TITANIC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 11)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_chart(feature):\n",
    "    survived = train[train['Survived']==1][feature].value_counts()\n",
    "    dead = train[train['Survived']==0][feature].value_counts()\n",
    "    df = pd.DataFrame([survived,dead])\n",
    "    df.index = ['Survived' , 'Dead']\n",
    "    df.plot(kind = 'bar',stacked=True,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('SibSp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Parch')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Embarked')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <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>95</th>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Shorney, Mr. Charles Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>374910</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17754</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>A5</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Greenfield, Mr. William Bertram</td>\n",
       "      <td>male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17759</td>\n",
       "      <td>63.3583</td>\n",
       "      <td>D10 D12</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Doling, Mrs. John T (Ada Julia Bone)</td>\n",
       "      <td>female</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>231919</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Kantor, Mr. Sinai</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>244367</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "0             1         0       3   \n",
       "1             2         1       1   \n",
       "2             3         1       3   \n",
       "3             4         1       1   \n",
       "4             5         0       3   \n",
       "..          ...       ...     ...   \n",
       "95           96         0       3   \n",
       "96           97         0       1   \n",
       "97           98         1       1   \n",
       "98           99         1       2   \n",
       "99          100         0       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",
       "..                                                ...     ...   ...    ...   \n",
       "95                        Shorney, Mr. Charles Joseph    male   NaN      0   \n",
       "96                          Goldschmidt, Mr. George B    male  71.0      0   \n",
       "97                    Greenfield, Mr. William Bertram    male  23.0      0   \n",
       "98               Doling, Mrs. John T (Ada Julia Bone)  female  34.0      0   \n",
       "99                                  Kantor, Mr. Sinai    male  34.0      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",
       "..    ...               ...      ...      ...      ...  \n",
       "95      0            374910   8.0500      NaN        S  \n",
       "96      0          PC 17754  34.6542       A5        C  \n",
       "97      1          PC 17759  63.3583  D10 D12        C  \n",
       "98      1            231919  23.0000      NaN        S  \n",
       "99      0            244367  26.0000      NaN        S  \n",
       "\n",
       "[100 rows x 12 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test_data = [train,test]\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Name'].str.extract('([A-Za-z]+)\\.',expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          517\n",
       "Miss        182\n",
       "Mrs         125\n",
       "Master       40\n",
       "Dr            7\n",
       "Rev           6\n",
       "Major         2\n",
       "Mlle          2\n",
       "Col           2\n",
       "Jonkheer      1\n",
       "Sir           1\n",
       "Don           1\n",
       "Mme           1\n",
       "Lady          1\n",
       "Countess      1\n",
       "Capt          1\n",
       "Ms            1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        240\n",
       "Miss       78\n",
       "Mrs        72\n",
       "Master     21\n",
       "Col         2\n",
       "Rev         2\n",
       "Dona        1\n",
       "Dr          1\n",
       "Ms          1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2, \n",
    "                 \"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"Countess\": 3,\n",
    "                 \"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Dona\" : 3, \"Mme\": 3,\"Capt\": 3,\"Sir\": 3 }\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Embarked</th>\n",
       "      <th>Title</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",
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       "      <td>S</td>\n",
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       "    </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",
       "      <td>2</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",
       "      <td>1</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",
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       "    <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Title  \n",
       "0      0         A/5 21171   7.2500   NaN        S      0  \n",
       "1      0          PC 17599  71.2833   C85        C      2  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3      0            113803  53.1000  C123        S      2  \n",
       "4      0            373450   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  Title  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q      0  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S      2  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q      0  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S      0  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S      2  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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",
       "      <th>Title</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",
       "      <td>0</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",
       "      <td>2</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",
       "      <td>1</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",
       "      <td>2</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Title  \n",
       "0      0         A/5 21171   7.2500   NaN        S      0  \n",
       "1      0          PC 17599  71.2833   C85        C      2  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3      0            113803  53.1000  C123        S      2  \n",
       "4      0            373450   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  Title  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q      0  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S      2  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q      0  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S      0  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S      2  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex_mapping = {\"male\": 0, \"female\": 1}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Sex'] = dataset['Sex'].map(sex_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill missing age with median age for each title (Mr, Mrs, Miss, Others)\n",
    "train[\"Age\"].fillna(train.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)\n",
    "test[\"Age\"].fillna(test.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      30.0\n",
       "1      35.0\n",
       "2      21.0\n",
       "3      35.0\n",
       "4      30.0\n",
       "       ... \n",
       "886     9.0\n",
       "887    21.0\n",
       "888    21.0\n",
       "889    30.0\n",
       "890    30.0\n",
       "Name: Age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(30)\n",
    "train.groupby(\"Title\")[\"Age\"].transform(\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0,\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 26), 'Age'] = 1,\n",
    "    dataset.loc[(dataset['Age'] > 26) & (dataset['Age'] <= 36), 'Age'] = 2,\n",
    "    dataset.loc[(dataset['Age'] > 36) & (dataset['Age'] <= 62), 'Age'] = 3,\n",
    "    dataset.loc[ dataset['Age'] > 62, 'Age'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "    </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>1</td>\n",
       "      <td>3.0</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",
       "      <td>2</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>1</td>\n",
       "      <td>1.0</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",
       "      <td>1</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>1</td>\n",
       "      <td>2.0</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",
       "      <td>2</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>0</td>\n",
       "      <td>2.0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex  Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0  1.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  3.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1  1.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  2.0      1      0   \n",
       "4                           Allen, Mr. William Henry    0  2.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  Title  \n",
       "0         A/5 21171   7.2500   NaN        S      0  \n",
       "1          PC 17599  71.2833   C85        C      2  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3            113803  53.1000  C123        S      2  \n",
       "4            373450   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a4ea491588>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pclass1 = train[train['Pclass']==1]['Embarked'].value_counts()\n",
    "Pclass2 = train[train['Pclass']==2]['Embarked'].value_counts()\n",
    "Pclass3 = train[train['Pclass']==3]['Embarked'].value_counts()\n",
    "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n",
    "df.index = ['1st class','2nd class', '3rd class']\n",
    "df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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",
       "      <th>Title</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>0</td>\n",
       "      <td>1.0</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",
       "      <td>0</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>1</td>\n",
       "      <td>3.0</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",
       "      <td>2</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>1</td>\n",
       "      <td>1.0</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",
       "      <td>1</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>1</td>\n",
       "      <td>2.0</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",
       "      <td>2</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>0</td>\n",
       "      <td>2.0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex  Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0  1.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  3.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1  1.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  2.0      1      0   \n",
       "4                           Allen, Mr. William Henry    0  2.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  Title  \n",
       "0         A/5 21171   7.2500   NaN        S      0  \n",
       "1          PC 17599  71.2833   C85        C      2  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S      1  \n",
       "3            113803  53.1000  C123        S      2  \n",
       "4            373450   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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",
       "      <th>Title</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>0</td>\n",
       "      <td>1.0</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>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>3.0</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>1</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>1.0</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>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>G6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Uruchurtu, Don. Manuel E</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17601</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>B78</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Glynn, Miss. Mary Agatha</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>335677</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Wheadon, Mr. Edward H</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 24579</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Meyer, Mr. Edgar Joseph</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17604</td>\n",
       "      <td>82.1708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Holverson, Mr. Alexander Oskar</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113789</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mamee, Mr. Hanna</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2677</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Cann, Mr. Ernest Charles</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A./5. 2152</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Miss. Augusta Maria</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>345764</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Nicola-Yarred, Miss. Jamila</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2651</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ahlin, Mrs. Johan (Johanna Persdotter Larsson)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7546</td>\n",
       "      <td>9.4750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Turpin, Mrs. William John Robert (Dorothy Ann ...</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11668</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kraeff, Mr. Theodor</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349253</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Laroche, Miss. Simonne Marie Anne Andree</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>SC/Paris 2123</td>\n",
       "      <td>41.5792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Devaney, Miss. Margaret Delia</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330958</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rogers, Mr. William John</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.C./A.4. 23567</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Lennon, Mr. Denis</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>370371</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Driscoll, Miss. Bridget</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14311</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Samaan, Mr. Youssef</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2662</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Arnold-Franchi, Mrs. Josef (Josefine Franchi)</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>349237</td>\n",
       "      <td>17.8000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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",
       "10           11         1       3   \n",
       "11           12         1       1   \n",
       "12           13         0       3   \n",
       "13           14         0       3   \n",
       "14           15         0       3   \n",
       "15           16         1       2   \n",
       "16           17         0       3   \n",
       "17           18         1       2   \n",
       "18           19         0       3   \n",
       "19           20         1       3   \n",
       "20           21         0       2   \n",
       "21           22         1       2   \n",
       "22           23         1       3   \n",
       "23           24         1       1   \n",
       "24           25         0       3   \n",
       "25           26         1       3   \n",
       "26           27         0       3   \n",
       "27           28         0       1   \n",
       "28           29         1       3   \n",
       "29           30         0       3   \n",
       "30           31         0       1   \n",
       "31           32         1       1   \n",
       "32           33         1       3   \n",
       "33           34         0       2   \n",
       "34           35         0       1   \n",
       "35           36         0       1   \n",
       "36           37         1       3   \n",
       "37           38         0       3   \n",
       "38           39         0       3   \n",
       "39           40         1       3   \n",
       "40           41         0       3   \n",
       "41           42         0       2   \n",
       "42           43         0       3   \n",
       "43           44         1       2   \n",
       "44           45         1       3   \n",
       "45           46         0       3   \n",
       "46           47         0       3   \n",
       "47           48         1       3   \n",
       "48           49         0       3   \n",
       "49           50         0       3   \n",
       "\n",
       "                                                 Name  Sex  Age  SibSp  Parch  \\\n",
       "0                             Braund, Mr. Owen Harris    0  1.0      1      0   \n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...    1  3.0      1      0   \n",
       "2                              Heikkinen, Miss. Laina    1  1.0      0      0   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  2.0      1      0   \n",
       "4                            Allen, Mr. William Henry    0  2.0      0      0   \n",
       "5                                    Moran, Mr. James    0  2.0      0      0   \n",
       "6                             McCarthy, Mr. Timothy J    0  3.0      0      0   \n",
       "7                      Palsson, Master. Gosta Leonard    0  0.0      3      1   \n",
       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)    1  2.0      0      2   \n",
       "9                 Nasser, Mrs. Nicholas (Adele Achem)    1  0.0      1      0   \n",
       "10                    Sandstrom, Miss. Marguerite Rut    1  0.0      1      1   \n",
       "11                           Bonnell, Miss. Elizabeth    1  3.0      0      0   \n",
       "12                     Saundercock, Mr. William Henry    0  1.0      0      0   \n",
       "13                        Andersson, Mr. Anders Johan    0  3.0      1      5   \n",
       "14               Vestrom, Miss. Hulda Amanda Adolfina    1  0.0      0      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)     1  3.0      0      0   \n",
       "16                               Rice, Master. Eugene    0  0.0      4      1   \n",
       "17                       Williams, Mr. Charles Eugene    0  2.0      0      0   \n",
       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...    1  2.0      1      0   \n",
       "19                            Masselmani, Mrs. Fatima    1  2.0      0      0   \n",
       "20                               Fynney, Mr. Joseph J    0  2.0      0      0   \n",
       "21                              Beesley, Mr. Lawrence    0  2.0      0      0   \n",
       "22                        McGowan, Miss. Anna \"Annie\"    1  0.0      0      0   \n",
       "23                       Sloper, Mr. William Thompson    0  2.0      0      0   \n",
       "24                      Palsson, Miss. Torborg Danira    1  0.0      3      1   \n",
       "25  Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...    1  3.0      1      5   \n",
       "26                            Emir, Mr. Farred Chehab    0  2.0      0      0   \n",
       "27                     Fortune, Mr. Charles Alexander    0  1.0      3      2   \n",
       "28                      O'Dwyer, Miss. Ellen \"Nellie\"    1  1.0      0      0   \n",
       "29                                Todoroff, Mr. Lalio    0  2.0      0      0   \n",
       "30                           Uruchurtu, Don. Manuel E    0  3.0      0      0   \n",
       "31     Spencer, Mrs. William Augustus (Marie Eugenie)    1  2.0      1      0   \n",
       "32                           Glynn, Miss. Mary Agatha    1  1.0      0      0   \n",
       "33                              Wheadon, Mr. Edward H    0  4.0      0      0   \n",
       "34                            Meyer, Mr. Edgar Joseph    0  2.0      1      0   \n",
       "35                     Holverson, Mr. Alexander Oskar    0  3.0      1      0   \n",
       "36                                   Mamee, Mr. Hanna    0  2.0      0      0   \n",
       "37                           Cann, Mr. Ernest Charles    0  1.0      0      0   \n",
       "38                 Vander Planke, Miss. Augusta Maria    1  1.0      2      0   \n",
       "39                        Nicola-Yarred, Miss. Jamila    1  0.0      1      0   \n",
       "40     Ahlin, Mrs. Johan (Johanna Persdotter Larsson)    1  3.0      1      0   \n",
       "41  Turpin, Mrs. William John Robert (Dorothy Ann ...    1  2.0      1      0   \n",
       "42                                Kraeff, Mr. Theodor    0  2.0      0      0   \n",
       "43           Laroche, Miss. Simonne Marie Anne Andree    1  0.0      1      2   \n",
       "44                      Devaney, Miss. Margaret Delia    1  1.0      0      0   \n",
       "45                           Rogers, Mr. William John    0  2.0      0      0   \n",
       "46                                  Lennon, Mr. Denis    0  2.0      1      0   \n",
       "47                          O'Driscoll, Miss. Bridget    1  1.0      0      0   \n",
       "48                                Samaan, Mr. Youssef    0  2.0      2      0   \n",
       "49      Arnold-Franchi, Mrs. Josef (Josefine Franchi)    1  1.0      1      0   \n",
       "\n",
       "              Ticket      Fare        Cabin  Embarked  Title  \n",
       "0          A/5 21171    7.2500          NaN         0      0  \n",
       "1           PC 17599   71.2833          C85         1      2  \n",
       "2   STON/O2. 3101282    7.9250          NaN         0      1  \n",
       "3             113803   53.1000         C123         0      2  \n",
       "4             373450    8.0500          NaN         0      0  \n",
       "5             330877    8.4583          NaN         2      0  \n",
       "6              17463   51.8625          E46         0      0  \n",
       "7             349909   21.0750          NaN         0      3  \n",
       "8             347742   11.1333          NaN         0      2  \n",
       "9             237736   30.0708          NaN         1      2  \n",
       "10           PP 9549   16.7000           G6         0      1  \n",
       "11            113783   26.5500         C103         0      1  \n",
       "12         A/5. 2151    8.0500          NaN         0      0  \n",
       "13            347082   31.2750          NaN         0      0  \n",
       "14            350406    7.8542          NaN         0      1  \n",
       "15            248706   16.0000          NaN         0      2  \n",
       "16            382652   29.1250          NaN         2      3  \n",
       "17            244373   13.0000          NaN         0      0  \n",
       "18            345763   18.0000          NaN         0      2  \n",
       "19              2649    7.2250          NaN         1      2  \n",
       "20            239865   26.0000          NaN         0      0  \n",
       "21            248698   13.0000          D56         0      0  \n",
       "22            330923    8.0292          NaN         2      1  \n",
       "23            113788   35.5000           A6         0      0  \n",
       "24            349909   21.0750          NaN         0      1  \n",
       "25            347077   31.3875          NaN         0      2  \n",
       "26              2631    7.2250          NaN         1      0  \n",
       "27             19950  263.0000  C23 C25 C27         0      0  \n",
       "28            330959    7.8792          NaN         2      1  \n",
       "29            349216    7.8958          NaN         0      0  \n",
       "30          PC 17601   27.7208          NaN         1      3  \n",
       "31          PC 17569  146.5208          B78         1      2  \n",
       "32            335677    7.7500          NaN         2      1  \n",
       "33        C.A. 24579   10.5000          NaN         0      0  \n",
       "34          PC 17604   82.1708          NaN         1      0  \n",
       "35            113789   52.0000          NaN         0      0  \n",
       "36              2677    7.2292          NaN         1      0  \n",
       "37        A./5. 2152    8.0500          NaN         0      0  \n",
       "38            345764   18.0000          NaN         0      1  \n",
       "39              2651   11.2417          NaN         1      1  \n",
       "40              7546    9.4750          NaN         0      2  \n",
       "41             11668   21.0000          NaN         0      2  \n",
       "42            349253    7.8958          NaN         1      0  \n",
       "43     SC/Paris 2123   41.5792          NaN         1      1  \n",
       "44            330958    7.8792          NaN         2      1  \n",
       "45   S.C./A.4. 23567    8.0500          NaN         0      0  \n",
       "46            370371   15.5000          NaN         2      0  \n",
       "47             14311    7.7500          NaN         2      1  \n",
       "48              2662   21.6792          NaN         1      0  \n",
       "49            349237   17.8000          NaN         0      2  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill missing Fare with median fare for each Pclass\n",
    "train[\"Fare\"].fillna(train.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
    "test[\"Fare\"].fillna(test.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
    "train.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset.loc[ dataset['Fare'] <= 17, 'Fare'] = 0,\n",
    "    dataset.loc[(dataset['Fare'] > 17) & (dataset['Fare'] <= 30), 'Fare'] = 1,\n",
    "    dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 100), 'Fare'] = 2,\n",
    "    dataset.loc[ dataset['Fare'] > 100, 'Fare'] = 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Title</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>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</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>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex  Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0  1.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  3.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1  1.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  2.0      1      0   \n",
       "4                           Allen, Mr. William Henry    0  2.0      0      0   \n",
       "\n",
       "             Ticket  Fare Cabin  Embarked  Title  \n",
       "0         A/5 21171   0.0   NaN         0      0  \n",
       "1          PC 17599   2.0   C85         1      2  \n",
       "2  STON/O2. 3101282   0.0   NaN         0      1  \n",
       "3            113803   2.0  C123         0      2  \n",
       "4            373450   0.0   NaN         0      0  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C23 C25 C27    4\n",
       "B96 B98        4\n",
       "G6             4\n",
       "D              3\n",
       "E101           3\n",
       "              ..\n",
       "E50            1\n",
       "C106           1\n",
       "C103           1\n",
       "B82 B84        1\n",
       "C99            1\n",
       "Name: Cabin, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Cabin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].str[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Name', axis=1, inplace=True)\n",
    "test.drop('Name', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</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>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "   Fare Cabin  Embarked  Title  \n",
       "0   0.0   NaN         0      0  \n",
       "1   2.0     C         1      2  \n",
       "2   0.0   NaN         0      1  \n",
       "3   2.0     C         0      2  \n",
       "4   0.0   NaN         0      0  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a4ea4ffd88>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AMIyCBQAAYBi3+AEAgJDzlJVpwlMv18i4tQEFCwAAhNzp7z2SLr1eVU1r2fIqrVq1vkbG5hIhAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGMZdhAAAIOSaNGool9t8DfF5fDp5usT4uJVFwQIAACHncrv01by3jY/b9uHfXPYzR47ka9iwu9S6dZsLtv/5z0+refMWRnJQsAAAgO00axarxYuX1dj4zMECAAAwjDNYAADAdo4dK9SoUcODr5OT+2r48JHGxqdgAQAA2+ESIQAAQJihYAEAABjGJUIAABByPo+vQksqVGXc2oCCBQAAQs7KxUBbtrxKq1atr9F9cIkQAADAMAoWAACAYRQsAAAAw5iDZYjH59HKe563OoateHweqyMAAHBRFCxD3C63vpk12OoYttJm+uuSyqyOAQDAT3CJEAAAwDDOYAEAgJBr1KiB3O76xsf1eLw6fbrU+LiVRcECAAAh53bXV05OjvFxJ0+eLOnyBcvn82np0v/Wli0b5XA4VF5ern79+is9fbQcDke1c1CwAACA7eTk/FknTx7XCy+8rOjoaBUXFykzc4oiI6M0eHBatcev0BysoqIi9e/fX4cOHZIk7d69W6mpqUpOTtYzzzwT/Nznn3+uu+66SykpKZo+fbp8vtqxXD0AAMAPjh4t0JYtG5SZ+biio6MlSZGRUfrjHx9RTEyMkX1ctmDt27dPw4YNU15eniSptLRUmZmZeu6557Rhwwbt379fu3btkiRNmTJFM2bM0ObNmxUIBLRy5UojIQEAAEz5/PN/qnXrNrryyisv2N6qVWvdfPOtRvZx2YK1cuVKZWdnKy4uTpL06aefqlWrVkpISJDL5VJqaqo2bdqkw4cPq7S0VL/+9a8lSXfddZc2bdpkJCQAAIBJP55ntXPnNo0aNVwjR96jsWNHGhn/snOwZs2adcHro0ePKjY2Nvg6Li5OBQUFP9keGxurgoKCSgeKiYmq9HdgX7Gx0VZHQJjgWIEd1Nbj/OhRp1yu0K0Mdbl9/epXv1Je3jcqKzuryMgoJSUlKykpWfn5+fr973970e87nc5K/XwrPcnd7/df0PoCgYAcDsfPbq+s48eL5PcHKv09q9XWg7quKyw8Y3UEhIHY2GiOFdR5tfk49/v98vn8Idvf5fbVrFlzpaTcrscfn6HMzGxFR0fL5/Ppf/5nl5xO50W/7/f7L/j5Op2OS54UqnTBatGihQoLC4OvCwsLFRcX95Ptx44dC15WBAAA+DGPx3t+SQXz41bE5MmPavnypXrooXHy+/06e/asOnfuonnznjWSo9IFq1OnTvr222/13Xff6Re/+IXefPNNDR48WPHx8YqIiNDevXvVpUsXrV27Vr169TISEgAA1C3nFgO1bkFQp9Op4cPTNXx4eo2MX+mCFRERodmzZ2vixIkqKytT79691bdvX0nSvHnzlJWVpaKiIv3qV7/SyJFmJooBAACEkwoXrB07dgT/3LNnT61bt+4nn2nfvr1WrVplJhkAAECY4mHPAACghjkUCIRukrtp57JX7sY9ChYAAKhRbncDnTp1TD6fV4FA+KwUEAgE5PN5derUMbndDSr1XZ5FCAAAalSTJrEqKjqtEycK5PeXWx2nUpzOemrYMEpRUY0q9T0KFgAAqFEOh0PR0Y0VHd3Y6ighwyVCAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYa6qfvG1117TkiVLgq8PHTqkAQMGqKSkRHv37lXDhg0lSRMmTFBSUlL1kwIAAISJKhesu+++W3fffbck6auvvtKDDz6oCRMm6L777tOSJUsUFxdnLCQAAEA4MXKJ8PHHH1dGRoYaNmyo/Px8ZWZmKjU1Vc8++6z8fr+JXQAAAISNKp/B+sHu3btVWlqqfv366eDBg+rRo4eys7MVHR2tcePGadWqVUpLS6vweDExUdWNBBuJjY22OgLCBMcK7IDjvPaodsFavny5Ro8eLUlKSEjQggULgu+lp6drzZo1lSpYx48Xye8PVDdWyHFQW6Ow8IzVERAGYmOjOVZQ53Gch5bT6bjkSaFqXSL0eDz68MMP1adPH0lSbm6uNm/eHHw/EAjI5ap2hwMAAAgr1SpYubm5at26ta644gpJ5wrVk08+qdOnT8vr9WrFihXcQQgAAGynWqeXDh48qBYtWgRft2/fXg888ICGDRsmn8+n5ORk9e/fv9ohAQAAwokjEAjUqglP4TwH65tZg62OYSttpr/OfANUCHNTYAcc56FVo3OwAAAA8FMULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYS6rA9QVfq9Hbaa/bnUMW/F7PVZHAADgoihYhjjru5U6ea3VMWxlfc4ASWVWxwAA4Ce4RAgAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwrFoLjaanp+vEiRNyuc4NM3PmTB04cEDPP/+8fD6f7rvvPo0YMcJIUAAAgHBR5YIVCASUl5ennTt3BgtWQUGBMjIytHr1arndbg0dOlTdu3fXtddeaywwAABAbVflgvXNN99IksaMGaNTp04pLS1NkZGR6tGjhxo3bixJSklJ0aZNmzRhwgQzaQEAAMJAledgff/99+rZs6cWLFigxYsXa/ny5crPz1dsbGzwM3FxcSooKDASFAAAIFxU+QxW586d1blz5+DrIUOG6KmnntL48eOD2wKBgBwOR6XGjYmJqmok2FBsbLTVERAmOFZgBxzntUeVC9aePXvk9XrVs2dPSefKVHx8vAoLC4OfKSwsVFxcXKXGPX68SH5/oKqxLMNBbY3CwjNWR0AYiI2N5lhBncdxHlpOp+OSJ4WqfInwzJkzmjNnjsrKylRUVKQ33nhDc+fO1bvvvqsTJ06opKREW7ZsUa9evaq6CwAAgLBU5TNYt9xyi/bt26eBAwfK7/dr+PDh6tKlizIyMjRy5Eh5vV4NGTJEHTt2NJkXAACg1nMEAoFadT0unC8Rpk5ea3UMW1mfM4DT4agQLp3ADjjOQ6vGLhECAADg4ihYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGOaqzpfnz5+vjRs3SpJ69+6tqVOnatq0adq7d68aNmwoSZowYYKSkpKqnxQAACBMVLlg7d69W2+//bbeeOMNORwOjR07Vlu3btX+/fu1ZMkSxcXFmcwJAAAQNqp8iTA2NlaPPvqo3G636tevr2uuuUb5+fnKz89XZmamUlNT9eyzz8rv95vMCwAAUOtV+QxW27Ztg3/Oy8vTxo0btXTpUn3wwQfKzs5WdHS0xo0bp1WrViktLa3C48bERFU1EmwoNjba6ggIExwrsAOO89qjWnOwJOmrr77SuHHjNHXqVLVp00YLFiwIvpeenq41a9ZUqmAdP14kvz9Q3Vghx0FtjcLCM1ZHQBiIjY3mWEGdx3EeWk6n45Inhap1F+HevXs1atQoTZ48WYMGDVJubq42b94cfD8QCMjlqnaHAwAACCtVLlhHjhzRgw8+qHnz5umOO+6QdK5QPfnkkzp9+rS8Xq9WrFjBHYQAAMB2qnx6adGiRSorK9Ps2bOD24YOHaoHHnhAw4YNk8/nU3Jysvr3728kKAAAQLhwBAKBWjXhKZznYKVOXmt1DFtZnzOA+QaoEOamwA44zkOrRudgAQAA4KcoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhPCgQQIU1adRQLnd4/rURrg9k93l8Onm6xOoYACopPP+mBGAJl9ulr+a9bXUMW2n78G+sjgCgCrhECAAAYBgFCwAAwDAKFgAAgGHMwQIA4EcaNWogt7u+1TGqJFxv5vB4vDp9utTqGEZRsAAA+BG3u75ycnKsjmErkydPllS3ChaXCAEAAAyjYAEAABhGwQIAADCMggUAAGAYk9wBVJjfW87K4iHm95ZbHcF2fF7f+UnXCBWf12d1BOMoWAAqzFm/nt4ZMNjqGLZy49rXrY5gO676PBIq1OriL25cIgQAADCMggUAAGAYBQsAAMAwChYAAIBhTHIHUGHlZR4mXYdYeZnH6ggAqoCCBaDC6kW4lbZivNUxbGXlPc9LKrM6BoBKomABAPAjrPcWenVxvbcaKVjr16/X888/L5/Pp/vuu08jRoyoid0AAGAc672FXl2cemC8YBUUFOiZZ57R6tWr5Xa7NXToUHXv3l3XXnut6V0BAADUSsYL1u7du9WjRw81btxYkpSSkqJNmzZpwoQJFfq+0+kwHSlk4po0tDqC7YTz8RKuYq9oanUE2+E4D72IuFirI9hOuB3nl8trvGAdPXpUsbH/e2DGxcXp008/rfD3mzSJNB0pZBZlJVsdwXZiYqKsjmA7C1JnWR3BdjjOQ6/rSy9YHcF26tpxbnwdLL/fL4fjf1tdIBC44DUAAEBdZ7xgtWjRQoWFhcHXhYWFiouLM70bAACAWst4wbrhhhv07rvv6sSJEyopKdGWLVvUq1cv07sBAACotYzPwWrevLkyMjI0cuRIeb1eDRkyRB07djS9GwAAgFrLEQgEAlaHAAAAqEt42DMAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsGyuaKiIqsjAACq6OjRo5KkPXv2aOnSpSotLbU4EX5AwbKZnTt3au7cuSouLla/fv106623avXq1VbHAow6cOCA1q1bp0AgoMcee0yDBw/WZ599ZnUswKjs7Gz913/9l/71r39p8uTJ+uc//6msrCyrY+E8CpbNzJ8/X6mpqdqwYYM6duyoHTt2aMmSJVbHAoyaNm2a/H6/tm/frry8PE2bNk2zZvGQatQtn332mWbNmqWNGzdqyJAhevLJJ/Xtt99aHQvnUbBsqH379nrrrbfUp08fRUZGyuv1Wh0JMKqsrEwDBw7Uzp07lZqaqq5du8rj8VgdCzCqvLw8+ItEr169VFJSopKSEqtj4TwKls00a9ZMTzzxhPbv36+bbrpJs2fP1lVXXWV1LMCoevXqafPmzXrrrbd08803a9u2bXI6+esOdcvAgQP1m9/8RvHx8erUqZMGDx6se+65x+pYOI9H5dhMUVGRtm3bps6dO6tVq1ZaunSpBgwYoKioKKujAcbk5uZq8eLFuvnmm5WSkqKMjAyNGzdO7du3tzoaYJTf7w/+8nDy5Ek1adLE4kT4Ab/S2YzP51NcXJxatWqlhQsX6v3339eJEyesjgUY1a5dO2VkZCglJUV79uxR165d1bp1a6tjAUbt3LlTOTk5wZuW+vbty01LtQgFy2YmT56szz//XLt379amTZvUp08fTZ8+3epYgFHcXQU74Kal2o2CZTOnT5/W/fffr+3bt2vQoEEaOHCgiouLrY4FGMXdVbALblqqvShYNuP3+7V//35t27ZNt9xyiz7//HOVl5dbHQswirurYAfctFS7uawOgNCaMmWK5syZo9GjRyshIUFpaWmaNm2a1bEAo364uyoxMVGdOnXS7bffrrS0NKtjAUbl5ORo27ZtGjlypK644golJCRo4sSJVsfCedxFaHN+v1+HDx9WQkKC1VEAo358d9WJEyfUtGlTixMBZnk8Hu3atSs4zaO8vFyHDh3SpEmTLE4GiTNYtrN8+XLNmTPngssl8fHx2rZtm4WpALM++eQTLVy4UGfPngTh9RoAAAhkSURBVFUgEJDf71d+fr527NhhdTTAmD/+8Y86ffq0Dhw4oK5du+r9999XYmKi1bFwHnOwbObFF1/U2rVrdfvtt2vr1q3KyspSp06drI4FGJWZmanbbrtN5eXlGjFihJo3b67bbrvN6liAUbm5uXrllVeUlJSksWPH6u9//7sOHz5sdSycR8GymZiYGCUkJKhdu3b68ssvNWLECOXm5lodCzDK7XZr8ODBuv7663XllVdqzpw5evvtt62OBRgVExMjh8Ohq6++Wrm5uUpISOAuwlqEgmUzDRs21Hvvvad27dpp586dKiwsVGlpqdWxAKMiIiJ06tQpXX311dq3b5/q1avH3bKoc9q2basnnnhC3bt31+LFi/Xiiy+KadW1BwXLZrKysrRjxw7ddNNNOnXqlPr27at7773X6liAUaNGjVJGRoZuueUWrV27VnfccYf+/d//3epYgFGPP/64+vXrp2uvvVYPPfSQjh49qpycHKtj4TzuIgRQJwUCATkcDp09e1Z5eXnq0KGDHA6H1bGAavvwww8v+X63bt1ClASXQsGyiT59+lzyH5ft27eHMA1QMy63pttTTz0VoiRAzUlPT//Z9xwOh1555ZUQpsHPoWDZxOXuLImPjw9REqDmvPHGG5d8f9CgQSFKAoTG8ePHFRMTo5KSEh09elStWrWyOhLOYw6WTcTHxys+Pl7FxcWaN2+e4uPjVVpaqqlTp6qsrMzqeIARgwYN0qBBg5SUlKSzZ89q0KBBuuGGG3TgwAH17dvX6niAUa+++qrGjh0r6dxiur/73e+0YsUKi1PhBxQsm8nKytLAgQMlSddcc41+//vfa/r06RanAsx6+OGHdfToUUlSZGSk/H6/pk6danEqwKwVK1Zo6dKlks79Er169WotWbLE4lT4AQXLZkpKStS7d+/g6xtvvJGH4KLOyc/PV0ZGhiQpKipKGRkZOnDggMWpALO8Xq/cbnfwdf369S1Mg/+LR+XYTNOmTfX3v/9dd955pyRpw4YNiomJsTgVYJbD4VBubq7atWsnSfr666/lcvHXHeqW2267Tffdd5/69esnh8OhzZs369Zbb7U6Fs5jkrvN5Ofn6z//8z/1wQcfyO12q2vXrnrsscfUokULq6MBxuzevVtTpkxR8+bNJUknT57U3Llz1bVrV4uTAWZt2rRJH374oVwul7p168YjoWoRChaAOsnj8ejLL7+Uy+VSmzZtLriUAgA1jYIFAABgGJPcAQAADGPWp8288847uvHGGy/YtmXLFiUnJ1uUCABQGTwqJzxwidAmNmzYII/Ho2effVYPPfRQcLvX69WLL76orVu3WpgOMINH5cAOfnhUzqlTp3TgwAElJibK6XTq448/1nXXXafly5dbnBASZ7Bso7i4WB999JGKi4v1/vvvB7fXq1cvuF4QEO6uv/56SdLOnTtVXFysO++8Uy6XSxs2bFB0dLTF6QAzXn31VUnSb3/7W82fPz/4eJzDhw9rxowZVkbDj3AGy2beffdd9ezZM/i6qKhIUVFRFiYCzLv77ru1YsUKOZ3nppn6/X6lpaVp1apVFicDzLnjjjv0j3/8I/g6EAjo9ttv18aNGy1MhR8wyd1mSkpKNHfuXBUXF6tfv3669dZbtXr1aqtjAUadOXNGp06dCr4+duyYzp49a2EiwLxf/vKXeuSRR/TWW29p586dmjx5Mmu91SKcwbKZwYMHa9asWfrss8+0Z88ezZgxQ+np6ZQs1Clr1qzRvHnzlJiYqEAgoE8++URZWVlKSUmxOhpgjMfj0ZIlS/TBBx9Ikm644QYNHz6cpxbUEvxfsKH27dvrr3/9q+68805FRkbK6/VaHQkwauDAgbrhhhv08ccfy+Fw6PHHH+eRUKhzxo8fr0WLFmnMmDFWR8FFULBsplmzZnriiSe0f/9+zZ07V7Nnz9ZVV11ldSzAqO+//15bt27VqVOnFAgE9OWXX0qSJkyYYHEywJySkhIdOXJELVu2tDoKLoKCZTM5OTnatm2bRo4cqSuuuEIJCQn8o4M6Z9KkSYqOjlbbtm3lcDisjgPUiJMnT6pPnz6KiYlRRESEAoGAHA6Htm/fbnU0iDlYAOqg1NRUrV+/3uoYQI06fPjwRbfHx8eHOAkuhjNYNpGfn3/J97lMiLqkQ4cO+uKLL9S+fXurowA1Ii8vTw0bNlTz5s312muvKTc3V126dFG/fv2sjobzOINlE6mpqcrLy1NcXJz+7/9yTimjrhk0aJC++OILLp2gTlq8eLFeffVV+f1+9ejRQ0eOHFFSUpJ27NihxMREPfjgg1ZHhChYtlFUVKThw4crOztbXbp0sToOUKO4dIK6LDU1VatWrdKxY8fUv39/vffee4qIiJDH49GQIUO0bt06qyNCLDRqG1FRUfrTn/6kNWvWWB0FqFHbtm3Tjh075Pf7FR8fH/zv7bfftjoaYITf75fb7VZ8fLzGjBmjiIiI4Hvl5eUWJsOPUbBspGPHjnriiSesjgHUmHnz5mnJkiXKy8vT0KFDtXbt2uB7PAAXdUVycrLuvfdelZeXa+LEiZKkL774QsOHD2cOVi3CJHcAdcauXbv0xhtvyOVyKT09XWPGjJHb7Va/fv1+MvcQCFeTJk3Shx9+qHr16gW3ud1uTZw4Ub1797YwGX6MggWgzvhhMrsktW7dWgsXLtTo0aPVtGlT1sNCndKtW7cLXrdp00Zt2rSxKA0uhkuEAOqMvn37Kj09XZ9++qkkqW3btvrLX/6iP/zhDzpw4IDF6QDYCXcRAqhT3n33XcXFxemaa64Jbjty5Ij+9re/afr06RYmA2AnFCwAAADDuEQIAABgGAULAADAMAoWAACAYRQsAAAAw/4/ZvkIJ/4CZtcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pclass1 = train[train['Pclass']==1]['Cabin'].value_counts()\n",
    "Pclass2 = train[train['Pclass']==2]['Cabin'].value_counts()\n",
    "Pclass3 = train[train['Pclass']==3]['Cabin'].value_counts()\n",
    "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n",
    "df.index = ['1st class','2nd class', '3rd class']\n",
    "df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "cabin_mapping = {\"A\": 0, \"B\": 0.4, \"C\": 0.8, \"D\": 1.2, \"E\": 1.6, \"F\": 2, \"G\": 2.4, \"T\": 2.8}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].map(cabin_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# fill missing Fare with median fare for each Pclass\n",
    "train[\"Cabin\"].fillna(train.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)\n",
    "test[\"Cabin\"].fillna(test.groupby(\"Pclass\")[\"Cabin\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"FamilySize\"] = train[\"SibSp\"] + train[\"Parch\"] + 1\n",
    "test[\"FamilySize\"] = test[\"SibSp\"] + test[\"Parch\"] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 11.0)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 912.425x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'FamilySize',shade= True)\n",
    "facet.set(xlim=(0, train['FamilySize'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlim(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "family_mapping = {1: 0, 2: 0.4, 3: 0.8, 4: 1.2, 5: 1.6, 6: 2, 7: 2.4, 8: 2.8, 9: 3.2, 10: 3.6, 11: 4}\n",
    "for dataset in train_test_data:\n",
    "    dataset['FamilySize'] = dataset['FamilySize'].map(family_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</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>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  1.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  3.0      1      0          PC 17599   \n",
       "2            3         1       3    1  1.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  2.0      1      0            113803   \n",
       "4            5         0       3    0  2.0      0      0            373450   \n",
       "\n",
       "   Fare  Cabin  Embarked  Title  FamilySize  \n",
       "0   0.0    2.0         0      0         0.4  \n",
       "1   2.0    0.8         1      2         0.4  \n",
       "2   0.0    2.0         0      1         0.0  \n",
       "3   2.0    0.8         0      2         0.4  \n",
       "4   0.0    2.0         0      0         0.0  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_drop = ['Ticket', 'SibSp', 'Parch']\n",
    "train = train.drop(features_drop, axis=1)\n",
    "test = test.drop(features_drop, axis=1)\n",
    "train = train.drop(['PassengerId'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,))"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = train.drop('Survived', axis=1)\n",
    "target = train['Survived']\n",
    "\n",
    "train_data.shape, target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  Fare  Cabin  Embarked  Title  FamilySize\n",
       "0       3    0  1.0   0.0    2.0         0      0         0.4\n",
       "1       1    1  3.0   2.0    0.8         1      2         0.4\n",
       "2       3    1  1.0   0.0    2.0         0      1         0.0\n",
       "3       1    1  2.0   2.0    0.8         0      2         0.4\n",
       "4       3    0  2.0   0.0    2.0         0      0         0.0\n",
       "5       3    0  2.0   0.0    2.0         2      0         0.0\n",
       "6       1    0  3.0   2.0    1.6         0      0         0.0\n",
       "7       3    0  0.0   1.0    2.0         0      3         1.6\n",
       "8       3    1  2.0   0.0    2.0         0      2         0.8\n",
       "9       2    1  0.0   2.0    1.8         1      2         0.4"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Survived    891 non-null    int64  \n",
      " 1   Pclass      891 non-null    int64  \n",
      " 2   Sex         891 non-null    int64  \n",
      " 3   Age         891 non-null    float64\n",
      " 4   Fare        891 non-null    float64\n",
      " 5   Cabin       891 non-null    float64\n",
      " 6   Embarked    891 non-null    int64  \n",
      " 7   Title       891 non-null    int64  \n",
      " 8   FamilySize  891 non-null    float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 62.8 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.82222222 0.76404494 0.80898876 0.83146067 0.87640449 0.82022472\n",
      " 0.85393258 0.79775281 0.84269663 0.84269663]\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_neighbors = 13)\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.6"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.6"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.6"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.76666667 0.80898876 0.78651685 0.7752809  0.88764045 0.76404494\n",
      " 0.83146067 0.82022472 0.74157303 0.78651685]\n"
     ]
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.69"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.78888889 0.80898876 0.79775281 0.76404494 0.85393258 0.7752809\n",
      " 0.79775281 0.80898876 0.76404494 0.83146067]\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=13)\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.85555556 0.73033708 0.75280899 0.75280899 0.70786517 0.80898876\n",
      " 0.76404494 0.80898876 0.86516854 0.83146067]\n"
     ]
    }
   ],
   "source": [
    "clf = GaussianNB()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.78"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.83333333 0.79775281 0.83146067 0.82022472 0.84269663 0.82022472\n",
      " 0.84269663 0.85393258 0.84269663 0.86516854]\n"
     ]
    }
   ],
   "source": [
    "clf = SVC()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.5"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(score)*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = SVC()\n",
    "clf.fit(train_data, target)\n",
    "\n",
    "test_data = test.drop(\"PassengerId\", axis=1).copy()\n",
    "prediction = clf.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test[\"PassengerId\"],\n",
    "        \"Survived\": prediction\n",
    "    })\n",
    "\n",
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv('submission.csv')\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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