. Guillaume Joseph (Emma)',\n",
       "       'Morrow, Mr. Thomas Rowan', 'Sivic, Mr. Husein',\n",
       "       'Norman, Mr. Robert Douglas', 'Simmons, Mr. John',\n",
       "       'Meanwell, Miss. (Marion Ogden)', 'Davies, Mr. Alfred J',\n",
       "       'Stoytcheff, Mr. Ilia',\n",
       "       'Palsson, Mrs. Nils (Alma Cornelia Berglund)',\n",
       "       'Doharr, Mr. Tannous', 'Jonsson, Mr. Carl', 'Harris, Mr. George',\n",
       "       'Appleton, Mrs. Edward Dale (Charlotte Lamson)',\n",
       "       'Flynn, Mr. John Irwin (\"Irving\")', 'Kelly, Miss. Mary',\n",
       "       'Rush, Mr. Alfred George John', 'Patchett, Mr. George',\n",
       "       'Garside, Miss. Ethel',\n",
       "       'Silvey, Mrs. William Baird (Alice Munger)',\n",
       "       'Caram, Mrs. Joseph (Maria Elias)', 'Jussila, Mr. Eiriik',\n",
       "       'Christy, Miss. Julie Rachel',\n",
       "       'Thayer, Mrs. John Borland (Marian Longstreth Morris)',\n",
       "       'Downton, Mr. William James', 'Ross, Mr. John Hugo',\n",
       "       'Paulner, Mr. Uscher', 'Taussig, Miss. Ruth',\n",
       "       'Jarvis, Mr. John Denzil', 'Frolicher-Stehli, Mr. Maxmillian',\n",
       "       'Gilinski, Mr. Eliezer', 'Murdlin, Mr. Joseph',\n",
       "       'Rintamaki, Mr. Matti',\n",
       "       'Stephenson, Mrs. Walter Bertram (Martha Eustis)',\n",
       "       'Elsbury, Mr. William James', 'Bourke, Miss. Mary',\n",
       "       'Chapman, Mr. John Henry', 'Van Impe, Mr. Jean Baptiste',\n",
       "       'Leitch, Miss. Jessie Wills', 'Johnson, Mr. Alfred',\n",
       "       'Boulos, Mr. Hanna',\n",
       "       'Duff Gordon, Sir. Cosmo Edmund (\"Mr Morgan\")',\n",
       "       'Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)',\n",
       "       'Slabenoff, Mr. Petco', 'Harrington, Mr. Charles H',\n",
       "       'Torber, Mr. Ernst William', 'Homer, Mr. Harry (\"Mr E Haven\")',\n",
       "       'Lindell, Mr. Edvard Bengtsson', 'Karaic, Mr. Milan',\n",
       "       'Daniel, Mr. Robert Williams',\n",
       "       'Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)',\n",
       "       'Shutes, Miss. Elizabeth W',\n",
       "       'Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)',\n",
       "       'Jardin, Mr. Jose Neto', 'Murphy, Miss. Margaret Jane',\n",
       "       'Horgan, Mr. John', 'Brocklebank, Mr. William Alfred',\n",
       "       'Herman, Miss. Alice', 'Danbom, Mr. Ernst Gilbert',\n",
       "       'Lobb, Mrs. William Arthur (Cordelia K Stanlick)',\n",
       "       'Becker, Miss. Marion Louise', 'Gavey, Mr. Lawrence',\n",
       "       'Yasbeck, Mr. Antoni', 'Kimball, Mr. Edwin Nelson Jr',\n",
       "       'Nakid, Mr. Sahid', 'Hansen, Mr. Henry Damsgaard',\n",
       "       'Bowen, Mr. David John \"Dai\"', 'Sutton, Mr. Frederick',\n",
       "       'Kirkland, Rev. Charles Leonard', 'Longley, Miss. Gretchen Fiske',\n",
       "       'Bostandyeff, Mr. Guentcho', \"O'Connell, Mr. Patrick D\",\n",
       "       'Barkworth, Mr. Algernon Henry Wilson',\n",
       "       'Lundahl, Mr. Johan Svensson', 'Stahelin-Maeglin, Dr. Max',\n",
       "       'Parr, Mr. William Henry Marsh', 'Skoog, Miss. Mabel',\n",
       "       'Davis, Miss. Mary', 'Leinonen, Mr. Antti Gustaf',\n",
       "       'Collyer, Mr. Harvey', 'Panula, Mrs. Juha (Maria Emilia Ojala)',\n",
       "       'Thorneycroft, Mr. Percival', 'Jensen, Mr. Hans Peder',\n",
       "       'Sagesser, Mlle. Emma', 'Skoog, Miss. Margit Elizabeth',\n",
       "       'Foo, Mr. Choong', 'Baclini, Miss. Eugenie',\n",
       "       'Harper, Mr. Henry Sleeper', 'Cor, Mr. Liudevit',\n",
       "       'Simonius-Blumer, Col. Oberst Alfons', 'Willey, Mr. Edward',\n",
       "       'Stanley, Miss. Amy Zillah Elsie', 'Mitkoff, Mr. Mito',\n",
       "       'Doling, Miss. Elsie', 'Kalvik, Mr. Johannes Halvorsen',\n",
       "       'O\\'Leary, Miss. Hanora \"Norah\"', 'Hegarty, Miss. Hanora \"Nora\"',\n",
       "       'Hickman, Mr. Leonard Mark', 'Radeff, Mr. Alexander',\n",
       "       'Bourke, Mrs. John (Catherine)', 'Eitemiller, Mr. George Floyd',\n",
       "       'Newell, Mr. Arthur Webster', 'Frauenthal, Dr. Henry William',\n",
       "       'Badt, Mr. Mohamed', 'Colley, Mr. Edward Pomeroy',\n",
       "       'Coleff, Mr. Peju', 'Lindqvist, Mr. Eino William',\n",
       "       'Hickman, Mr. Lewis', 'Butler, Mr. Reginald Fenton',\n",
       "       'Rommetvedt, Mr. Knud Paust', 'Cook, Mr. Jacob',\n",
       "       'Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)',\n",
       "       'Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)',\n",
       "       'Davidson, Mr. Thornton', 'Mitchell, Mr. Henry Michael',\n",
       "       'Wilhelms, Mr. Charles', 'Watson, Mr. Ennis Hastings',\n",
       "       'Edvardsson, Mr. Gustaf Hjalmar', 'Sawyer, Mr. Frederick Charles',\n",
       "       'Turja, Miss. Anna Sofia',\n",
       "       'Goodwin, Mrs. Frederick (Augusta Tyler)',\n",
       "       'Cardeza, Mr. Thomas Drake Martinez', 'Peters, Miss. Katie',\n",
       "       'Hassab, Mr. Hammad', 'Olsvigen, Mr. Thor Anderson',\n",
       "       'Goodwin, Mr. Charles Edward', 'Brown, Mr. Thomas William Solomon',\n",
       "       'Laroche, Mr. Joseph Philippe Lemercier',\n",
       "       'Panula, Mr. Jaako Arnold', 'Dakic, Mr. Branko',\n",
       "       'Fischer, Mr. Eberhard Thelander',\n",
       "       'Madill, Miss. Georgette Alexandra', 'Dick, Mr. Albert Adrian',\n",
       "       'Karun, Miss. Manca', 'Lam, Mr. Ali', 'Saad, Mr. Khalil',\n",
       "       'Weir, Col. John', 'Chapman, Mr. Charles Henry',\n",
       "       'Kelly, Mr. James', 'Mullens, Miss. Katherine \"Katie\"',\n",
       "       'Thayer, Mr. John Borland',\n",
       "       'Humblen, Mr. Adolf Mathias Nicolai Olsen',\n",
       "       'Astor, Mrs. John Jacob (Madeleine Talmadge Force)',\n",
       "       'Silverthorne, Mr. Spencer Victor', 'Barbara, Miss. Saiide',\n",
       "       'Gallagher, Mr. Martin', 'Hansen, Mr. Henrik Juul',\n",
       "       'Morley, Mr. Henry Samuel (\"Mr Henry Marshall\")',\n",
       "       'Kelly, Mrs. Florence \"Fannie\"',\n",
       "       'Calderhead, Mr. Edward Pennington', 'Cleaver, Miss. Alice',\n",
       "       'Moubarek, Master. Halim Gonios (\"William George\")',\n",
       "       'Mayne, Mlle. Berthe Antonine (\"Mrs de Villiers\")',\n",
       "       'Klaber, Mr. Herman', 'Taylor, Mr. Elmer Zebley',\n",
       "       'Larsson, Mr. August Viktor', 'Greenberg, Mr. Samuel',\n",
       "       'Soholt, Mr. Peter Andreas Lauritz Andersen',\n",
       "       'Endres, Miss. Caroline Louise',\n",
       "       'Troutt, Miss. Edwina Celia \"Winnie\"', 'McEvoy, Mr. Michael',\n",
       "       'Johnson, Mr. Malkolm Joackim',\n",
       "       'Harper, Miss. Annie Jessie \"Nina\"', 'Jensen, Mr. Svend Lauritz',\n",
       "       'Gillespie, Mr. William Henry', 'Hodges, Mr. Henry Price',\n",
       "       'Chambers, Mr. Norman Campbell', 'Oreskovic, Mr. Luka',\n",
       "       'Renouf, Mrs. Peter Henry (Lillian Jefferys)',\n",
       "       'Mannion, Miss. Margareth', 'Bryhl, Mr. Kurt Arnold Gottfrid',\n",
       "       'Ilmakangas, Miss. Pieta Sofia', 'Allen, Miss. Elisabeth Walton',\n",
       "       'Hassan, Mr. Houssein G N', 'Knight, Mr. Robert J',\n",
       "       'Berriman, Mr. William John', 'Troupiansky, Mr. Moses Aaron',\n",
       "       'Williams, Mr. Leslie', 'Ford, Mrs. Edward (Margaret Ann Watson)',\n",
       "       'Lesurer, Mr. Gustave J', 'Ivanoff, Mr. Kanio',\n",
       "       'Nankoff, Mr. Minko', 'Hawksford, Mr. Walter James',\n",
       "       'Cavendish, Mr. Tyrell William',\n",
       "       'Ryerson, Miss. Susan Parker \"Suzette\"', 'McNamee, Mr. Neal',\n",
       "       'Stranden, Mr. Juho', 'Crosby, Capt. Edward Gifford',\n",
       "       'Abbott, Mr. Rossmore Edward', 'Sinkkonen, Miss. Anna',\n",
       "       'Marvin, Mr. Daniel Warner', 'Connaghton, Mr. Michael',\n",
       "       'Wells, Miss. Joan', 'Moor, Master. Meier',\n",
       "       'Vande Velde, Mr. Johannes Joseph', 'Jonkoff, Mr. Lalio',\n",
       "       'Herman, Mrs. Samuel (Jane Laver)', 'Hamalainen, Master. Viljo',\n",
       "       'Carlsson, Mr. August Sigfrid', 'Bailey, Mr. Percy Andrew',\n",
       "       'Theobald, Mr. Thomas Leonard',\n",
       "       'Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)',\n",
       "       'Garfirth, Mr. John', 'Nirva, Mr. Iisakki Antino Aijo',\n",
       "       'Barah, Mr. Hanna Assi',\n",
       "       'Carter, Mrs. William Ernest (Lucile Polk)',\n",
       "       'Eklund, Mr. Hans Linus', 'Hogeboom, Mrs. John C (Anna Andrews)',\n",
       "       'Brewe, Dr. Arthur Jackson', 'Mangan, Miss. Mary',\n",
       "       'Moran, Mr. Daniel J', 'Gronnestad, Mr. Daniel Danielsen',\n",
       "       'Lievens, Mr. Rene Aime', 'Jensen, Mr. Niels Peder',\n",
       "       'Mack, Mrs. (Mary)', 'Elias, Mr. Dibo',\n",
       "       'Hocking, Mrs. Elizabeth (Eliza Needs)',\n",
       "       'Myhrman, Mr. Pehr Fabian Oliver Malkolm', 'Tobin, Mr. Roger',\n",
       "       'Emanuel, Miss. Virginia Ethel', 'Kilgannon, Mr. Thomas J',\n",
       "       'Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)',\n",
       "       'Ayoub, Miss. Banoura',\n",
       "       'Dick, Mrs. Albert Adrian (Vera Gillespie)',\n",
       "       'Long, Mr. Milton Clyde', 'Johnston, Mr. Andrew G',\n",
       "       'Ali, Mr. William', 'Harmer, Mr. Abraham (David Lishin)',\n",
       "       'Sjoblom, Miss. Anna Sofia', 'Rice, Master. George Hugh',\n",
       "       'Dean, Master. Bertram Vere', 'Guggenheim, Mr. Benjamin',\n",
       "       'Keane, Mr. Andrew \"Andy\"', 'Gaskell, Mr. Alfred',\n",
       "       'Sage, Miss. Stella Anna', 'Hoyt, Mr. William Fisher',\n",
       "       'Dantcheff, Mr. Ristiu', 'Otter, Mr. Richard',\n",
       "       'Leader, Dr. Alice (Farnham)', 'Osman, Mrs. Mara',\n",
       "       'Ibrahim Shawah, Mr. Yousseff',\n",
       "       'Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)',\n",
       "       'Ponesell, Mr. Martin',\n",
       "       'Collyer, Mrs. Harvey (Charlotte Annie Tate)',\n",
       "       'Carter, Master. William Thornton II',\n",
       "       'Thomas, Master. Assad Alexander', 'Hedman, Mr. Oskar Arvid',\n",
       "       'Johansson, Mr. Karl Johan', 'Andrews, Mr. Thomas Jr',\n",
       "       'Pettersson, Miss. Ellen Natalia', 'Meyer, Mr. August',\n",
       "       'Chambers, Mrs. Norman Campbell (Bertha Griggs)',\n",
       "       'Alexander, Mr. William', 'Lester, Mr. James',\n",
       "       'Slemen, Mr. Richard James', 'Andersson, Miss. Ebba Iris Alfrida',\n",
       "       'Tomlin, Mr. Ernest Portage', 'Fry, Mr. Richard',\n",
       "       'Heininen, Miss. Wendla Maria', 'Mallet, Mr. Albert',\n",
       "       'Holm, Mr. John Fredrik Alexander', 'Skoog, Master. Karl Thorsten',\n",
       "       'Hays, Mrs. Charles Melville (Clara Jennings Gregg)',\n",
       "       'Lulic, Mr. Nikola', 'Reuchlin, Jonkheer. John George',\n",
       "       'Moor, Mrs. (Beila)', 'Panula, Master. Urho Abraham',\n",
       "       'Flynn, Mr. John', 'Lam, Mr. Len', 'Mallet, Master. Andre',\n",
       "       'McCormack, Mr. Thomas Joseph',\n",
       "       'Stone, Mrs. George Nelson (Martha Evelyn)',\n",
       "       'Yasbeck, Mrs. Antoni (Selini Alexander)',\n",
       "       'Richards, Master. George Sibley', 'Saad, Mr. Amin',\n",
       "       'Augustsson, Mr. Albert', 'Allum, Mr. Owen George',\n",
       "       'Compton, Miss. Sara Rebecca', 'Pasic, Mr. Jakob',\n",
       "       'Sirota, Mr. Maurice', 'Chip, Mr. Chang', 'Marechal, Mr. Pierre',\n",
       "       'Alhomaki, Mr. Ilmari Rudolf', 'Mudd, Mr. Thomas Charles',\n",
       "       'Serepeca, Miss. Augusta', 'Lemberopolous, Mr. Peter L',\n",
       "       'Culumovic, Mr. Jeso', 'Abbing, Mr. Anthony',\n",
       "       'Sage, Mr. Douglas Bullen', 'Markoff, Mr. Marin',\n",
       "       'Harper, Rev. John',\n",
       "       'Goldenberg, Mrs. Samuel L (Edwiga Grabowska)',\n",
       "       'Andersson, Master. Sigvard Harald Elias', 'Svensson, Mr. Johan',\n",
       "       'Boulos, Miss. Nourelain', 'Lines, Miss. Mary Conover',\n",
       "       'Carter, Mrs. Ernest Courtenay (Lilian Hughes)',\n",
       "       'Aks, Mrs. Sam (Leah Rosen)',\n",
       "       'Wick, Mrs. George Dennick (Mary Hitchcock)',\n",
       "       'Daly, Mr. Peter Denis ', 'Baclini, Mrs. Solomon (Latifa Qurban)',\n",
       "       'Razi, Mr. Raihed', 'Hansen, Mr. Claus Peter',\n",
       "       'Giles, Mr. Frederick Edward',\n",
       "       'Swift, Mrs. Frederick Joel (Margaret Welles Barron)',\n",
       "       'Sage, Miss. Dorothy Edith \"Dolly\"', 'Gill, Mr. John William',\n",
       "       'Bystrom, Mrs. (Karolina)', 'Duran y More, Miss. Asuncion',\n",
       "       'Roebling, Mr. Washington Augustus II',\n",
       "       'van Melkebeke, Mr. Philemon', 'Johnson, Master. Harold Theodor',\n",
       "       'Balkic, Mr. Cerin',\n",
       "       'Beckwith, Mrs. Richard Leonard (Sallie Monypeny)',\n",
       "       'Carlsson, Mr. Frans Olof', 'Vander Cruyssen, Mr. Victor',\n",
       "       'Abelson, Mrs. Samuel (Hannah Wizosky)',\n",
       "       'Najib, Miss. Adele Kiamie \"Jane\"',\n",
       "       'Gustafsson, Mr. Alfred Ossian', 'Petroff, Mr. Nedelio',\n",
       "       'Laleff, Mr. Kristo',\n",
       "       'Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)',\n",
       "       'Shelley, Mrs. William (Imanita Parrish Hall)',\n",
       "       'Markun, Mr. Johann', 'Dahlberg, Miss. Gerda Ulrika',\n",
       "       'Banfield, Mr. Frederick James', 'Sutehall, Mr. Henry Jr',\n",
       "       'Rice, Mrs. William (Margaret Norton)', 'Montvila, Rev. Juozas',\n",
       "       'Graham, Miss. Margaret Edith',\n",
       "       'Johnston, Miss. Catherine Helen \"Carrie\"',\n",
       "       'Behr, Mr. Karl Howell', 'Dooley, Mr. Patrick'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n=data[\"Name\"].unique()\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean       0.383838\n",
       "std        0.486592\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        1.000000\n",
       "max        1.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survive=data[\"Survived\"].describe()\n",
    "survive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "data[\"Fare\"].plot()\n",
    "data.plot.scatter(x=\"Pclass\", y=\"Survived\",alpha=0.5, color=\"green\")\n",
    "data.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "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": [
    "data[\"Age\"].plot.box()\n",
    "data[\"Pclass\"].plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be trying to predict a classification- survival or deceased. Let's begin our understanding of implementing Logistic Regression in Python for classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first will check whether there is any null value in the titanic dataset\n",
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see there are some NA values in the dataset in Age column and Cabin column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull(),yticklabels= False,xticklabels= True, cbar= True, cmap= \"viridis\"  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Roughly 30 percent of the Age data is missing. The proportion of Age missing is likely small enough for reasonable replacement with some form of imputation. Looking at the Cabin column, it looks like we are just missing too much of that data to do something useful with at a basic level. We'll probably drop this later, or change it to another feature like \"Cabin Known: 1 or 0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#explore further feature relations\n",
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The countplot shows that more than 500 passenger deceased and around 350 passengers have survived.Now i want to knw how many male's and female's have survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "sns.countplot(x=\"Survived\", data= data, hue= \"Sex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows that more than 450 passenger which are male had deceased and more than 200 passengers which are female has survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#I want to know how many passengers died in different passenger class\n",
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',hue='Pclass',data=data,palette='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see more than 350 passengers from Pclass-3 has deceased and around 130 more passengers from Pclass-1 has survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age distribution')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Age'].hist(bins=30,color='darkred',alpha=0.6)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel(\"Passengers\")\n",
    "plt.title(\"Age distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This histogram shows Number of passengers in the various age group. Age group between 20 to 25 have maximum passenger size of more than 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#further showing whether the passengers having any sibling or spouse with them.\n",
    "sns.countplot(x= \"SibSp\", data= data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 SibSp shows there is no sibling or Spouse travelling with the passengers.\n",
    "1 shows there is sibling or spouse travelling, 3 shows sibling or spouse is travelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Fare'].hist(color='green',bins=40,figsize=(8,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning\n",
    "We want to fill in missing age data instead of just dropping the missing age data rows. One way to do this is by filling in the mean age of all the passengers (imputation). However we can be smarter about this and check the average age by passenger class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8/6DBnHtQDufe0e9Af9Gpy2UY9957bz73uc8lSX7xi19k9+7dOeusszIwMJAk2bBhQ+bMmVOPpQEAYNLUZWd50aJFufbaa3PJJZekqakpn/3sZ3PiiSdm2bJlWblyZWbNmpWFCxfWY+mDuvfeezM0NFTK2lPF8/9/n787mPqYOXNmFi1aVPYYAFBpdYnlY489Np///Od/7e3r1q2rx3IvydDQULY+9limv3xG2aNU1vgxv/qxenxkZ7mDVNizT+0sewQAmBKm5Cv4TX/5jMzqeWfZY8Ahe7TvW2WPAABTglcFAQCAAmIZAAAKiGUAACgglgEAoIBYBgCAAmIZAAAKiGUAACgglgEAoIBYBgCAAmIZAAAKiGUAACgglgEAoIBYBgCAAmIZAAAKiGUAACgglgEAoIBYBgCAAmIZAAAKiGUAACgglgEAoIBYBgCAAmIZAAAKiGUAACgglgEAoIBYBgCAAmIZAAAKiGUAACgglgEAoIBYBgCAAi8qlh977LF85zvfyRNPPJFarVbvmQAA4Igw7WAfsG7dujz44IMZHh7Oe9/73vzsZz/L8uXLGzEbAACU6qA7y9/85jdzxx135IQTTsjll1+e733ve42YCwAASnfQWH7+soumpqYkybHHHlvfiQAA4Ahx0Msw3v3ud2fx4sXZtm1bPvShD+Wcc85pxFwAAFC6g8bykiVLcuaZZ+ZHP/pRZs2alTe+8Y2NmAsAAEp30Fi+9tpr9/33hg0bcswxx+TVr351Fi9enI6OjsLP++Uvf5kLL7wwX/3qVzNt2rRcc801aWpqymmnnZYVK1akudmz1gEAcGQ7aLGOjY3lVa96VX7/938/v/Ebv5Ff/OIX2bNnT5YuXVr4OXv37s3y5cszffr0JMkNN9yQ3t7e3H333anValm/fv3kfQUAAFAnB91Zfuqpp7Jy5cokydlnn50rrrgivb29Wbx4ceHn3Hjjjbn44ovz5S9/OUmyZcuWzJs3L0myYMGCbNy4Meeee+4B1x0bG8vg4OCL/kJerNHR0Ul/TCjD6OhoXc4Rjl7P/37zcwGN5dyrtoPG8u7du7N169bMnj07W7duzdNPP50dO3bk6aeffsGPv++++/Lyl788Z5999r5YrtVq+55No62tLbt27TroYK2trens7HwpX8uL0tbWlp0jOyf9caHR2tra6nKOcPRqa2tLEj8X0GDOvaPfgf6ic9BYXr58ea6++ur8z//8T6ZPn54LLrgg//iP/5grr7zyBT/+H/7hH9LU1JSHH344g4ODWbp0aZ566ql97x8dHU17e/shfBkAANBYB71m+Xd+53fyyU9+MmeeeWaeeeaZ/PKXv8zixYuzcOHCF/z4u+66K+vWrcvatWvT2dmZG2+8MQsWLMjAwECSX90kOGfOnMn9KgAAoA4Kd5b37NmTb37zm7nrrrty7LHHZvfu3Vm/fv2+m/ZeiqVLl2bZsmVZuXJlZs2aVRjaAABwJCmM5Xe+8535gz/4g9x88835zd/8zXzwgx98yaG8du3aff+9bt26Q58SAABKUBjLl112Wb7xjW/k8ccfz6JFi/a97DUAAEwVhdcsf/jDH84DDzyQJUuW5Bvf+EZ+8IMf5KabbsqPfvSjRs4HAAClOegNfvPmzctNN92UBx98MK9+9avziU98ohFzAQBA6V70a063t7dnyZIluf/+++s4DgAAHDledCwDAMBUI5YBAKDAQV/Br2pGRkby7FM782jft8oeBQ7Zs0/tzIi/6wJA3fnTFgAACky5neX29vbsykRm9byz7FHgkD3a9620t7eXPQYAVJ6dZQAAKCCWAQCgwJS7DAMox7333puhoaGyx6i05///rl69utxBpoCZM2dm0aJFZY8BNIBYBhpiaGgoP//5Y3nNazrKHqWyXvayliTJ+PiOkieptm3bhsseAWggsQw0zGte05E/+ZMFZY8Bh+XWWzeUPQLQQK5ZBgCAAmIZAAAKiGUAACgglgEAoIBYBgCAAmIZAAAKiGUAACgglgEAoIBYBgCAAmIZAAAKiGUAACgglgEAoIBYBgCAAmIZAAAKTCt7AACgfu69994MDQ2VPUalPf//d/Xq1eUOMgXMnDkzixYtauiaYhkAKmxoaCiPPfbTzOh4RdmjVNa0ltYkyc4doyVPUm07h58sZV2xDAAVN6PjFXnnOy4seww4LN/61/tKWdc1ywAAUEAsAwBAAbEMAAAFxDIAABSoyw1+4+Pjuf766/OTn/wkLS0tueGGG1Kr1XLNNdekqakpp512WlasWJHmZq0OAMCRqy6x/O1vfztJcs8992RgYGBfLPf29qarqyvLly/P+vXrc+6559ZjeQAAmBR12do955xz8ulPfzpJsm3btrziFa/Ili1bMm/evCTJggULsmnTpnosDQAAk6Zuz7M8bdq0LF26NA8++GC+8IUv5Nvf/naampqSJG1tbdm1a9cBP39sbCyDg4OTPtfoqCcMpxpGR0frco7Uy+joaKZPL3sKmBxH0/nnzz2qpIxzr64vSnLjjTfmz/7sz/K+970vY2Nj+94+Ojqa9vb2A35ua2trOjs7J32mtra27BzZOemPC43W1tZWl3OkXtra2jI+vqfsMWBSHE3nX1tbW3buEcxUQ73OvQMFeF0uw7j//vtz++23J0mOO+64NDU15c1vfnMGBgaSJBs2bMicOXPqsTQAAEyauuwsv+td78q1116bxYsX57nnnst1112X2bNnZ9myZVm5cmVmzZqVhQsX1mNp4Ag1MjKS4eGdufXWDWWPAodl27ad6ehoKXsMoEHqEsvHH398brnlll97+7p16+qxHAAA1EVdr1kGeF57e3va2sbzJ3+yoOxR4LDceuuGtLQc+L4boDq8KggAABQQywAAUEAsAwBAAbEMAAAFxDIAABQQywAAUGBKPnXcs0/tzKN93yp7jMp67plnkyTTjpte8iTV9exTO5P2GWWPAQCVN+VieebMmWWPUHlDI0NJkt84aUa5g1RZ+ww/ywDQAFMulhctWlT2CJW3evXqJElvb2+pcwAAHC7XLAMAQAGxDAAABcQyAAAUEMsAAFBALAMAQAGxDAAABcQyAAAUEMsAAFBALAMAQAGxDAAABabcy10DwFQyMjKSnTt35Fv/el/Zo8Bh2bnzyTS3jDd8XTvLAABQwM4yAFRYe3t7JsZb8s53XFj2KHBYvvWv96W9va3h69pZBgCAAnaWgYbZtm04t966oewxKmvXrmeTJCecML3kSapt27bhnHLKiWWPATSIWAYaYubMmWWPUHm7dw8lSWbMEHL1dMopJ/p5hilELAMNsWjRorJHqLzVq1cnSXp7e0udA6BKXLMMAAAFxDIAABQQywAAUEAsAwBAAbEMAAAFxDIAABQQywAAUEAsAwBAAbEMAAAFJv0V/Pbu3Zvrrrsujz/+ePbs2ZOPfOQjef3rX59rrrkmTU1NOe2007JixYo0N+t0AACObJMeyw888EBmzJiRm266KTt27MgFF1yQN73pTent7U1XV1eWL1+e9evX59xzz53spQEAYFJNeiyfd955Wbhw4b7jlpaWbNmyJfPmzUuSLFiwIBs3bjxoLI+NjWVwcHCyx6MBRkdHk8T3DxrMuccLef7nAqpgdHS04b/jJj2W29rakiS7d+/Oxz72sfT29ubGG29MU1PTvvfv2rXroI/T2tqazs7OyR6PBnj+Z8D3DxrLuccLaWtry849gplqaGtrq8vvuAMFeF0uHN6+fXsuu+yynH/++XnPe96z3/XJo6OjaW9vr8eyAAAwqSY9lp988slcccUVufrqq7No0aIkyemnn56BgYEkyYYNGzJnzpzJXhYAACbdpMfybbfdlpGRkdx6661ZsmRJlixZkt7e3qxZsyYXXXRR9u7du981zQAAcKSa9GuWr7/++lx//fW/9vZ169ZN9lIAAFBXnuwYAAAKiGUAACgw6ZdhAABHlp3DT+Zb/3pf2WNU1rPPPp0kmT79+JInqbadw09mxoltDV9XLANAhc2cObPsESpvaGhHkmTGia8seZJqm3FiWyk/z2IZACrs+adxpX5Wr16dJOnt7S11DurDNcsAAFBALAMAQAGxDAAABcQyAAAUEMsAAFBALAMAQAGxDAAABcQyAAAUEMsAAFBALAMAQAGxDAAABcQyAAAUEMsAAFBALAMAQAGxDAAABcQyAAAUEMsAAFBALAMAQAGxDAAABcQyAAAUEMsAAFBALAMAQAGxDAAABcQyAAAUEMsAAFBALAMAQAGxDAAABcQyAAAUEMsAAFBALAMAQIG6xfL3vve9LFmyJEny05/+NJdcckkuvfTSrFixIhMTE/VaFgAAJk1dYvkrX/lKrr/++oyNjSVJbrjhhvT29ubuu+9OrVbL+vXr67EsAABMqrrE8mtf+9qsWbNm3/GWLVsyb968JMmCBQuyadOmeiwLAACTalo9HnThwoUZGhrad1yr1dLU1JQkaWtry65duw76GGNjYxkcHKzHeNTZ6Ohokvj+QYM596Aczr1qq0ss/1/Nzf9/A3t0dDTt7e0H/ZzW1tZ0dnbWcyzqpK2tLUl8/6DBnHtQDufe0e9Af9FpyLNhnH766RkYGEiSbNiwIXPmzGnEsgAAcFgaEstLly7NmjVrctFFF2Xv3r1ZuHBhI5YFAIDDUrfLMGbOnJmvfe1rSZJTTz0169atq9dSAABQF16UBAAACohlAAAoIJYBAKCAWAYAgAJiGQAACohlAAAoIJYBAKCAWAYAgAJiGQAACohlAAAoIJYBAKCAWAYAgAJiGQAACohlAAAoIJYBAKCAWAYAgAJiGQAACohlAAAoIJYBAKCAWAYAgALTyh4A4Eg1MDCQhx9+uOwxXrShoaEkyerVq8sd5CWaP39+urq6yh4D4AXZWQaoiNbW1jzzzDMZHR0texSAyrCzDFCgq6vrqNrxvPrqq5MkO3fuzJ//+Z+XPA1ANdhZBqiAwcHBPP3000mSp59+Oj/84Q9LngigGsQyQAV89atf3e/4r//6r0uaBKBaxDJABTy/q1x0DMChEcsAFXD88ccf8BiAQyOWASrgiiuu2O/4Ax/4QEmTAFSLWAaogJe97GUHPAbg0IhlgAq488479zu+4447yhkEoGLEMkAFbN++/YDHABwasQxQASeffPIBjwE4NGIZoALOO++8/Y57enpKmgSgWrzc9RFuYGAgDz/8cNljvCRDQ0NJktWrV5c7yEs0f/78o+qljeF/u//++/c7/vrXv563ve1t5QwDUCFimUnX3t5e9ggw5ezYseOAxwAcGrF8hOvq6rLbCQBQkobF8sTERD75yU/mv/7rv3LsscfmM5/5TF73utc1anmASmttbc3Y2Nh+x3C0OtouQXT5YbU17Aa/f/mXf8mePXvyd3/3d/n4xz+ez33uc41aGqDyPvShD+13/OEPf7ikSWDqaW9vdwlihTVsZ/nf//3fc/bZZydJzjjjjPzgBz844MePjY1lcHCwEaMBVMIxxxyTvXv35phjjkmtVvM7lKNWe3t7Fi5cWPYYU4LfEwfXsFjevXv3fi+/2tLSkueeey7Tpr3wCK2trens7GzUeABHvT/+4z/OF7/4xVx55ZV505veVPY4AEeNA/2loWGx/LKXvSyjo6P7jicmJgpDGYCXrrOzM3/1V39V9hgAldKwa5bf+ta3ZsOGDUmSRx55JG94wxsatTQAAByShm3tnnvuudm4cWMuvvji1Gq1fPazn23U0gAAcEgaFsvNzc35i7/4i0YtBwAAh61hl2EAAMDRRiwDAEABsQwAAAXEMgAAFBDLAABQQCwDAEABsQwAAAXEMgAAFBDLAABQQCwDAECBhr3c9Us1NjaWwcHBsscAAKDixsbGCt/XVKvVag2cBQAAjhouwwAAgAJiGQAACohlAAAoIJYBAKCAWAYAgAJiGQAACohlJt33vve9LFmypOwxYErZu3dvrr766lx66aVZtGhR1q9fX/ZIMCWMj4/n2muvzcUXX5zFixfnZz/7WdkjMcmO2Bcl4ej0la98JQ888ECOO+64skeBKeWBBx7IjBkzctNNN2XHjh254IIL8nu/93tljwWV9+1vfztJcs8992RgYCA33HBDvvSlL5U8FZPJzjKT6rWvfW3WrFlT9hgw5Zx33nn50z/9033HLS0tJU4DU8c555yTT3/600mSbdu25RWveEXJEzHZ7CwzqRYuXJihoaGyx4App62tLUmye/fufOxjH0tvb2+5A8EUMm3atCxdujQPPvhgvvCFL5Q9DpPMzjJARWzfvj2XXXZZzj///LznPe8pexyYUm688cb88z//c5YtW5ann3667HGYRGIZoAKefPLJXHHFFbn66quzaNGisseBKeP+++/P7bffniQ57rjj0tTU5DKoihHLABVw2223ZWRkJLfeemuWLFmSJUuW5Nlnny17LKi8d73rXfnP//zPLF68OB/4wAdy3XXXpbW1teyxmERNtVqtVvYQAABwJLKzDAAABcQyAAAUEMsAAFBALAMAQAGxDAAABcQywBFoYGAg8+fP3/c0cO973/uydu3aF/zYJUuWZOvWrQ2eEGBq8HLXAEeo7u7urFq1KkmyZ8+enHfeeTn//PPT3t5e8mQAU4dYBjgK7N69O83NzfnhD3+Ym2++ObVaLSeddFJuvvnmfR/zxBNP5JOf/GTGxsayc+fOfPSjH80555yTVatWpb+/PxMTE3n3u9+dyy+/PHfddVfuv//+NDc3561vfWuWLl1a4lcHcOQSywBHqP7+/ixZsiRNTU055phjsmzZsnzmM5/JqlWrMnv27Nx11137XX7x6KOP5o/+6I/S1dWV//iP/8iaNWtyzjnn5P7778+6dety0kkn5b777kuS3HfffVm2bFnOOOOM3H333XnuuecybZo/EgD+L78ZAY5Q//syjOddd911mT17dpJk8eLF+73vla98Zb70pS/l3nvvTVNTU5577rkkycqVK7Ny5co8+eSTOfvss5MkN9xwQ7761a/m5ptvzhlnnBEv5grwwtzgB3AUedWrXpXHHnssSfLlL385Dz744L733XLLLTn//PNz0003paurK7VaLXv27Mk//dM/ZeXKlbnzzjvz9a9/PY8//ni+9rWv5VOf+lTWrVuXwcHBfPe73y3pKwI4stlZBjiKfOpTn8p1112X5ubmvPKVr8zll1+ev/mbv0mSnHfeefnLv/zL3H777Tn55JOzY8eOHHvsseno6Mj555+fjo6OnHXWWXnNa16TN77xjVm0aFFOPPHEnHTSSXnLW95S8lcGcGRqqvm3NwAAeEEuwwAAgAJiGQAACohlAAAoIJYBAKCAWAYAgAJiGQAACohlAAAo8P8Ahp3UzbKdFnIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.boxplot(x='Pclass',y='Age',data=data,palette='Set3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can see the wealthier passengers in the higher classes tend to be older, which makes sense. We'll use these average age values to impute based on Pclass for Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "\n",
    "        if Pclass == 1:\n",
    "            return 37\n",
    "\n",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "\n",
    "        else:\n",
    "            return 24\n",
    "\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age'] = data[['Age','Pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are lot of NaN values in the  cabin column we should drop the column and it can be further improved by using feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "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  24.0      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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  \n",
       "5      0            330877   8.4583        Q  \n",
       "6      0             17463  51.8625        S  \n",
       "7      1            349909  21.0750        S  \n",
       "8      2            347742  11.1333        S  \n",
       "9      0            237736  30.0708        C  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop('Cabin',axis=1,inplace=True)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  889 non-null    int64  \n",
      " 1   Survived     889 non-null    int64  \n",
      " 2   Pclass       889 non-null    int64  \n",
      " 3   Name         889 non-null    object \n",
      " 4   Sex          889 non-null    object \n",
      " 5   Age          889 non-null    float64\n",
      " 6   SibSp        889 non-null    int64  \n",
      " 7   Parch        889 non-null    int64  \n",
      " 8   Ticket       889 non-null    object \n",
      " 9   Fare         889 non-null    float64\n",
      " 10  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 83.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   Q  S\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  1\n",
       "4  0  1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(data['Embarked'],drop_first=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex = pd.get_dummies(data['Sex'],drop_first=True)\n",
    "embark = pd.get_dummies(data['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
       "0            1         0       3  22.0      1      0   7.2500\n",
       "1            2         1       1  38.0      1      0  71.2833\n",
       "2            3         1       3  26.0      0      0   7.9250\n",
       "3            4         1       1  35.0      1      0  53.1000\n",
       "4            5         0       3  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S  \\\n",
       "0            1         0       3  22.0      1      0   7.2500     1  0  1   \n",
       "1            2         1       1  38.0      1      0  71.2833     0  0  0   \n",
       "2            3         1       3  26.0      0      0   7.9250     0  0  1   \n",
       "3            4         1       1  35.0      1      0  53.1000     0  0  1   \n",
       "4            5         0       3  35.0      0      0   8.0500     1  0  1   \n",
       "\n",
       "   male  Q  S  \n",
       "0     1  0  1  \n",
       "1     0  0  0  \n",
       "2     0  0  1  \n",
       "3     0  0  1  \n",
       "4     1  0  1  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([data,sex,embark],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a logistic regression model\n",
    "Let's start by splitting our data into a training set and test set (there is another test.csv file that you can play around with in case you want to use all this data for training)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <th>male</th>\n",
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       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass   Age  SibSp  Parch     Fare  male  Q  S  male  Q  S\n",
       "0            1       3  22.0      1      0   7.2500     1  0  1     1  0  1\n",
       "1            2       1  38.0      1      0  71.2833     0  0  0     0  0  0\n",
       "2            3       3  26.0      0      0   7.9250     0  0  1     0  0  1\n",
       "3            4       1  35.0      1      0  53.1000     0  0  1     0  0  1\n",
       "4            5       3  35.0      0      0   8.0500     1  0  1     1  0  1"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop('Survived',axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Survived'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.drop('Survived',axis=1), \n",
    "                                                    data['Survived'], test_size=0.30, \n",
    "                                                    random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training And Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ashish\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = logmodel.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[146,  17],\n",
       "       [ 39,  65]], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "accuracy=confusion_matrix(y_test,predictions)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7902621722846442"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy=accuracy_score(y_test,predictions)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation\n",
    "We can check precision,recall,f1-score using classification report!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.90      0.84       163\n",
      "           1       0.79      0.62      0.70       104\n",
      "\n",
      "    accuracy                           0.79       267\n",
      "   macro avg       0.79      0.76      0.77       267\n",
      "weighted avg       0.79      0.79      0.78       267\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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