python titanic project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab7d4797",
   "metadata": {},
   "source": [
    "# Titanic: Machine Learning from Disaster\n",
    "### Predict survival on the Titanic\n",
    "- Defining the problem statement\n",
    "- Collecting the data\n",
    "- Exploratory data analysis\n",
    "- Feature engineering\n",
    "- Modelling\n",
    "- Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24a7361c",
   "metadata": {},
   "source": [
    "## 1. Defining the problem statement\n",
    "Complete the analysis of what sorts of people were likely to survive.  \n",
    "In particular, we ask you to apply the tools of machine learning to predict which passengers survived the Titanic tragedy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3e930f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"titanic-disaster.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url= \"titanic-disaster.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae02a236",
   "metadata": {},
   "source": [
    "## 2. Collecting the data\n",
    "\n",
    "training data set and testing data set are given by Kaggle\n",
    "you can download from kaggle directly [kaggle](https://www.kaggle.com/c/titanic/data)  \n",
    "\n",
    "### load train, test dataset using Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e16b092e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train=pd.read_csv('train.csv')\n",
    "test=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8ebe268a",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
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       "      <td>A/5 21171</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <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",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
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       "      <td>S</td>\n",
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       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
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       "      <td>S</td>\n",
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       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
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       "      <td>S</td>\n",
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       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
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       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "983f5cdb",
   "metadata": {},
   "outputs": [
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       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
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       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
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       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>1306</td>\n",
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       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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      ],
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       "     PassengerId  Pclass                                          Name  \\\n",
       "0            892       3                              Kelly, Mr. James   \n",
       "1            893       3              Wilkes, Mrs. James (Ellen Needs)   \n",
       "2            894       2                     Myles, Mr. Thomas Francis   \n",
       "3            895       3                              Wirz, Mr. Albert   \n",
       "4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "..           ...     ...                                           ...   \n",
       "413         1305       3                            Spector, Mr. Woolf   \n",
       "414         1306       1                  Oliva y Ocana, Dona. Fermina   \n",
       "415         1307       3                  Saether, Mr. Simon Sivertsen   \n",
       "416         1308       3                           Ware, Mr. Frederick   \n",
       "417         1309       3                      Peter, Master. Michael J   \n",
       "\n",
       "        Sex   Age  SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
       "0      male  34.5      0      0              330911    7.8292   NaN        Q  \n",
       "1    female  47.0      1      0              363272    7.0000   NaN        S  \n",
       "2      male  62.0      0      0              240276    9.6875   NaN        Q  \n",
       "3      male  27.0      0      0              315154    8.6625   NaN        S  \n",
       "4    female  22.0      1      1             3101298   12.2875   NaN        S  \n",
       "..      ...   ...    ...    ...                 ...       ...   ...      ...  \n",
       "413    male   NaN      0      0           A.5. 3236    8.0500   NaN        S  \n",
       "414  female  39.0      0      0            PC 17758  108.9000  C105        C  \n",
       "415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "416    male   NaN      0      0              359309    8.0500   NaN        S  \n",
       "417    male   NaN      1      1                2668   22.3583   NaN        C  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fcf6ad2",
   "metadata": {},
   "source": [
    "## 3. Exploratory data analysis\n",
    "Printing first 5 rows of the train dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d6ca43d3",
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       "   PassengerId  Survived  Pclass  \\\n",
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       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
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       "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",
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "033cb39e",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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>male</td>\n",
       "      <td>19.0</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>S</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>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>male</td>\n",
       "      <td>40.0</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>C</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>female</td>\n",
       "      <td>NaN</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>C</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>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>335677</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>male</td>\n",
       "      <td>66.0</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>S</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>male</td>\n",
       "      <td>28.0</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>C</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>male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113789</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2677</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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>male</td>\n",
       "      <td>21.0</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>S</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>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>345764</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2651</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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>female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7546</td>\n",
       "      <td>9.4750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11668</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349253</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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>female</td>\n",
       "      <td>3.0</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>C</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>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330958</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>male</td>\n",
       "      <td>NaN</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>S</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>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>370371</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14311</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2662</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>349237</td>\n",
       "      <td>17.8000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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",
       "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  \\\n",
       "0                             Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                              Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                            Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                    Moran, Mr. James    male   NaN      0   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                      Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                 Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                     Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                        Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14               Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                               Rice, Master. Eugene    male   2.0      4   \n",
       "17                       Williams, Mr. Charles Eugene    male   NaN      0   \n",
       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                            Masselmani, Mrs. Fatima  female   NaN      0   \n",
       "20                               Fynney, Mr. Joseph J    male  35.0      0   \n",
       "21                              Beesley, Mr. Lawrence    male  34.0      0   \n",
       "22                        McGowan, Miss. Anna \"Annie\"  female  15.0      0   \n",
       "23                       Sloper, Mr. William Thompson    male  28.0      0   \n",
       "24                      Palsson, Miss. Torborg Danira  female   8.0      3   \n",
       "25  Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.0      1   \n",
       "26                            Emir, Mr. Farred Chehab    male   NaN      0   \n",
       "27                     Fortune, Mr. Charles Alexander    male  19.0      3   \n",
       "28                      O'Dwyer, Miss. Ellen \"Nellie\"  female   NaN      0   \n",
       "29                                Todoroff, Mr. Lalio    male   NaN      0   \n",
       "30                           Uruchurtu, Don. Manuel E    male  40.0      0   \n",
       "31     Spencer, Mrs. William Augustus (Marie Eugenie)  female   NaN      1   \n",
       "32                           Glynn, Miss. Mary Agatha  female   NaN      0   \n",
       "33                              Wheadon, Mr. Edward H    male  66.0      0   \n",
       "34                            Meyer, Mr. Edgar Joseph    male  28.0      1   \n",
       "35                     Holverson, Mr. Alexander Oskar    male  42.0      1   \n",
       "36                                   Mamee, Mr. Hanna    male   NaN      0   \n",
       "37                           Cann, Mr. Ernest Charles    male  21.0      0   \n",
       "38                 Vander Planke, Miss. Augusta Maria  female  18.0      2   \n",
       "39                        Nicola-Yarred, Miss. Jamila  female  14.0      1   \n",
       "40     Ahlin, Mrs. Johan (Johanna Persdotter Larsson)  female  40.0      1   \n",
       "41  Turpin, Mrs. William John Robert (Dorothy Ann ...  female  27.0      1   \n",
       "42                                Kraeff, Mr. Theodor    male   NaN      0   \n",
       "43           Laroche, Miss. Simonne Marie Anne Andree  female   3.0      1   \n",
       "44                      Devaney, Miss. Margaret Delia  female  19.0      0   \n",
       "45                           Rogers, Mr. William John    male   NaN      0   \n",
       "46                                  Lennon, Mr. Denis    male   NaN      1   \n",
       "47                          O'Driscoll, Miss. Bridget  female   NaN      0   \n",
       "48                                Samaan, Mr. Youssef    male   NaN      2   \n",
       "49      Arnold-Franchi, Mrs. Josef (Josefine Franchi)  female  18.0      1   \n",
       "\n",
       "    Parch            Ticket      Fare        Cabin Embarked  \n",
       "0       0         A/5 21171    7.2500          NaN        S  \n",
       "1       0          PC 17599   71.2833          C85        C  \n",
       "2       0  STON/O2. 3101282    7.9250          NaN        S  \n",
       "3       0            113803   53.1000         C123        S  \n",
       "4       0            373450    8.0500          NaN        S  \n",
       "5       0            330877    8.4583          NaN        Q  \n",
       "6       0             17463   51.8625          E46        S  \n",
       "7       1            349909   21.0750          NaN        S  \n",
       "8       2            347742   11.1333          NaN        S  \n",
       "9       0            237736   30.0708          NaN        C  \n",
       "10      1           PP 9549   16.7000           G6        S  \n",
       "11      0            113783   26.5500         C103        S  \n",
       "12      0         A/5. 2151    8.0500          NaN        S  \n",
       "13      5            347082   31.2750          NaN        S  \n",
       "14      0            350406    7.8542          NaN        S  \n",
       "15      0            248706   16.0000          NaN        S  \n",
       "16      1            382652   29.1250          NaN        Q  \n",
       "17      0            244373   13.0000          NaN        S  \n",
       "18      0            345763   18.0000          NaN        S  \n",
       "19      0              2649    7.2250          NaN        C  \n",
       "20      0            239865   26.0000          NaN        S  \n",
       "21      0            248698   13.0000          D56        S  \n",
       "22      0            330923    8.0292          NaN        Q  \n",
       "23      0            113788   35.5000           A6        S  \n",
       "24      1            349909   21.0750          NaN        S  \n",
       "25      5            347077   31.3875          NaN        S  \n",
       "26      0              2631    7.2250          NaN        C  \n",
       "27      2             19950  263.0000  C23 C25 C27        S  \n",
       "28      0            330959    7.8792          NaN        Q  \n",
       "29      0            349216    7.8958          NaN        S  \n",
       "30      0          PC 17601   27.7208          NaN        C  \n",
       "31      0          PC 17569  146.5208          B78        C  \n",
       "32      0            335677    7.7500          NaN        Q  \n",
       "33      0        C.A. 24579   10.5000          NaN        S  \n",
       "34      0          PC 17604   82.1708          NaN        C  \n",
       "35      0            113789   52.0000          NaN        S  \n",
       "36      0              2677    7.2292          NaN        C  \n",
       "37      0        A./5. 2152    8.0500          NaN        S  \n",
       "38      0            345764   18.0000          NaN        S  \n",
       "39      0              2651   11.2417          NaN        C  \n",
       "40      0              7546    9.4750          NaN        S  \n",
       "41      0             11668   21.0000          NaN        S  \n",
       "42      0            349253    7.8958          NaN        C  \n",
       "43      2     SC/Paris 2123   41.5792          NaN        C  \n",
       "44      0            330958    7.8792          NaN        Q  \n",
       "45      0   S.C./A.4. 23567    8.0500          NaN        S  \n",
       "46      0            370371   15.5000          NaN        Q  \n",
       "47      0             14311    7.7500          NaN        Q  \n",
       "48      0              2662   21.6792          NaN        C  \n",
       "49      0            349237   17.8000          NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8c2045f",
   "metadata": {},
   "source": [
    "### Data Dictionary\n",
    "- Survived: \t0 = No, 1 = Yes  \n",
    "- pclass: \tTicket class\t1 = 1st, 2 = 2nd, 3 = 3rd  \t\n",
    "- sibsp:\t# of siblings / spouses aboard the Titanic  \t\n",
    "- parch:\t# of parents / children aboard the Titanic  \t\n",
    "- ticket:\tTicket number\t\n",
    "- cabin:\tCabin number\t\n",
    "- embarked:\tPort of Embarkation\tC = Cherbourg, Q = Queenstown, S = Southampton  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ff2ada6",
   "metadata": {},
   "source": [
    "**Total rows and columns**\n",
    "\n",
    "We can see that there are 891 rows and 12 columns in our training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d49e5140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6dac7e",
   "metadata": {},
   "source": [
    "We can see \n",
    "that there are 418 rows and 11 columns in our tasting dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8688bbbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 11)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27c1de8c",
   "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": "markdown",
   "id": "a94d4486",
   "metadata": {},
   "source": [
    "We can see that *Age* value is missing for many rows. \n",
    "\n",
    "Out of 891 rows, the *Age* value is present only in 714 rows.\n",
    "\n",
    "Similarly, *Cabin* values are also missing in many rows. Only 204 out of 891 rows have *Cabin* values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4201544c",
   "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": "markdown",
   "id": "8d6f303c",
   "metadata": {},
   "source": [
    "We can see that *Age* value is missing for many rows. \n",
    "\n",
    "Out of 418 rows, the *Age* value is present only in 332 rows.\n",
    "\n",
    "Similarly, *Cabin* values are also missing in many rows. Only 91 out of 418 rows have *Cabin* values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c3f0fe33",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>886</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>888</th>\n",
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       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "999c7f5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            177\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             0\n",
      "Cabin          687\n",
      "Embarked         2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3dba7ed",
   "metadata": {},
   "source": [
    "There are 177 rows with missing *Age*, 687 rows with missing *Cabin* and 2 rows with missing *Embarked* information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "95f19c3c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass   Name    Sex    Age  SibSp  Parch  Ticket   Fare  \\\n",
       "0          False   False  False  False  False  False  False   False  False   \n",
       "1          False   False  False  False  False  False  False   False  False   \n",
       "2          False   False  False  False  False  False  False   False  False   \n",
       "3          False   False  False  False  False  False  False   False  False   \n",
       "4          False   False  False  False  False  False  False   False  False   \n",
       "..           ...     ...    ...    ...    ...    ...    ...     ...    ...   \n",
       "413        False   False  False  False   True  False  False   False  False   \n",
       "414        False   False  False  False  False  False  False   False  False   \n",
       "415        False   False  False  False  False  False  False   False  False   \n",
       "416        False   False  False  False   True  False  False   False  False   \n",
       "417        False   False  False  False   True  False  False   False  False   \n",
       "\n",
       "     Cabin  Embarked  \n",
       "0     True     False  \n",
       "1     True     False  \n",
       "2     True     False  \n",
       "3     True     False  \n",
       "4     True     False  \n",
       "..     ...       ...  \n",
       "413   True     False  \n",
       "414  False     False  \n",
       "415   True     False  \n",
       "416   True     False  \n",
       "417   True     False  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f5d9b7f8",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81b797aa",
   "metadata": {},
   "source": [
    "There are 86 rows with missing *Age*, 327 rows with missing *Cabin* and 2 rows with missing *Embarked* information."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8eb269",
   "metadata": {},
   "source": [
    "### import python lib for visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee8a5f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set() # setting seaborn default for plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cd5606",
   "metadata": {},
   "source": [
    "### Bar Chart for Categorical Features\n",
    "- Pclass\n",
    "- Sex\n",
    "- SibSp ( # of siblings and spouse)\n",
    "- Parch ( # of parents and children)\n",
    "- Embarked\n",
    "- Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f1703e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9140a01e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5fb7f903",
   "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": 19,
   "id": "8c5e1d45",
   "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": "markdown",
   "id": "07af1c02",
   "metadata": {},
   "source": [
    "The Chart confirms **Women** more likely survivied than **Men**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ff47152c",
   "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": "markdown",
   "id": "dc9ebb9c",
   "metadata": {},
   "source": [
    "The Chart confirms **1st class** more likely survivied than **other classes**  \n",
    "The Chart confirms **3rd class** more likely dead than **other classes**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d3e1a2e",
   "metadata": {},
   "source": [
    "**siblings/spouses**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8b277657",
   "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": "markdown",
   "id": "159c82be",
   "metadata": {},
   "source": [
    "The Chart confirms **a person aboarded with more than 2 siblings or spouse** more likely survived  \n",
    "The Chart confirms **a person aboarded without siblings or spouse** more likely dead"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d0ba342",
   "metadata": {},
   "source": [
    "**parent,children**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ef4a412e",
   "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": "markdown",
   "id": "aec880c2",
   "metadata": {},
   "source": [
    "The Chart confirms **a person aboarded with more than 2 parents or children** more likely survived  \n",
    "The Chart confirms **a person aboarded alone** more likely dead"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb933935",
   "metadata": {},
   "source": [
    "- **C=Cherbourg**\n",
    "- **Q=Queenstown**\n",
    "- **S=Southampton**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "03a67991",
   "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('Embarked')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cff02cf",
   "metadata": {},
   "source": [
    "The Chart confirms **a person aboarded from C** slightly more likely survived  \n",
    "The Chart confirms **a person aboarded from Q** more likely dead  \n",
    "The Chart confirms **a person aboarded from S** more likely dead"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d154c4b",
   "metadata": {},
   "source": [
    "## 4. Feature engineering\n",
    "\n",
    "Feature engineering is the process of using domain knowledge of the data  \n",
    "to create features (**feature vectors**) that make machine learning algorithms work.  \n",
    "\n",
    "feature vector is an n-dimensional vector of numerical features that represent some object.  \n",
    "Many algorithms in machine learning require a numerical representation of objects,  \n",
    "since such representations facilitate processing and statistical analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3f7dee0b",
   "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",
       "  </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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8631fdd7",
   "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": "markdown",
   "id": "fb0a35da",
   "metadata": {},
   "source": [
    "### 4.1 how titanic sank?\n",
    "sank from the bow of the ship where third class rooms located  \n",
    "conclusion, Pclass is key feature for classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bb7ef63a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"TItanic-Survival-Infographic.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"TItanic-Survival-Infographic.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7d588057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <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>female</td>\n",
       "      <td>4.0</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>S</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>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</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>male</td>\n",
       "      <td>20.0</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>S</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>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>female</td>\n",
       "      <td>14.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
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       "    <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>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "0             1         0       3   \n",
       "1             2         1       1   \n",
       "2             3         1       3   \n",
       "3             4         1       1   \n",
       "4             5         0       3   \n",
       "5             6         0       3   \n",
       "6             7         0       1   \n",
       "7             8         0       3   \n",
       "8             9         1       3   \n",
       "9            10         1       2   \n",
       "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",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "0                             Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                              Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                            Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                    Moran, Mr. James    male   NaN      0   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                      Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                 Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                     Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                        Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14               Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                               Rice, Master. Eugene    male   2.0      4   \n",
       "17                       Williams, Mr. Charles Eugene    male   NaN      0   \n",
       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                            Masselmani, Mrs. Fatima  female   NaN      0   \n",
       "\n",
       "    Parch            Ticket     Fare Cabin Embarked  \n",
       "0       0         A/5 21171   7.2500   NaN        S  \n",
       "1       0          PC 17599  71.2833   C85        C  \n",
       "2       0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3       0            113803  53.1000  C123        S  \n",
       "4       0            373450   8.0500   NaN        S  \n",
       "5       0            330877   8.4583   NaN        Q  \n",
       "6       0             17463  51.8625   E46        S  \n",
       "7       1            349909  21.0750   NaN        S  \n",
       "8       2            347742  11.1333   NaN        S  \n",
       "9       0            237736  30.0708   NaN        C  \n",
       "10      1           PP 9549  16.7000    G6        S  \n",
       "11      0            113783  26.5500  C103        S  \n",
       "12      0         A/5. 2151   8.0500   NaN        S  \n",
       "13      5            347082  31.2750   NaN        S  \n",
       "14      0            350406   7.8542   NaN        S  \n",
       "15      0            248706  16.0000   NaN        S  \n",
       "16      1            382652  29.1250   NaN        Q  \n",
       "17      0            244373  13.0000   NaN        S  \n",
       "18      0            345763  18.0000   NaN        S  \n",
       "19      0              2649   7.2250   NaN        C  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc8a4821",
   "metadata": {},
   "source": [
    "### 4.2 Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2a38afca",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_test_data = [train, test]\n",
    "\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "990665cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          517\n",
       "Miss        182\n",
       "Mrs         125\n",
       "Master       40\n",
       "Dr            7\n",
       "Rev           6\n",
       "Col           2\n",
       "Major         2\n",
       "Mlle          2\n",
       "Sir           1\n",
       "Don           1\n",
       "Mme           1\n",
       "Jonkheer      1\n",
       "Countess      1\n",
       "Capt          1\n",
       "Lady          1\n",
       "Ms            1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b9123120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr        240\n",
       "Miss       78\n",
       "Mrs        72\n",
       "Master     21\n",
       "Rev         2\n",
       "Col         2\n",
       "Dona        1\n",
       "Ms          1\n",
       "Dr          1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d81ce42b",
   "metadata": {},
   "source": [
    "#### Title map\n",
    "Mr : 0  \n",
    "Miss : 1  \n",
    "Mrs: 2  \n",
    "Others: 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b625c79d",
   "metadata": {},
   "outputs": [],
   "source": [
    "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2,\"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"Countess\": 3,\"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Dona\" : 3, \"Mme\": 3,\"Capt\": 3,\"Sir\": 3 }\n",
    "\n",
    "for dataset in train_test_data:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "421aeee7",
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <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>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <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>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <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>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <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>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <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>female</td>\n",
       "      <td>4.0</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>S</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>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</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>male</td>\n",
       "      <td>20.0</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>S</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>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "\n",
       "                                                 Name     Sex   Age  SibSp  \\\n",
       "0                             Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                              Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                            Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                    Moran, Mr. James    male   NaN      0   \n",
       "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                      Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8   Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                 Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
       "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "12                     Saundercock, Mr. William Henry    male  20.0      0   \n",
       "13                        Andersson, Mr. Anders Johan    male  39.0      1   \n",
       "14               Vestrom, Miss. Hulda Amanda Adolfina  female  14.0      0   \n",
       "15                   Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "16                               Rice, Master. Eugene    male   2.0      4   \n",
       "17                       Williams, Mr. Charles Eugene    male   NaN      0   \n",
       "18  Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0      1   \n",
       "19                            Masselmani, Mrs. Fatima  female   NaN      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  \n",
       "5       0            330877   8.4583   NaN        Q      0  \n",
       "6       0             17463  51.8625   E46        S      0  \n",
       "7       1            349909  21.0750   NaN        S      3  \n",
       "8       2            347742  11.1333   NaN        S      2  \n",
       "9       0            237736  30.0708   NaN        C      2  \n",
       "10      1           PP 9549  16.7000    G6        S      1  \n",
       "11      0            113783  26.5500  C103        S      1  \n",
       "12      0         A/5. 2151   8.0500   NaN        S      0  \n",
       "13      5            347082  31.2750   NaN        S      0  \n",
       "14      0            350406   7.8542   NaN        S      1  \n",
       "15      0            248706  16.0000   NaN        S      2  \n",
       "16      1            382652  29.1250   NaN        Q      3  \n",
       "17      0            244373  13.0000   NaN        S      0  \n",
       "18      0            345763  18.0000   NaN        S      2  \n",
       "19      0              2649   7.2250   NaN        C      2  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d23028a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 13)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b83d0fa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 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",
      " 12  Title        891 non-null    int64  \n",
      "dtypes: float64(2), int64(6), object(5)\n",
      "memory usage: 90.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "51f70a93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>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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       "    <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",
       "      <td>2</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",
       "      <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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan Cervin</td>\n",
       "      <td>male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7538</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330972</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Mr. Albert Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>Davies, Mr. John Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4 48871</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>902</td>\n",
       "      <td>3</td>\n",
       "      <td>Ilieff, Mr. Ylio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349220</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>903</td>\n",
       "      <td>1</td>\n",
       "      <td>Jones, Mr. Charles Cresson</td>\n",
       "      <td>male</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>694</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>904</td>\n",
       "      <td>1</td>\n",
       "      <td>Snyder, Mrs. John Pillsbury (Nelle Stevenson)</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21228</td>\n",
       "      <td>82.2667</td>\n",
       "      <td>B45</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>905</td>\n",
       "      <td>2</td>\n",
       "      <td>Howard, Mr. Benjamin</td>\n",
       "      <td>male</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24065</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>906</td>\n",
       "      <td>1</td>\n",
       "      <td>Chaffee, Mrs. Herbert Fuller (Carrie Constance...</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>W.E.P. 5734</td>\n",
       "      <td>61.1750</td>\n",
       "      <td>E31</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>907</td>\n",
       "      <td>2</td>\n",
       "      <td>del Carlo, Mrs. Sebastiano (Argenia Genovesi)</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2167</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>908</td>\n",
       "      <td>2</td>\n",
       "      <td>Keane, Mr. Daniel</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233734</td>\n",
       "      <td>12.3500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>909</td>\n",
       "      <td>3</td>\n",
       "      <td>Assaf, Mr. Gerios</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2692</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>910</td>\n",
       "      <td>3</td>\n",
       "      <td>Ilmakangas, Miss. Ida Livija</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101270</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>911</td>\n",
       "      <td>3</td>\n",
       "      <td>Assaf Khalil, Mrs. Mariana (Miriam\")\"</td>\n",
       "      <td>female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2696</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Pclass                                               Name  \\\n",
       "0           892       3                                   Kelly, Mr. James   \n",
       "1           893       3                   Wilkes, Mrs. James (Ellen Needs)   \n",
       "2           894       2                          Myles, Mr. Thomas Francis   \n",
       "3           895       3                                   Wirz, Mr. Albert   \n",
       "4           896       3       Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
       "5           897       3                         Svensson, Mr. Johan Cervin   \n",
       "6           898       3                               Connolly, Miss. Kate   \n",
       "7           899       2                       Caldwell, Mr. Albert Francis   \n",
       "8           900       3          Abrahim, Mrs. Joseph (Sophie Halaut Easu)   \n",
       "9           901       3                            Davies, Mr. John Samuel   \n",
       "10          902       3                                   Ilieff, Mr. Ylio   \n",
       "11          903       1                         Jones, Mr. Charles Cresson   \n",
       "12          904       1      Snyder, Mrs. John Pillsbury (Nelle Stevenson)   \n",
       "13          905       2                               Howard, Mr. Benjamin   \n",
       "14          906       1  Chaffee, Mrs. Herbert Fuller (Carrie Constance...   \n",
       "15          907       2      del Carlo, Mrs. Sebastiano (Argenia Genovesi)   \n",
       "16          908       2                                  Keane, Mr. Daniel   \n",
       "17          909       3                                  Assaf, Mr. Gerios   \n",
       "18          910       3                       Ilmakangas, Miss. Ida Livija   \n",
       "19          911       3              Assaf Khalil, Mrs. Mariana (Miriam\")\"   \n",
       "\n",
       "       Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \\\n",
       "0     male  34.5      0      0            330911   7.8292   NaN        Q   \n",
       "1   female  47.0      1      0            363272   7.0000   NaN        S   \n",
       "2     male  62.0      0      0            240276   9.6875   NaN        Q   \n",
       "3     male  27.0      0      0            315154   8.6625   NaN        S   \n",
       "4   female  22.0      1      1           3101298  12.2875   NaN        S   \n",
       "5     male  14.0      0      0              7538   9.2250   NaN        S   \n",
       "6   female  30.0      0      0            330972   7.6292   NaN        Q   \n",
       "7     male  26.0      1      1            248738  29.0000   NaN        S   \n",
       "8   female  18.0      0      0              2657   7.2292   NaN        C   \n",
       "9     male  21.0      2      0         A/4 48871  24.1500   NaN        S   \n",
       "10    male   NaN      0      0            349220   7.8958   NaN        S   \n",
       "11    male  46.0      0      0               694  26.0000   NaN        S   \n",
       "12  female  23.0      1      0             21228  82.2667   B45        S   \n",
       "13    male  63.0      1      0             24065  26.0000   NaN        S   \n",
       "14  female  47.0      1      0       W.E.P. 5734  61.1750   E31        S   \n",
       "15  female  24.0      1      0     SC/PARIS 2167  27.7208   NaN        C   \n",
       "16    male  35.0      0      0            233734  12.3500   NaN        Q   \n",
       "17    male  21.0      0      0              2692   7.2250   NaN        C   \n",
       "18  female  27.0      1      0  STON/O2. 3101270   7.9250   NaN        S   \n",
       "19  female  45.0      0      0              2696   7.2250   NaN        C   \n",
       "\n",
       "    Title  \n",
       "0       0  \n",
       "1       2  \n",
       "2       0  \n",
       "3       0  \n",
       "4       2  \n",
       "5       0  \n",
       "6       1  \n",
       "7       0  \n",
       "8       2  \n",
       "9       0  \n",
       "10      0  \n",
       "11      0  \n",
       "12      2  \n",
       "13      0  \n",
       "14      2  \n",
       "15      2  \n",
       "16      0  \n",
       "17      0  \n",
       "18      1  \n",
       "19      2  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eb1cc8cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 12)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a0916081",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 12 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",
      " 11  Title        418 non-null    int64  \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 39.3+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "360f6747",
   "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": 39,
   "id": "f0f92304",
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete unnecessary name feature from dataset\n",
    "train.drop('Name', axis=1, inplace=True)\n",
    "test.drop('Name', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7aa904d4",
   "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>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>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>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>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>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     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3    male  22.0      1      0   \n",
       "1            2         1       1  female  38.0      1      0   \n",
       "2            3         1       3  female  26.0      0      0   \n",
       "3            4         1       1  female  35.0      1      0   \n",
       "4            5         0       3    male  35.0      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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e42ab8cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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>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     Sex   Age  SibSp  Parch   Ticket     Fare Cabin  \\\n",
       "0          892       3    male  34.5      0      0   330911   7.8292   NaN   \n",
       "1          893       3  female  47.0      1      0   363272   7.0000   NaN   \n",
       "2          894       2    male  62.0      0      0   240276   9.6875   NaN   \n",
       "3          895       3    male  27.0      0      0   315154   8.6625   NaN   \n",
       "4          896       3  female  22.0      1      1  3101298  12.2875   NaN   \n",
       "\n",
       "  Embarked  Title  \n",
       "0        Q      0  \n",
       "1        S      2  \n",
       "2        Q      0  \n",
       "3        S      0  \n",
       "4        S      2  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af2e34b0",
   "metadata": {},
   "source": [
    "### 4.3 Sex Mapping\n",
    "\n",
    "male: 0\n",
    "female: 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2240a322",
   "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": 43,
   "id": "c9462b03",
   "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": 44,
   "id": "37d0e9c2",
   "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>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>1</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>1</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>1</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>0</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  Sex   Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  22.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  38.0      1      0          PC 17599   \n",
       "2            3         1       3    1  26.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  35.0      1      0            113803   \n",
       "4            5         0       3    0  35.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin Embarked  Title  \n",
       "0   7.2500   NaN        S      0  \n",
       "1  71.2833   C85        C      2  \n",
       "2   7.9250   NaN        S      1  \n",
       "3  53.1000  C123        S      2  \n",
       "4   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2ec978cb",
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
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      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex   Age  SibSp  Parch   Ticket     Fare Cabin  \\\n",
       "0          892       3    0  34.5      0      0   330911   7.8292   NaN   \n",
       "1          893       3    1  47.0      1      0   363272   7.0000   NaN   \n",
       "2          894       2    0  62.0      0      0   240276   9.6875   NaN   \n",
       "3          895       3    0  27.0      0      0   315154   8.6625   NaN   \n",
       "4          896       3    1  22.0      1      1  3101298  12.2875   NaN   \n",
       "\n",
       "  Embarked  Title  \n",
       "0        Q      0  \n",
       "1        S      2  \n",
       "2        Q      0  \n",
       "3        S      0  \n",
       "4        S      2  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ce7544",
   "metadata": {},
   "source": [
    "### 4.4 Age"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb751403",
   "metadata": {},
   "source": [
    "#### 4.4.1 some age is missing\n",
    "Let's use Title's median age for missing Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "71106501",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Survived</th>\n",
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       "      <td>0</td>\n",
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       "      <th>7</th>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2</td>\n",
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       "      <td>23</td>\n",
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       "      <td>24</td>\n",
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       "      <td>35.5000</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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>0</td>\n",
       "      <td>19.0</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>S</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>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>0</td>\n",
       "      <td>40.0</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>C</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>1</td>\n",
       "      <td>NaN</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>C</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>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>335677</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>0</td>\n",
       "      <td>66.0</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>S</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>0</td>\n",
       "      <td>28.0</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>C</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>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113789</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2677</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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>0</td>\n",
       "      <td>21.0</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>S</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>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>345764</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2651</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7546</td>\n",
       "      <td>9.4750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11668</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349253</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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>1</td>\n",
       "      <td>3.0</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>C</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>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330958</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>0</td>\n",
       "      <td>NaN</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>S</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>370371</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14311</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2662</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>349237</td>\n",
       "      <td>17.8000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</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  22.0      1      0         A/5 21171   \n",
       "1             2         1       1    1  38.0      1      0          PC 17599   \n",
       "2             3         1       3    1  26.0      0      0  STON/O2. 3101282   \n",
       "3             4         1       1    1  35.0      1      0            113803   \n",
       "4             5         0       3    0  35.0      0      0            373450   \n",
       "5             6         0       3    0   NaN      0      0            330877   \n",
       "6             7         0       1    0  54.0      0      0             17463   \n",
       "7             8         0       3    0   2.0      3      1            349909   \n",
       "8             9         1       3    1  27.0      0      2            347742   \n",
       "9            10         1       2    1  14.0      1      0            237736   \n",
       "10           11         1       3    1   4.0      1      1           PP 9549   \n",
       "11           12         1       1    1  58.0      0      0            113783   \n",
       "12           13         0       3    0  20.0      0      0         A/5. 2151   \n",
       "13           14         0       3    0  39.0      1      5            347082   \n",
       "14           15         0       3    1  14.0      0      0            350406   \n",
       "15           16         1       2    1  55.0      0      0            248706   \n",
       "16           17         0       3    0   2.0      4      1            382652   \n",
       "17           18         1       2    0   NaN      0      0            244373   \n",
       "18           19         0       3    1  31.0      1      0            345763   \n",
       "19           20         1       3    1   NaN      0      0              2649   \n",
       "20           21         0       2    0  35.0      0      0            239865   \n",
       "21           22         1       2    0  34.0      0      0            248698   \n",
       "22           23         1       3    1  15.0      0      0            330923   \n",
       "23           24         1       1    0  28.0      0      0            113788   \n",
       "24           25         0       3    1   8.0      3      1            349909   \n",
       "25           26         1       3    1  38.0      1      5            347077   \n",
       "26           27         0       3    0   NaN      0      0              2631   \n",
       "27           28         0       1    0  19.0      3      2             19950   \n",
       "28           29         1       3    1   NaN      0      0            330959   \n",
       "29           30         0       3    0   NaN      0      0            349216   \n",
       "30           31         0       1    0  40.0      0      0          PC 17601   \n",
       "31           32         1       1    1   NaN      1      0          PC 17569   \n",
       "32           33         1       3    1   NaN      0      0            335677   \n",
       "33           34         0       2    0  66.0      0      0        C.A. 24579   \n",
       "34           35         0       1    0  28.0      1      0          PC 17604   \n",
       "35           36         0       1    0  42.0      1      0            113789   \n",
       "36           37         1       3    0   NaN      0      0              2677   \n",
       "37           38         0       3    0  21.0      0      0        A./5. 2152   \n",
       "38           39         0       3    1  18.0      2      0            345764   \n",
       "39           40         1       3    1  14.0      1      0              2651   \n",
       "40           41         0       3    1  40.0      1      0              7546   \n",
       "41           42         0       2    1  27.0      1      0             11668   \n",
       "42           43         0       3    0   NaN      0      0            349253   \n",
       "43           44         1       2    1   3.0      1      2     SC/Paris 2123   \n",
       "44           45         1       3    1  19.0      0      0            330958   \n",
       "45           46         0       3    0   NaN      0      0   S.C./A.4. 23567   \n",
       "46           47         0       3    0   NaN      1      0            370371   \n",
       "47           48         1       3    1   NaN      0      0             14311   \n",
       "48           49         0       3    0   NaN      2      0              2662   \n",
       "49           50         0       3    1  18.0      1      0            349237   \n",
       "\n",
       "        Fare        Cabin Embarked  Title  \n",
       "0     7.2500          NaN        S      0  \n",
       "1    71.2833          C85        C      2  \n",
       "2     7.9250          NaN        S      1  \n",
       "3    53.1000         C123        S      2  \n",
       "4     8.0500          NaN        S      0  \n",
       "5     8.4583          NaN        Q      0  \n",
       "6    51.8625          E46        S      0  \n",
       "7    21.0750          NaN        S      3  \n",
       "8    11.1333          NaN        S      2  \n",
       "9    30.0708          NaN        C      2  \n",
       "10   16.7000           G6        S      1  \n",
       "11   26.5500         C103        S      1  \n",
       "12    8.0500          NaN        S      0  \n",
       "13   31.2750          NaN        S      0  \n",
       "14    7.8542          NaN        S      1  \n",
       "15   16.0000          NaN        S      2  \n",
       "16   29.1250          NaN        Q      3  \n",
       "17   13.0000          NaN        S      0  \n",
       "18   18.0000          NaN        S      2  \n",
       "19    7.2250          NaN        C      2  \n",
       "20   26.0000          NaN        S      0  \n",
       "21   13.0000          D56        S      0  \n",
       "22    8.0292          NaN        Q      1  \n",
       "23   35.5000           A6        S      0  \n",
       "24   21.0750          NaN        S      1  \n",
       "25   31.3875          NaN        S      2  \n",
       "26    7.2250          NaN        C      0  \n",
       "27  263.0000  C23 C25 C27        S      0  \n",
       "28    7.8792          NaN        Q      1  \n",
       "29    7.8958          NaN        S      0  \n",
       "30   27.7208          NaN        C      3  \n",
       "31  146.5208          B78        C      2  \n",
       "32    7.7500          NaN        Q      1  \n",
       "33   10.5000          NaN        S      0  \n",
       "34   82.1708          NaN        C      0  \n",
       "35   52.0000          NaN        S      0  \n",
       "36    7.2292          NaN        C      0  \n",
       "37    8.0500          NaN        S      0  \n",
       "38   18.0000          NaN        S      1  \n",
       "39   11.2417          NaN        C      1  \n",
       "40    9.4750          NaN        S      2  \n",
       "41   21.0000          NaN        S      2  \n",
       "42    7.8958          NaN        C      0  \n",
       "43   41.5792          NaN        C      1  \n",
       "44    7.8792          NaN        Q      1  \n",
       "45    8.0500          NaN        S      0  \n",
       "46   15.5000          NaN        Q      0  \n",
       "47    7.7500          NaN        Q      1  \n",
       "48   21.6792          NaN        C      0  \n",
       "49   17.8000          NaN        S      2  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8bcfa7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</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>0</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>1</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7538</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330972</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4 48871</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>902</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349220</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>903</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>694</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>904</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21228</td>\n",
       "      <td>82.2667</td>\n",
       "      <td>B45</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>905</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24065</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>906</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>W.E.P. 5734</td>\n",
       "      <td>61.1750</td>\n",
       "      <td>E31</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>907</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2167</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>908</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233734</td>\n",
       "      <td>12.3500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>909</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2692</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101270</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>911</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2696</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>912</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17603</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>913</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>C 17368</td>\n",
       "      <td>3.1708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>914</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17598</td>\n",
       "      <td>31.6833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>915</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17597</td>\n",
       "      <td>61.3792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>916</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>917</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 3337</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>918</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113509</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>B36</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>919</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2698</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>920</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113054</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>A21</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>921</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2662</td>\n",
       "      <td>21.6792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>922</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/AH 3085</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>923</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 31029</td>\n",
       "      <td>31.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>924</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>C.A. 2315</td>\n",
       "      <td>20.5750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>925</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13236</td>\n",
       "      <td>57.7500</td>\n",
       "      <td>C78</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>927</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2682</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>928</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>342712</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>929</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315087</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>930</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345768</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>931</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>932</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>349256</td>\n",
       "      <td>13.4167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>933</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113778</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>D34</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>934</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101263</td>\n",
       "      <td>7.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>935</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>237249</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>936</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11753</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D19</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>937</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O 2. 3101291</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>938</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17594</td>\n",
       "      <td>29.7000</td>\n",
       "      <td>A9</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>939</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370374</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>940</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11813</td>\n",
       "      <td>76.2917</td>\n",
       "      <td>D15</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>941</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>C.A. 37671</td>\n",
       "      <td>15.9000</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  Sex   Age  SibSp  Parch              Ticket  \\\n",
       "0           892       3    0  34.5      0      0              330911   \n",
       "1           893       3    1  47.0      1      0              363272   \n",
       "2           894       2    0  62.0      0      0              240276   \n",
       "3           895       3    0  27.0      0      0              315154   \n",
       "4           896       3    1  22.0      1      1             3101298   \n",
       "5           897       3    0  14.0      0      0                7538   \n",
       "6           898       3    1  30.0      0      0              330972   \n",
       "7           899       2    0  26.0      1      1              248738   \n",
       "8           900       3    1  18.0      0      0                2657   \n",
       "9           901       3    0  21.0      2      0           A/4 48871   \n",
       "10          902       3    0   NaN      0      0              349220   \n",
       "11          903       1    0  46.0      0      0                 694   \n",
       "12          904       1    1  23.0      1      0               21228   \n",
       "13          905       2    0  63.0      1      0               24065   \n",
       "14          906       1    1  47.0      1      0         W.E.P. 5734   \n",
       "15          907       2    1  24.0      1      0       SC/PARIS 2167   \n",
       "16          908       2    0  35.0      0      0              233734   \n",
       "17          909       3    0  21.0      0      0                2692   \n",
       "18          910       3    1  27.0      1      0    STON/O2. 3101270   \n",
       "19          911       3    1  45.0      0      0                2696   \n",
       "20          912       1    0  55.0      1      0            PC 17603   \n",
       "21          913       3    0   9.0      0      1             C 17368   \n",
       "22          914       1    1   NaN      0      0            PC 17598   \n",
       "23          915       1    0  21.0      0      1            PC 17597   \n",
       "24          916       1    1  48.0      1      3            PC 17608   \n",
       "25          917       3    0  50.0      1      0           A/5. 3337   \n",
       "26          918       1    1  22.0      0      1              113509   \n",
       "27          919       3    0  22.5      0      0                2698   \n",
       "28          920       1    0  41.0      0      0              113054   \n",
       "29          921       3    0   NaN      2      0                2662   \n",
       "30          922       2    0  50.0      1      0          SC/AH 3085   \n",
       "31          923       2    0  24.0      2      0          C.A. 31029   \n",
       "32          924       3    1  33.0      1      2           C.A. 2315   \n",
       "33          925       3    1   NaN      1      2          W./C. 6607   \n",
       "34          926       1    0  30.0      1      0               13236   \n",
       "35          927       3    0  18.5      0      0                2682   \n",
       "36          928       3    1   NaN      0      0              342712   \n",
       "37          929       3    1  21.0      0      0              315087   \n",
       "38          930       3    0  25.0      0      0              345768   \n",
       "39          931       3    0   NaN      0      0                1601   \n",
       "40          932       3    0  39.0      0      1              349256   \n",
       "41          933       1    0   NaN      0      0              113778   \n",
       "42          934       3    0  41.0      0      0  SOTON/O.Q. 3101263   \n",
       "43          935       2    1  30.0      0      0              237249   \n",
       "44          936       1    1  45.0      1      0               11753   \n",
       "45          937       3    0  25.0      0      0   STON/O 2. 3101291   \n",
       "46          938       1    0  45.0      0      0            PC 17594   \n",
       "47          939       3    0   NaN      0      0              370374   \n",
       "48          940       1    1  60.0      0      0               11813   \n",
       "49          941       3    1  36.0      0      2          C.A. 37671   \n",
       "\n",
       "        Fare            Cabin Embarked  Title  \n",
       "0     7.8292              NaN        Q      0  \n",
       "1     7.0000              NaN        S      2  \n",
       "2     9.6875              NaN        Q      0  \n",
       "3     8.6625              NaN        S      0  \n",
       "4    12.2875              NaN        S      2  \n",
       "5     9.2250              NaN        S      0  \n",
       "6     7.6292              NaN        Q      1  \n",
       "7    29.0000              NaN        S      0  \n",
       "8     7.2292              NaN        C      2  \n",
       "9    24.1500              NaN        S      0  \n",
       "10    7.8958              NaN        S      0  \n",
       "11   26.0000              NaN        S      0  \n",
       "12   82.2667              B45        S      2  \n",
       "13   26.0000              NaN        S      0  \n",
       "14   61.1750              E31        S      2  \n",
       "15   27.7208              NaN        C      2  \n",
       "16   12.3500              NaN        Q      0  \n",
       "17    7.2250              NaN        C      0  \n",
       "18    7.9250              NaN        S      1  \n",
       "19    7.2250              NaN        C      2  \n",
       "20   59.4000              NaN        C      0  \n",
       "21    3.1708              NaN        S      3  \n",
       "22   31.6833              NaN        S      2  \n",
       "23   61.3792              NaN        C      0  \n",
       "24  262.3750  B57 B59 B63 B66        C      2  \n",
       "25   14.5000              NaN        S      0  \n",
       "26   61.9792              B36        C      1  \n",
       "27    7.2250              NaN        C      0  \n",
       "28   30.5000              A21        S      0  \n",
       "29   21.6792              NaN        C      0  \n",
       "30   26.0000              NaN        S      0  \n",
       "31   31.5000              NaN        S      0  \n",
       "32   20.5750              NaN        S      2  \n",
       "33   23.4500              NaN        S      2  \n",
       "34   57.7500              C78        C      0  \n",
       "35    7.2292              NaN        C      0  \n",
       "36    8.0500              NaN        S      1  \n",
       "37    8.6625              NaN        S      1  \n",
       "38    9.5000              NaN        S      0  \n",
       "39   56.4958              NaN        S      0  \n",
       "40   13.4167              NaN        C      0  \n",
       "41   26.5500              D34        S      0  \n",
       "42    7.8500              NaN        S      0  \n",
       "43   13.0000              NaN        S      2  \n",
       "44   52.5542              D19        S      2  \n",
       "45    7.9250              NaN        S      0  \n",
       "46   29.7000               A9        C      0  \n",
       "47    7.7500              NaN        Q      0  \n",
       "48   76.2917              D15        C      2  \n",
       "49   15.9000              NaN        S      2  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "40591e0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill missing age with median age for each title (Mr, Mrs, Miss, Others)\n",
    "\n",
    "train[\"Age\"].fillna(train.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)\n",
    "\n",
    "test[\"Age\"].fillna(test.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f5dd0cab",
   "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>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>1</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>1</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>1</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>0</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <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>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <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>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <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>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <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>1</td>\n",
       "      <td>4.0</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>S</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>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</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>0</td>\n",
       "      <td>20.0</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>S</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>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2</td>\n",
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       "      <th>16</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
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       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2649</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</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  22.0      1      0         A/5 21171   \n",
       "1             2         1       1    1  38.0      1      0          PC 17599   \n",
       "2             3         1       3    1  26.0      0      0  STON/O2. 3101282   \n",
       "3             4         1       1    1  35.0      1      0            113803   \n",
       "4             5         0       3    0  35.0      0      0            373450   \n",
       "5             6         0       3    0  30.0      0      0            330877   \n",
       "6             7         0       1    0  54.0      0      0             17463   \n",
       "7             8         0       3    0   2.0      3      1            349909   \n",
       "8             9         1       3    1  27.0      0      2            347742   \n",
       "9            10         1       2    1  14.0      1      0            237736   \n",
       "10           11         1       3    1   4.0      1      1           PP 9549   \n",
       "11           12         1       1    1  58.0      0      0            113783   \n",
       "12           13         0       3    0  20.0      0      0         A/5. 2151   \n",
       "13           14         0       3    0  39.0      1      5            347082   \n",
       "14           15         0       3    1  14.0      0      0            350406   \n",
       "15           16         1       2    1  55.0      0      0            248706   \n",
       "16           17         0       3    0   2.0      4      1            382652   \n",
       "17           18         1       2    0  30.0      0      0            244373   \n",
       "18           19         0       3    1  31.0      1      0            345763   \n",
       "19           20         1       3    1  35.0      0      0              2649   \n",
       "\n",
       "       Fare Cabin Embarked  Title  \n",
       "0    7.2500   NaN        S      0  \n",
       "1   71.2833   C85        C      2  \n",
       "2    7.9250   NaN        S      1  \n",
       "3   53.1000  C123        S      2  \n",
       "4    8.0500   NaN        S      0  \n",
       "5    8.4583   NaN        Q      0  \n",
       "6   51.8625   E46        S      0  \n",
       "7   21.0750   NaN        S      3  \n",
       "8   11.1333   NaN        S      2  \n",
       "9   30.0708   NaN        C      2  \n",
       "10  16.7000    G6        S      1  \n",
       "11  26.5500  C103        S      1  \n",
       "12   8.0500   NaN        S      0  \n",
       "13  31.2750   NaN        S      0  \n",
       "14   7.8542   NaN        S      1  \n",
       "15  16.0000   NaN        S      2  \n",
       "16  29.1250   NaN        Q      3  \n",
       "17  13.0000   NaN        S      0  \n",
       "18  18.0000   NaN        S      2  \n",
       "19   7.2250   NaN        C      2  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3f462694",
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   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>2696</td>\n",
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      ],
      "text/plain": [
       "    PassengerId  Pclass  Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0           892       3    0  34.5      0      0            330911   7.8292   \n",
       "1           893       3    1  47.0      1      0            363272   7.0000   \n",
       "2           894       2    0  62.0      0      0            240276   9.6875   \n",
       "3           895       3    0  27.0      0      0            315154   8.6625   \n",
       "4           896       3    1  22.0      1      1           3101298  12.2875   \n",
       "5           897       3    0  14.0      0      0              7538   9.2250   \n",
       "6           898       3    1  30.0      0      0            330972   7.6292   \n",
       "7           899       2    0  26.0      1      1            248738  29.0000   \n",
       "8           900       3    1  18.0      0      0              2657   7.2292   \n",
       "9           901       3    0  21.0      2      0         A/4 48871  24.1500   \n",
       "10          902       3    0  28.5      0      0            349220   7.8958   \n",
       "11          903       1    0  46.0      0      0               694  26.0000   \n",
       "12          904       1    1  23.0      1      0             21228  82.2667   \n",
       "13          905       2    0  63.0      1      0             24065  26.0000   \n",
       "14          906       1    1  47.0      1      0       W.E.P. 5734  61.1750   \n",
       "15          907       2    1  24.0      1      0     SC/PARIS 2167  27.7208   \n",
       "16          908       2    0  35.0      0      0            233734  12.3500   \n",
       "17          909       3    0  21.0      0      0              2692   7.2250   \n",
       "18          910       3    1  27.0      1      0  STON/O2. 3101270   7.9250   \n",
       "19          911       3    1  45.0      0      0              2696   7.2250   \n",
       "\n",
       "   Cabin Embarked  Title  \n",
       "0    NaN        Q      0  \n",
       "1    NaN        S      2  \n",
       "2    NaN        Q      0  \n",
       "3    NaN        S      0  \n",
       "4    NaN        S      2  \n",
       "5    NaN        S      0  \n",
       "6    NaN        Q      1  \n",
       "7    NaN        S      0  \n",
       "8    NaN        C      2  \n",
       "9    NaN        S      0  \n",
       "10   NaN        S      0  \n",
       "11   NaN        S      0  \n",
       "12   B45        S      2  \n",
       "13   NaN        S      0  \n",
       "14   E31        S      2  \n",
       "15   NaN        C      2  \n",
       "16   NaN        Q      0  \n",
       "17   NaN        C      0  \n",
       "18   NaN        S      1  \n",
       "19   NaN        C      2  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3099cf9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      30.0\n",
       "1      35.0\n",
       "2      21.0\n",
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       "4      30.0\n",
       "       ... \n",
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       "889    30.0\n",
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       "Name: Age, Length: 891, dtype: float64"
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     },
     "execution_count": 51,
     "metadata": {},
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    }
   ],
   "source": [
    "train.groupby(\"Title\")[\"Age\"].transform(\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "89b8735a",
   "metadata": {},
   "outputs": [
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       "    <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>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>1</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>1</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>1</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>0</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  Sex   Age  SibSp  Parch            Ticket  \\\n",
       "0            1         0       3    0  22.0      1      0         A/5 21171   \n",
       "1            2         1       1    1  38.0      1      0          PC 17599   \n",
       "2            3         1       3    1  26.0      0      0  STON/O2. 3101282   \n",
       "3            4         1       1    1  35.0      1      0            113803   \n",
       "4            5         0       3    0  35.0      0      0            373450   \n",
       "\n",
       "      Fare Cabin Embarked  Title  \n",
       "0   7.2500   NaN        S      0  \n",
       "1  71.2833   C85        C      2  \n",
       "2   7.9250   NaN        S      1  \n",
       "3  53.1000  C123        S      2  \n",
       "4   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a4d37e97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      28.5\n",
       "1      36.5\n",
       "2      28.5\n",
       "3      28.5\n",
       "4      36.5\n",
       "       ... \n",
       "413    28.5\n",
       "414    10.0\n",
       "415    28.5\n",
       "416    28.5\n",
       "417    10.0\n",
       "Name: Age, Length: 418, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby(\"Title\")[\"Age\"].transform(\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "74ee2c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</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>0</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>1</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  Sex   Age  SibSp  Parch   Ticket     Fare Cabin  \\\n",
       "0          892       3    0  34.5      0      0   330911   7.8292   NaN   \n",
       "1          893       3    1  47.0      1      0   363272   7.0000   NaN   \n",
       "2          894       2    0  62.0      0      0   240276   9.6875   NaN   \n",
       "3          895       3    0  27.0      0      0   315154   8.6625   NaN   \n",
       "4          896       3    1  22.0      1      1  3101298  12.2875   NaN   \n",
       "\n",
       "  Embarked  Title  \n",
       "0        Q      0  \n",
       "1        S      2  \n",
       "2        Q      0  \n",
       "3        S      0  \n",
       "4        S      2  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b091d3e",
   "metadata": {},
   "source": [
    "### Chart total dead ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7962858d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 919.55x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1c521891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 20.0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 919.55x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    " \n",
    "plt.xlim(0,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8a40a0c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20.0, 30.0)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 919.55x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "\n",
    "plt.xlim(20, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "36d0b5b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30.0, 40.0)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 919.55x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "\n",
    "plt.xlim(30, 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "fcd593c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40.0, 60.0)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 919.55x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "\n",
    "plt.xlim(40, 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bd608b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60.0, 80.0)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 919.55x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "\n",
    "plt.xlim(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d4adf691",
   "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   Sex          891 non-null    int64  \n",
      " 4   Age          891 non-null    float64\n",
      " 5   SibSp        891 non-null    int64  \n",
      " 6   Parch        891 non-null    int64  \n",
      " 7   Ticket       891 non-null    object \n",
      " 8   Fare         891 non-null    float64\n",
      " 9   Cabin        204 non-null    object \n",
      " 10  Embarked     889 non-null    object \n",
      " 11  Title        891 non-null    int64  \n",
      "dtypes: float64(2), int64(7), object(3)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "72ceaa08",
   "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   Sex          418 non-null    int64  \n",
      " 3   Age          418 non-null    float64\n",
      " 4   SibSp        418 non-null    int64  \n",
      " 5   Parch        418 non-null    int64  \n",
      " 6   Ticket       418 non-null    object \n",
      " 7   Fare         417 non-null    float64\n",
      " 8   Cabin        91 non-null     object \n",
      " 9   Embarked     418 non-null    object \n",
      " 10  Title        418 non-null    int64  \n",
      "dtypes: float64(2), int64(6), object(3)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10773e69",
   "metadata": {},
   "source": [
    "#### 4.4.2 Binning\n",
    "Binning/Converting Numerical Age to Categorical Variable  \n",
    "\n",
    "feature vector map:  \n",
    "child: 0  \n",
    "young: 1  \n",
    "adult: 2  \n",
    "mid-age: 3  \n",
    "senior: 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "edbf638f",
   "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": 64,
   "id": "384469f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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>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>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>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>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>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",
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       "      <td>0</td>\n",
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       "      <th>5</th>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>S</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>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>C</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>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>S</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>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>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
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       "      <td>3</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
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       "      <td>S</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>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>S</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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>S</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>1</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
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       "      <td>3</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>Q</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>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",
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       "    <tr>\n",
       "      <th>18</th>\n",
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       "      <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>S</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>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>C</td>\n",
       "      <td>2</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",
       "5             6         0       3    0  2.0      0      0            330877   \n",
       "6             7         0       1    0  3.0      0      0             17463   \n",
       "7             8         0       3    0  0.0      3      1            349909   \n",
       "8             9         1       3    1  2.0      0      2            347742   \n",
       "9            10         1       2    1  0.0      1      0            237736   \n",
       "10           11         1       3    1  0.0      1      1           PP 9549   \n",
       "11           12         1       1    1  3.0      0      0            113783   \n",
       "12           13         0       3    0  1.0      0      0         A/5. 2151   \n",
       "13           14         0       3    0  3.0      1      5            347082   \n",
       "14           15         0       3    1  0.0      0      0            350406   \n",
       "15           16         1       2    1  3.0      0      0            248706   \n",
       "16           17         0       3    0  0.0      4      1            382652   \n",
       "17           18         1       2    0  2.0      0      0            244373   \n",
       "18           19         0       3    1  2.0      1      0            345763   \n",
       "19           20         1       3    1  2.0      0      0              2649   \n",
       "\n",
       "       Fare Cabin Embarked  Title  \n",
       "0    7.2500   NaN        S      0  \n",
       "1   71.2833   C85        C      2  \n",
       "2    7.9250   NaN        S      1  \n",
       "3   53.1000  C123        S      2  \n",
       "4    8.0500   NaN        S      0  \n",
       "5    8.4583   NaN        Q      0  \n",
       "6   51.8625   E46        S      0  \n",
       "7   21.0750   NaN        S      3  \n",
       "8   11.1333   NaN        S      2  \n",
       "9   30.0708   NaN        C      2  \n",
       "10  16.7000    G6        S      1  \n",
       "11  26.5500  C103        S      1  \n",
       "12   8.0500   NaN        S      0  \n",
       "13  31.2750   NaN        S      0  \n",
       "14   7.8542   NaN        S      1  \n",
       "15  16.0000   NaN        S      2  \n",
       "16  29.1250   NaN        Q      3  \n",
       "17  13.0000   NaN        S      0  \n",
       "18  18.0000   NaN        S      2  \n",
       "19   7.2250   NaN        C      2  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b34a0551",
   "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   Sex          891 non-null    int64  \n",
      " 4   Age          891 non-null    float64\n",
      " 5   SibSp        891 non-null    int64  \n",
      " 6   Parch        891 non-null    int64  \n",
      " 7   Ticket       891 non-null    object \n",
      " 8   Fare         891 non-null    float64\n",
      " 9   Cabin        204 non-null    object \n",
      " 10  Embarked     889 non-null    object \n",
      " 11  Title        891 non-null    int64  \n",
      "dtypes: float64(2), int64(7), object(3)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7c83770a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4 48871</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>902</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349220</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>903</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>694</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>904</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21228</td>\n",
       "      <td>82.2667</td>\n",
       "      <td>B45</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>905</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24065</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>906</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>W.E.P. 5734</td>\n",
       "      <td>61.1750</td>\n",
       "      <td>E31</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>907</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2167</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>908</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233734</td>\n",
       "      <td>12.3500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>909</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2692</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101270</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>911</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2696</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Pclass  Sex  Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0           892       3    0  2.0      0      0            330911   7.8292   \n",
       "1           893       3    1  3.0      1      0            363272   7.0000   \n",
       "2           894       2    0  3.0      0      0            240276   9.6875   \n",
       "3           895       3    0  2.0      0      0            315154   8.6625   \n",
       "4           896       3    1  1.0      1      1           3101298  12.2875   \n",
       "5           897       3    0  0.0      0      0              7538   9.2250   \n",
       "6           898       3    1  2.0      0      0            330972   7.6292   \n",
       "7           899       2    0  1.0      1      1            248738  29.0000   \n",
       "8           900       3    1  1.0      0      0              2657   7.2292   \n",
       "9           901       3    0  1.0      2      0         A/4 48871  24.1500   \n",
       "10          902       3    0  2.0      0      0            349220   7.8958   \n",
       "11          903       1    0  3.0      0      0               694  26.0000   \n",
       "12          904       1    1  1.0      1      0             21228  82.2667   \n",
       "13          905       2    0  4.0      1      0             24065  26.0000   \n",
       "14          906       1    1  3.0      1      0       W.E.P. 5734  61.1750   \n",
       "15          907       2    1  1.0      1      0     SC/PARIS 2167  27.7208   \n",
       "16          908       2    0  2.0      0      0            233734  12.3500   \n",
       "17          909       3    0  1.0      0      0              2692   7.2250   \n",
       "18          910       3    1  2.0      1      0  STON/O2. 3101270   7.9250   \n",
       "19          911       3    1  3.0      0      0              2696   7.2250   \n",
       "\n",
       "   Cabin Embarked  Title  \n",
       "0    NaN        Q      0  \n",
       "1    NaN        S      2  \n",
       "2    NaN        Q      0  \n",
       "3    NaN        S      0  \n",
       "4    NaN        S      2  \n",
       "5    NaN        S      0  \n",
       "6    NaN        Q      1  \n",
       "7    NaN        S      0  \n",
       "8    NaN        C      2  \n",
       "9    NaN        S      0  \n",
       "10   NaN        S      0  \n",
       "11   NaN        S      0  \n",
       "12   B45        S      2  \n",
       "13   NaN        S      0  \n",
       "14   E31        S      2  \n",
       "15   NaN        C      2  \n",
       "16   NaN        Q      0  \n",
       "17   NaN        C      0  \n",
       "18   NaN        S      1  \n",
       "19   NaN        C      2  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0ca9f0e7",
   "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   Sex          418 non-null    int64  \n",
      " 3   Age          418 non-null    float64\n",
      " 4   SibSp        418 non-null    int64  \n",
      " 5   Parch        418 non-null    int64  \n",
      " 6   Ticket       418 non-null    object \n",
      " 7   Fare         417 non-null    float64\n",
      " 8   Cabin        91 non-null     object \n",
      " 9   Embarked     418 non-null    object \n",
      " 10  Title        418 non-null    int64  \n",
      "dtypes: float64(2), int64(6), object(3)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "38690913",
   "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('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c778935b",
   "metadata": {},
   "source": [
    "### 4.5 Embarked"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8049ee21",
   "metadata": {},
   "source": [
    "#### 4.5.1 filling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9e6257bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 69,
     "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": "markdown",
   "id": "c1263c1b",
   "metadata": {},
   "source": [
    "more than 50% of 1st class are from S embark  \n",
    "more than 50% of 2nd class are from S embark  \n",
    "more than 50% of 3rd class are from S embark\n",
    "\n",
    "**fill out missing embark with S embark**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1be53ff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a7488821",
   "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>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>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>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>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>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  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   7.2500   NaN        S      0  \n",
       "1  71.2833   C85        C      2  \n",
       "2   7.9250   NaN        S      1  \n",
       "3  53.1000  C123        S      2  \n",
       "4   8.0500   NaN        S      0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3f818f99",
   "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": 73,
   "id": "be8b3e6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>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",
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       "      <td>3</td>\n",
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       "      <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>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>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>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>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",
       "  </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   7.2500   NaN         0      0  \n",
       "1  71.2833   C85         1      2  \n",
       "2   7.9250   NaN         0      1  \n",
       "3  53.1000  C123         0      2  \n",
       "4   8.0500   NaN         0      0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f40c1c3a",
   "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   Sex          891 non-null    int64  \n",
      " 4   Age          891 non-null    float64\n",
      " 5   SibSp        891 non-null    int64  \n",
      " 6   Parch        891 non-null    int64  \n",
      " 7   Ticket       891 non-null    object \n",
      " 8   Fare         891 non-null    float64\n",
      " 9   Cabin        204 non-null    object \n",
      " 10  Embarked     891 non-null    int64  \n",
      " 11  Title        891 non-null    int64  \n",
      "dtypes: float64(2), int64(8), object(2)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "27f26655",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>1</th>\n",
       "      <td>893</td>\n",
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       "      <td>363272</td>\n",
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       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>895</td>\n",
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       "      <td>2.0</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  SibSp  Parch   Ticket     Fare Cabin  \\\n",
       "0          892       3    0  2.0      0      0   330911   7.8292   NaN   \n",
       "1          893       3    1  3.0      1      0   363272   7.0000   NaN   \n",
       "2          894       2    0  3.0      0      0   240276   9.6875   NaN   \n",
       "3          895       3    0  2.0      0      0   315154   8.6625   NaN   \n",
       "4          896       3    1  1.0      1      1  3101298  12.2875   NaN   \n",
       "\n",
       "   Embarked  Title  \n",
       "0         2      0  \n",
       "1         0      2  \n",
       "2         2      0  \n",
       "3         0      0  \n",
       "4         0      2  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6dfa2b2c",
   "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   Sex          418 non-null    int64  \n",
      " 3   Age          418 non-null    float64\n",
      " 4   SibSp        418 non-null    int64  \n",
      " 5   Parch        418 non-null    int64  \n",
      " 6   Ticket       418 non-null    object \n",
      " 7   Fare         417 non-null    float64\n",
      " 8   Cabin        91 non-null     object \n",
      " 9   Embarked     418 non-null    int64  \n",
      " 10  Title        418 non-null    int64  \n",
      "dtypes: float64(2), int64(7), object(2)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a847456",
   "metadata": {},
   "source": [
    "### 4.6 Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "85969fb3",
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d66178f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8.0500</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>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>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",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</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",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>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>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>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>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>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",
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       "      <td>1</td>\n",
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       "      <td>350406</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
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       "      <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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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  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",
       "5             6         0       3    0  2.0      0      0            330877   \n",
       "6             7         0       1    0  3.0      0      0             17463   \n",
       "7             8         0       3    0  0.0      3      1            349909   \n",
       "8             9         1       3    1  2.0      0      2            347742   \n",
       "9            10         1       2    1  0.0      1      0            237736   \n",
       "10           11         1       3    1  0.0      1      1           PP 9549   \n",
       "11           12         1       1    1  3.0      0      0            113783   \n",
       "12           13         0       3    0  1.0      0      0         A/5. 2151   \n",
       "13           14         0       3    0  3.0      1      5            347082   \n",
       "14           15         0       3    1  0.0      0      0            350406   \n",
       "15           16         1       2    1  3.0      0      0            248706   \n",
       "16           17         0       3    0  0.0      4      1            382652   \n",
       "17           18         1       2    0  2.0      0      0            244373   \n",
       "18           19         0       3    1  2.0      1      0            345763   \n",
       "19           20         1       3    1  2.0      0      0              2649   \n",
       "20           21         0       2    0  2.0      0      0            239865   \n",
       "21           22         1       2    0  2.0      0      0            248698   \n",
       "22           23         1       3    1  0.0      0      0            330923   \n",
       "23           24         1       1    0  2.0      0      0            113788   \n",
       "24           25         0       3    1  0.0      3      1            349909   \n",
       "25           26         1       3    1  3.0      1      5            347077   \n",
       "26           27         0       3    0  2.0      0      0              2631   \n",
       "27           28         0       1    0  1.0      3      2             19950   \n",
       "28           29         1       3    1  1.0      0      0            330959   \n",
       "29           30         0       3    0  2.0      0      0            349216   \n",
       "30           31         0       1    0  3.0      0      0          PC 17601   \n",
       "31           32         1       1    1  2.0      1      0          PC 17569   \n",
       "32           33         1       3    1  1.0      0      0            335677   \n",
       "33           34         0       2    0  4.0      0      0        C.A. 24579   \n",
       "34           35         0       1    0  2.0      1      0          PC 17604   \n",
       "35           36         0       1    0  3.0      1      0            113789   \n",
       "36           37         1       3    0  2.0      0      0              2677   \n",
       "37           38         0       3    0  1.0      0      0        A./5. 2152   \n",
       "38           39         0       3    1  1.0      2      0            345764   \n",
       "39           40         1       3    1  0.0      1      0              2651   \n",
       "40           41         0       3    1  3.0      1      0              7546   \n",
       "41           42         0       2    1  2.0      1      0             11668   \n",
       "42           43         0       3    0  2.0      0      0            349253   \n",
       "43           44         1       2    1  0.0      1      2     SC/Paris 2123   \n",
       "44           45         1       3    1  1.0      0      0            330958   \n",
       "45           46         0       3    0  2.0      0      0   S.C./A.4. 23567   \n",
       "46           47         0       3    0  2.0      1      0            370371   \n",
       "47           48         1       3    1  1.0      0      0             14311   \n",
       "48           49         0       3    0  2.0      2      0              2662   \n",
       "49           50         0       3    1  1.0      1      0            349237   \n",
       "\n",
       "        Fare        Cabin  Embarked  Title  \n",
       "0     7.2500          NaN         0      0  \n",
       "1    71.2833          C85         1      2  \n",
       "2     7.9250          NaN         0      1  \n",
       "3    53.1000         C123         0      2  \n",
       "4     8.0500          NaN         0      0  \n",
       "5     8.4583          NaN         2      0  \n",
       "6    51.8625          E46         0      0  \n",
       "7    21.0750          NaN         0      3  \n",
       "8    11.1333          NaN         0      2  \n",
       "9    30.0708          NaN         1      2  \n",
       "10   16.7000           G6         0      1  \n",
       "11   26.5500         C103         0      1  \n",
       "12    8.0500          NaN         0      0  \n",
       "13   31.2750          NaN         0      0  \n",
       "14    7.8542          NaN         0      1  \n",
       "15   16.0000          NaN         0      2  \n",
       "16   29.1250          NaN         2      3  \n",
       "17   13.0000          NaN         0      0  \n",
       "18   18.0000          NaN         0      2  \n",
       "19    7.2250          NaN         1      2  \n",
       "20   26.0000          NaN         0      0  \n",
       "21   13.0000          D56         0      0  \n",
       "22    8.0292          NaN         2      1  \n",
       "23   35.5000           A6         0      0  \n",
       "24   21.0750          NaN         0      1  \n",
       "25   31.3875          NaN         0      2  \n",
       "26    7.2250          NaN         1      0  \n",
       "27  263.0000  C23 C25 C27         0      0  \n",
       "28    7.8792          NaN         2      1  \n",
       "29    7.8958          NaN         0      0  \n",
       "30   27.7208          NaN         1      3  \n",
       "31  146.5208          B78         1      2  \n",
       "32    7.7500          NaN         2      1  \n",
       "33   10.5000          NaN         0      0  \n",
       "34   82.1708          NaN         1      0  \n",
       "35   52.0000          NaN         0      0  \n",
       "36    7.2292          NaN         1      0  \n",
       "37    8.0500          NaN         0      0  \n",
       "38   18.0000          NaN         0      1  \n",
       "39   11.2417          NaN         1      1  \n",
       "40    9.4750          NaN         0      2  \n",
       "41   21.0000          NaN         0      2  \n",
       "42    7.8958          NaN         1      0  \n",
       "43   41.5792          NaN         1      1  \n",
       "44    7.8792          NaN         2      1  \n",
       "45    8.0500          NaN         0      0  \n",
       "46   15.5000          NaN         2      0  \n",
       "47    7.7500          NaN         2      1  \n",
       "48   21.6792          NaN         1      0  \n",
       "49   17.8000          NaN         0      2  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a1a54d34",
   "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   Sex          891 non-null    int64  \n",
      " 4   Age          891 non-null    float64\n",
      " 5   SibSp        891 non-null    int64  \n",
      " 6   Parch        891 non-null    int64  \n",
      " 7   Ticket       891 non-null    object \n",
      " 8   Fare         891 non-null    float64\n",
      " 9   Cabin        204 non-null    object \n",
      " 10  Embarked     891 non-null    int64  \n",
      " 11  Title        891 non-null    int64  \n",
      "dtypes: float64(2), int64(8), object(2)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d78fadc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  SibSp  Parch   Ticket     Fare Cabin  \\\n",
       "0          892       3    0  2.0      0      0   330911   7.8292   NaN   \n",
       "1          893       3    1  3.0      1      0   363272   7.0000   NaN   \n",
       "2          894       2    0  3.0      0      0   240276   9.6875   NaN   \n",
       "3          895       3    0  2.0      0      0   315154   8.6625   NaN   \n",
       "4          896       3    1  1.0      1      1  3101298  12.2875   NaN   \n",
       "\n",
       "   Embarked  Title  \n",
       "0         2      0  \n",
       "1         0      2  \n",
       "2         2      0  \n",
       "3         0      0  \n",
       "4         0      2  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "643227f5",
   "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   Sex          418 non-null    int64  \n",
      " 3   Age          418 non-null    float64\n",
      " 4   SibSp        418 non-null    int64  \n",
      " 5   Parch        418 non-null    int64  \n",
      " 6   Ticket       418 non-null    object \n",
      " 7   Fare         418 non-null    float64\n",
      " 8   Cabin        91 non-null     object \n",
      " 9   Embarked     418 non-null    int64  \n",
      " 10  Title        418 non-null    int64  \n",
      "dtypes: float64(2), int64(7), object(2)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "273f680f",
   "metadata": {},
   "source": [
    "### Fare Chart ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ffc559f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 919.55x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    " \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e61776b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 20.0)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 919.55x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    "\n",
    "plt.xlim(0, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "fa0b2108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 30.0)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 919.55x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    "\n",
    "plt.xlim(0, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9a12b8ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 30.0)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 919.55x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    "\n",
    "plt.xlim(0, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8fe94084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 512.3292)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 919.55x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Fare',shade= True)\n",
    "facet.set(xlim=(0, train['Fare'].max()))\n",
    "facet.add_legend()\n",
    "\n",
    "plt.xlim(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e6ce2c1c",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "0606c428",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>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",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <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>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
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       "      <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>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>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   C85         1      2  \n",
       "2   0.0   NaN         0      1  \n",
       "3   2.0  C123         0      2  \n",
       "4   0.0   NaN         0      0  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "75bf601d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  SibSp  Parch   Ticket  Fare Cabin  Embarked  \\\n",
       "0          892       3    0  2.0      0      0   330911   0.0   NaN         2   \n",
       "1          893       3    1  3.0      1      0   363272   0.0   NaN         0   \n",
       "2          894       2    0  3.0      0      0   240276   0.0   NaN         2   \n",
       "3          895       3    0  2.0      0      0   315154   0.0   NaN         0   \n",
       "4          896       3    1  1.0      1      1  3101298   0.0   NaN         0   \n",
       "\n",
       "   Title  \n",
       "0      0  \n",
       "1      2  \n",
       "2      0  \n",
       "3      0  \n",
       "4      2  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde3f436",
   "metadata": {},
   "source": [
    "### 4.7 Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ad12f2d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G6             4\n",
       "C23 C25 C27    4\n",
       "B96 B98        4\n",
       "C22 C26        3\n",
       "F2             3\n",
       "              ..\n",
       "E31            1\n",
       "C82            1\n",
       "A34            1\n",
       "B19            1\n",
       "E50            1\n",
       "Name: Cabin, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Cabin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "62a11c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in train_test_data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].str[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "418e2b8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 92,
     "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]['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",
    "\n",
    "df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "30b96cfb",
   "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": 94,
   "id": "4e6d10be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 95,
   "id": "6c2e87b3",
   "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>2.0</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>0.8</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>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>373450</td>\n",
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       "      <td>2.0</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    2.0         0      0  \n",
       "1   2.0    0.8         1      2  \n",
       "2   0.0    2.0         0      1  \n",
       "3   2.0    0.8         0      2  \n",
       "4   0.0    2.0         0      0  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "58ce2096",
   "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   Sex          891 non-null    int64  \n",
      " 4   Age          891 non-null    float64\n",
      " 5   SibSp        891 non-null    int64  \n",
      " 6   Parch        891 non-null    int64  \n",
      " 7   Ticket       891 non-null    object \n",
      " 8   Fare         891 non-null    float64\n",
      " 9   Cabin        891 non-null    float64\n",
      " 10  Embarked     891 non-null    int64  \n",
      " 11  Title        891 non-null    int64  \n",
      "dtypes: float64(3), int64(8), object(1)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6fcfd500",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  SibSp  Parch   Ticket  Fare  Cabin  \\\n",
       "0          892       3    0  2.0      0      0   330911   0.0    2.0   \n",
       "1          893       3    1  3.0      1      0   363272   0.0    2.0   \n",
       "2          894       2    0  3.0      0      0   240276   0.0    2.0   \n",
       "3          895       3    0  2.0      0      0   315154   0.0    2.0   \n",
       "4          896       3    1  1.0      1      1  3101298   0.0    2.0   \n",
       "\n",
       "   Embarked  Title  \n",
       "0         2      0  \n",
       "1         0      2  \n",
       "2         2      0  \n",
       "3         0      0  \n",
       "4         0      2  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c38505dd",
   "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   Sex          418 non-null    int64  \n",
      " 3   Age          418 non-null    float64\n",
      " 4   SibSp        418 non-null    int64  \n",
      " 5   Parch        418 non-null    int64  \n",
      " 6   Ticket       418 non-null    object \n",
      " 7   Fare         418 non-null    float64\n",
      " 8   Cabin        418 non-null    float64\n",
      " 9   Embarked     418 non-null    int64  \n",
      " 10  Title        418 non-null    int64  \n",
      "dtypes: float64(3), int64(7), object(1)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a32969",
   "metadata": {},
   "source": [
    "### 4.8 FamilySize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "007a154f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"FamilySize\"] = train[\"SibSp\"] + train[\"Parch\"] + 1\n",
    "test[\"FamilySize\"] = test[\"SibSp\"] + test[\"Parch\"] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2345ab0a",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "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           2  \n",
       "1   2.0    0.8         1      2           2  \n",
       "2   0.0    2.0         0      1           1  \n",
       "3   2.0    0.8         0      2           2  \n",
       "4   0.0    2.0         0      0           1  "
      ]
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     "execution_count": 100,
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    "train.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d03b4cca",
   "metadata": {},
   "outputs": [
    {
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       "   PassengerId  Pclass  Sex  Age  SibSp  Parch   Ticket  Fare  Cabin  \\\n",
       "0          892       3    0  2.0      0      0   330911   0.0    2.0   \n",
       "1          893       3    1  3.0      1      0   363272   0.0    2.0   \n",
       "2          894       2    0  3.0      0      0   240276   0.0    2.0   \n",
       "3          895       3    0  2.0      0      0   315154   0.0    2.0   \n",
       "4          896       3    1  1.0      1      1  3101298   0.0    2.0   \n",
       "\n",
       "   Embarked  Title  FamilySize  \n",
       "0         2      0           1  \n",
       "1         0      2           2  \n",
       "2         2      0           1  \n",
       "3         0      0           1  \n",
       "4         0      2           3  "
      ]
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     "execution_count": 101,
     "metadata": {},
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   "source": [
    "test.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "3b4587a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 11.0)"
      ]
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     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 919.55x216 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": 103,
   "id": "57f30859",
   "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": 104,
   "id": "30c574bb",
   "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": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b59b46a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
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       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</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>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</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>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</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>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  SibSp  Parch   Ticket  Fare  Cabin  \\\n",
       "0          892       3    0  2.0      0      0   330911   0.0    2.0   \n",
       "1          893       3    1  3.0      1      0   363272   0.0    2.0   \n",
       "2          894       2    0  3.0      0      0   240276   0.0    2.0   \n",
       "3          895       3    0  2.0      0      0   315154   0.0    2.0   \n",
       "4          896       3    1  1.0      1      1  3101298   0.0    2.0   \n",
       "\n",
       "   Embarked  Title  FamilySize  \n",
       "0         2      0         0.0  \n",
       "1         0      2         0.4  \n",
       "2         2      0         0.0  \n",
       "3         0      0         0.0  \n",
       "4         0      2         0.8  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "816882dc",
   "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": 107,
   "id": "5af4ca1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,))"
      ]
     },
     "execution_count": 107,
     "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": 108,
   "id": "f5712809",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 9)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0fb23b28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 9)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "b387909f",
   "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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a1ae67",
   "metadata": {},
   "source": [
    "## 5. Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b5dcf032",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Classifier Modules\n",
    "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": 112,
   "id": "4f49e846",
   "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": 113,
   "id": "dd31f0a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Sex          418 non-null    int64  \n",
      " 3   Age          418 non-null    float64\n",
      " 4   Fare         418 non-null    float64\n",
      " 5   Cabin        418 non-null    float64\n",
      " 6   Embarked     418 non-null    int64  \n",
      " 7   Title        418 non-null    int64  \n",
      " 8   FamilySize   418 non-null    float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 29.5 KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5963edc1",
   "metadata": {},
   "source": [
    "### 6.2 Cross Validation (K-fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "576793f5",
   "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": "markdown",
   "id": "3d08744b",
   "metadata": {},
   "source": [
    "### 6.2.1 kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "e5212cab",
   "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": 116,
   "id": "bef67ae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.6"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kNN Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dcea915",
   "metadata": {},
   "source": [
    "### 6.2.2 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "19df1dfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.76666667 0.80898876 0.7752809  0.76404494 0.88764045 0.7752809\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": 118,
   "id": "a6bb224f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.58"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# decision tree Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3813feff",
   "metadata": {},
   "source": [
    "### 6.2.3 Ramdom Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "75fb6667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.77777778 0.82022472 0.79775281 0.78651685 0.85393258 0.80898876\n",
      " 0.79775281 0.82022472 0.76404494 0.78651685]\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": 120,
   "id": "2f7a90ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.14"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd450ef",
   "metadata": {},
   "source": [
    "### 6.2.4 Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "8df8c46e",
   "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": 122,
   "id": "359dde53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.78"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Naive Bayes Score\n",
    "round(np.mean(score)*100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d89a896",
   "metadata": {},
   "source": [
    "### 6.2.5 SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c001b433",
   "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": 124,
   "id": "254ff455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.5"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(np.mean(score)*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8f2257",
   "metadata": {},
   "source": [
    "## 7. Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "d39d02de",
   "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": 126,
   "id": "3e952940",
   "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": 127,
   "id": "6ebaa8c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
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       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
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       "    </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",
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      ],
      "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": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv('submission.csv')\n",
    "submission.head()"
   ]
  }
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