Titanic Data Anaysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "forked from https://www.kaggle.com/startupsci/titanic-data-science-solutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Acquirung Data\n",
    "train_df = pd.read_csv('train.csv')\n",
    "test_df = pd.read_csv('test.csv')\n",
    "combine = [train_df, test_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking which features are available\n",
    "train_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# categorical features\n",
    "nominal = survived, sex, embarked.\n",
    "ordinal = pclass.\n",
    "\n",
    "# numerical features\n",
    "discrete = sibsp, parch.\n",
    "continous = Age, Fare.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ticket is of alpha numeric and numeric datatype.\n",
    "cabin is of alphanumeric datatype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Name feature may contain typos error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n",
      "****************************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking blank values\n",
    "print('*'*40)\n",
    "train_df.info()\n",
    "print('*'*40)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>in training data cabin >Age >Embarked have missing values</p>\n",
    "in testin data cabin > Age > Fare have missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking distribution of numerical feature\n",
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total samples are 891 out of 2224.\n",
    "38% of people survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <td>179.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.85420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>268.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.05000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.40</th>\n",
       "      <td>357.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>446.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.45420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.60</th>\n",
       "      <td>535.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.67920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>624.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.80</th>\n",
       "      <td>713.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.68750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.90</th>\n",
       "      <td>802.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>77.95830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.99</th>\n",
       "      <td>882.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.87</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>249.00622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>891.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>512.32920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass    Age  SibSp  Parch       Fare\n",
       "0.10         90.0       0.0     1.0  14.00    0.0    0.0    7.55000\n",
       "0.20        179.0       0.0     1.0  19.00    0.0    0.0    7.85420\n",
       "0.30        268.0       0.0     2.0  22.00    0.0    0.0    8.05000\n",
       "0.40        357.0       0.0     2.0  25.00    0.0    0.0   10.50000\n",
       "0.50        446.0       0.0     3.0  28.00    0.0    0.0   14.45420\n",
       "0.60        535.0       0.0     3.0  31.80    0.0    0.0   21.67920\n",
       "0.70        624.0       1.0     3.0  36.00    1.0    0.0   27.00000\n",
       "0.80        713.0       1.0     3.0  41.00    1.0    1.0   39.68750\n",
       "0.90        802.0       1.0     3.0  50.00    1.0    2.0   77.95830\n",
       "0.99        882.1       1.0     3.0  65.87    5.0    4.0  249.00622\n",
       "1.00        891.0       1.0     3.0  80.00    8.0    6.0  512.32920"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.quantile([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only around 20% of people have first class ticket\n",
    "less than 1% people were between 65 and 80\n",
    "around 60% of the people were not without siblings or spouse\n",
    "around 30% of the people were with parent or children\n",
    "less than 1% people paid for fare more than 249$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Name   Sex    Ticket Cabin Embarked\n",
       "count                                   891   891       891   204      889\n",
       "unique                                  891     2       681   147        3\n",
       "top     Nasser, Mrs. Nicholas (Adele Achem)  male  CA. 2343    G6        S\n",
       "freq                                      1   577         7     4      644"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking distribution of categorical feature\n",
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Name are unique across dataset.\n",
    "sex has 2 unique values male and female, with male being 577/891. \n",
    "ticket has duplicates values around (891-681)/891.\n",
    "Cabin have duplicates values.\n",
    "embarked has 3 possible values with S being the highest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "to conform our observation and assumption we do pivoting of eatires against each order. it is done for those fearures whihc are complete and are catagorical(sex), ordinal(pclass) or Discrete(Parch, Sibsp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Survived', 'Sex']].groupby(['Sex'], as_index = False).mean().sort_values(by = 'Survived', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it seems that gender feature have a good coorelation with survived feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "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>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Survived', 'Pclass']].groupby(['Pclass'], as_index = False).mean().sort_values(by = 'Survived', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it seems that Pclass have a good coorelation with survived feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "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>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Survived', 'Parch']].groupby(['Parch'], as_index = False).mean().sort_values(by = 'Survived', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "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>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Survived', 'SibSp']].groupby(['SibSp'], as_index = False).mean().sort_values(by = 'Survived', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for SibSp and Parch some categories does not have any correlation with survived feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26fc3b4a390>"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing numerical features\n",
    "g = sns.FacetGrid(train_df, col = 'Survived')\n",
    "g.map(plt.hist,'Age', bins = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it seems that children and adult have high survival rate. and most of the audience was in between 20 and 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26fc3c72780>"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col = 'Survived')\n",
    "g.map(plt.hist, 'Fare', bins = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it does not give much info but it seems that those with high fare survived"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also combine multiple features to find correlation using a single plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musharraf\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26fc3d7b630>"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col = 'Survived', row = 'Pclass', size = 2.2, aspect = 1.6)\n",
    "g.map(plt.hist, 'Age', bins = 40)\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musharraf\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:715: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "C:\\Users\\Musharraf\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:720: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "C:\\Users\\Musharraf\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26fc41b9128>"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.5x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, row = 'Embarked', height = 5)\n",
    "g.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette = 'deep')\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26fc454cb00>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col = 'Embarked', row = 'Sex')\n",
    "g.map(plt.hist, 'Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musharraf\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:715: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26fc49ef908>"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 440x648 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col = 'Survived', row = 'Embarked')\n",
    "g.map(sns.barplot, 'Sex', 'Fare' )\n",
    "g.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it seems that people with higher fare have high survival rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12) (418, 11)\n",
      "(891, 10) (418, 9)\n"
     ]
    }
   ],
   "source": [
    "#Wrangling data\n",
    "#dropping the ticket and Cabin feature #Correcting\n",
    "print(train_df.shape, test_df.shape)\n",
    "train_df = train_df.drop(['Ticket', 'Cabin',], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "print(train_df.shape, test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating new feature\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Name'].str.extract('([A-Za-z]+)\\.', expand = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "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",
       "    .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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Capt</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Col</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Countess</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Don</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dr</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jonkheer</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lady</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Major</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.697802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mlle</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mme</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.792000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ms</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Rev</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Sir</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Title  Survived\n",
       "0       Capt  0.000000\n",
       "1        Col  0.500000\n",
       "2   Countess  1.000000\n",
       "3        Don  0.000000\n",
       "4         Dr  0.428571\n",
       "5   Jonkheer  0.000000\n",
       "6       Lady  1.000000\n",
       "7      Major  0.500000\n",
       "8     Master  0.575000\n",
       "9       Miss  0.697802\n",
       "10      Mlle  1.000000\n",
       "11       Mme  1.000000\n",
       "12        Mr  0.156673\n",
       "13       Mrs  0.792000\n",
       "14        Ms  1.000000\n",
       "15       Rev  0.000000\n",
       "16       Sir  1.000000"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine[0][['Survived', 'Title']].groupby('Title', as_index = False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          517\n",
       "Miss        182\n",
       "Mrs         125\n",
       "Master       40\n",
       "Dr            7\n",
       "Rev           6\n",
       "Mlle          2\n",
       "Col           2\n",
       "Major         2\n",
       "Sir           1\n",
       "Capt          1\n",
       "Jonkheer      1\n",
       "Ms            1\n",
       "Lady          1\n",
       "Mme           1\n",
       "Don           1\n",
       "Countess      1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].str.replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].str.replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].str.replace('Mme', 'Mrs')\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
    " \t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "3     Mrs  0.793651\n",
       "1    Miss  0.702703\n",
       "0  Master  0.575000\n",
       "4    Rare  0.347826\n",
       "2      Mr  0.156673"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Title', 'Survived']].groupby('Title', as_index = False).mean().sort_values(by = 'Survived', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_mapping = {'Mr':1, \"Miss\":2, \"Mrs\": 3,\"Master\" :4, 'Rare':5}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>Title</th>\n",
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       "  <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>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</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>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</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>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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     Fare Embarked  Title  \n",
       "0      0   7.2500        S      1  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      1  "
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Sex</th>\n",
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       "    <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>7.8292</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>7.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</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>8.6625</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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>12.2875</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch     Fare Embarked  Title  \n",
       "0  34.5      0      0   7.8292        Q      1  \n",
       "1  47.0      1      0   7.0000        S      3  \n",
       "2  62.0      0      0   9.6875        Q      1  \n",
       "3  27.0      0      0   8.6625        S      1  \n",
       "4  22.0      1      1  12.2875        S      3  "
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(['Name', 'PassengerId'], axis = 1)\n",
    "test_df = test_df.drop(['Name', 'PassengerId'], axis = 1)\n",
    "combine = [train_df, test_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    male  22.0      1      0   7.2500        S      1\n",
       "1         1       1  female  38.0      1      0  71.2833        C      3\n",
       "2         1       3  female  26.0      0      0   7.9250        S      2\n",
       "3         1       1  female  35.0      1      0  53.1000        S      3\n",
       "4         0       3    male  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>S</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Pclass     Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0       3    male  34.5      0      0   7.8292        Q      1\n",
       "1       3  female  47.0      1      0   7.0000        S      3\n",
       "2       2    male  62.0      0      0   9.6875        Q      1\n",
       "3       3    male  27.0      0      0   8.6625        S      1\n",
       "4       3  female  22.0      1      1  12.2875        S      3"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine[1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting categorical to numerical\n",
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map({'female':1, 'male':0}).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      1\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# completing a numerical age feature\n",
    "More accurate way of guessing missing values is to use other correlated features. In our case we note correlation among Age, Gender, and Pclass. Guess Age values using median values for Age across sets of Pclass and Gender feature combinations. So, median Age for Pclass=1 and Gender=0, Pclass=1 and Gender=1, and so on..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x26fc5058358>"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x648 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row = 'Pclass', col = 'Sex')\n",
    "grid.map(plt.hist, 'Age', bins = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us start by preparing an empty array to contain guessed Age values based on Pclass x Gender combinations.\n",
    "\n",
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0,2):\n",
    "        for j in range(0,3):\n",
    "            guess_df = dataset[(dataset['Sex']==i) & (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "             \n",
    "            age_guess = guess_df.median()\n",
    "            guess_ages[i, j] = int(age_guess/0.5 + 0.5) *0.5\n",
    "            \n",
    "    for i in range(0,2):\n",
    "        for j in range(0,3):\n",
    "            dataset.loc[(dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1), 'Age'] = guess_ages[i, j]\n",
    "            \n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    891\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Age'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    418\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Age'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "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>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let us create agebands and determine correlations between them\n",
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "train_df[['AgeBand', 'Survived']].groupby('AgeBand', as_index = False)['Survived'].mean().sort_values(by = 'AgeBand')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let us age replace age with ordinals based on this band\n",
    "for dataset in combine:\n",
    "    dataset.loc[dataset['Age'] <=16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] >16) &( dataset['Age'] <=32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] >32) & (dataset['Age'] <=48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] >48) & (dataset['Age'] <=64), 'Age'] = 3\n",
    "    dataset.loc[dataset['Age'] > 64, 'Age'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    495\n",
       "2    216\n",
       "0    100\n",
       "3     69\n",
       "5     11\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeBand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0         0       3    0    1      1      0   7.2500        S      1   \n",
       "1         1       1    1    2      1      0  71.2833        C      3   \n",
       "2         1       3    1    1      0      0   7.9250        S      2   \n",
       "3         1       1    1    2      1      0  53.1000        S      3   \n",
       "4         0       3    0    2      0      0   8.0500        S      1   \n",
       "\n",
       "        AgeBand  \n",
       "0  (16.0, 32.0]  \n",
       "1  (32.0, 48.0]  \n",
       "2  (16.0, 32.0]  \n",
       "3  (32.0, 48.0]  \n",
       "4  (32.0, 48.0]  "
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
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       "      <th>2</th>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      1\n",
       "1         1       1    1    2      1      0  71.2833        C      3\n",
       "2         1       3    1    1      0      0   7.9250        S      2\n",
       "3         1       1    1    2      1      0  53.1000        S      3\n",
       "4         0       3    0    2      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis = 1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
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       "      <th>6</th>\n",
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       "      <td>0.333333</td>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
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       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can create a new feature for FamilySize which combines Parch and SibSp. This will enable us to drop Parch and SibSp from our datasets.\n",
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] +dataset['Parch'] + 1\n",
    "    \n",
    "train_df[['FamilySize', 'Survived']].groupby('FamilySize', as_index = False)['Survived'].mean().sort_values(by = 'Survived'\\\n",
    "                                                                                                           ,ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>IsAlone</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.505650</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can create another feature called IsAlone.\n",
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize']==1, 'IsAlone'] = 1\n",
    "    \n",
    "train_df[['IsAlone', 'Survived']].groupby('IsAlone', as_index = False)['Survived'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare Embarked  Title  IsAlone\n",
       "0         0       3    0    1   7.2500        S      1        0\n",
       "1         1       1    1    2  71.2833        C      3        0\n",
       "2         1       3    1    1   7.9250        S      2        1\n",
       "3         1       1    1    2  53.1000        S      3        0\n",
       "4         0       3    0    2   8.0500        S      1        1"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us drop Parch, SibSp, and FamilySize features in favor of IsAlone.\n",
    "train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis = 1)\n",
    "test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis = 1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age*Class</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age*Class  Age  Pclass\n",
       "0          3    1       3\n",
       "1          2    2       1\n",
       "2          3    1       3\n",
       "3          2    2       1\n",
       "4          6    2       3"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can also create an artificial feature combining Pclass and Age.\n",
    "\n",
    "for dataset in combine:\n",
    "    dataset['Age*Class'] = dataset['Age']*dataset['Pclass']\n",
    "    \n",
    "train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Embarked feature takes S, Q, C values based on port of embarkation. Our training dataset has two missing values. \n",
    "#We simply fill these with the most common occurance.\n",
    "freq_port = train_df['Embarked'].dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "    \n",
    "train_df[['Embarked', 'Survived']].groupby('Embarked', as_index = False)['Survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1   7.2500         0      1        0          3\n",
       "1         1       1    1    2  71.2833         2      3        0          2\n",
       "2         1       3    1    1   7.9250         0      2        1          3\n",
       "3         1       1    1    2  53.1000         0      3        0          2\n",
       "4         0       3    0    2   8.0500         0      1        1          6"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now we can convert embarked feature to numeric feature\n",
    "\n",
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map({'S':0, 'Q':1, 'C':2})\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "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",
      "Survived     891 non-null int64\n",
      "Pclass       891 non-null int64\n",
      "Sex          891 non-null int32\n",
      "Age          891 non-null int32\n",
      "Fare         891 non-null float64\n",
      "Embarked     891 non-null int64\n",
      "Title        891 non-null int64\n",
      "IsAlone      891 non-null int64\n",
      "Age*Class    891 non-null int64\n",
      "dtypes: float64(1), int32(2), int64(6)\n",
      "memory usage: 55.8 KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 8 columns):\n",
      "Pclass       418 non-null int64\n",
      "Sex          418 non-null int32\n",
      "Age          418 non-null int32\n",
      "Fare         417 non-null float64\n",
      "Embarked     418 non-null int64\n",
      "Title        418 non-null int64\n",
      "IsAlone      418 non-null int64\n",
      "Age*Class    418 non-null int64\n",
      "dtypes: float64(1), int32(2), int64(5)\n",
      "memory usage: 22.9 KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 8 columns):\n",
      "Pclass       418 non-null int64\n",
      "Sex          418 non-null int32\n",
      "Age          418 non-null int32\n",
      "Fare         418 non-null float64\n",
      "Embarked     418 non-null int64\n",
      "Title        418 non-null int64\n",
      "IsAlone      418 non-null int64\n",
      "Age*Class    418 non-null int64\n",
      "dtypes: float64(1), int32(2), int64(5)\n",
      "memory usage: 22.9 KB\n"
     ]
    }
   ],
   "source": [
    "#completing the Fare featuer of test dataset\n",
    "\n",
    "test_df['Fare'].fillna(train_df['Fare'].dropna().median(), inplace = True)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>7.8292</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>3</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0       3    0    2   7.8292         1      1        1          6\n",
       "1       3    1    2   7.0000         0      3        0          6\n",
       "2       2    0    3   9.6875         1      1        1          6\n",
       "3       3    0    1   8.6625         0      1        1          3\n",
       "4       3    1    1  12.2875         0      3        0          3"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FareBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.001, 7.91]</td>\n",
       "      <td>0.197309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0.303571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(14.454, 31.0]</td>\n",
       "      <td>0.454955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FareBand  Survived\n",
       "0   (-0.001, 7.91]  0.197309\n",
       "1   (7.91, 14.454]  0.303571\n",
       "2   (14.454, 31.0]  0.454955\n",
       "3  (31.0, 512.329]  0.581081"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now creating Fare Band\n",
    "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "train_df[['FareBand', 'Survived']].groupby('FareBand', as_index = False)['Survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1     0         0      1        0          3\n",
       "1         1       1    1    2     3         2      3        0          2\n",
       "2         1       3    1    1     1         0      2        1          3\n",
       "3         1       1    1    2     3         0      3        0          2\n",
       "4         0       3    0    2     1         0      1        1          6\n",
       "5         0       3    0    1     1         1      1        1          3\n",
       "6         0       1    0    3     3         0      1        1          3\n",
       "7         0       3    0    0     2         0      4        0          0\n",
       "8         1       3    1    1     1         0      3        0          3\n",
       "9         1       2    1    0     2         2      3        0          0"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "\n",
    "train_df = train_df.drop(['FareBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "    \n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,), (418, 8))"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_df.drop(\"Survived\", axis=1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test  = test_df\n",
    "X_train.shape, Y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musharraf\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "81.26"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
    "acc_log\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "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>Features</th>\n",
       "      <th>Correlations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.214166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Title</td>\n",
       "      <td>0.427577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.284213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IsAlone</td>\n",
       "      <td>0.274276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fare</td>\n",
       "      <td>-0.050910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Age*Class</td>\n",
       "      <td>-0.083687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.362851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-1.059035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Features  Correlations\n",
       "1        Sex      2.214166\n",
       "5      Title      0.427577\n",
       "4   Embarked      0.284213\n",
       "6    IsAlone      0.274276\n",
       "3       Fare     -0.050910\n",
       "7  Age*Class     -0.083687\n",
       "2        Age     -0.362851\n",
       "0     Pclass     -1.059035"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
    "coeff_df.columns = ['Features']\n",
    "coeff_df['Correlations'] = pd.Series(logreg.coef_[0])\n",
    "coeff_df.sort_values(by = 'Correlations', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Embarked', 'Title',\n",
       "       'IsAlone', 'Age*Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musharraf\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "83.28"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support Vector Machines\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_predict = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train, Y_train)*100, 2)\n",
    "acc_svc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.61"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train)*100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.88"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gaussian Naive Bayes\n",
    "\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train)*100, 2)\n",
    "acc_gaussian\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musharraf\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:166: FutureWarning: max_iter and tol parameters have been added in Perceptron in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "78.56"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perceptron\n",
    "\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n",
    "acc_perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musharraf\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:922: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "79.57"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear SVC\n",
    "\n",
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_linear_svc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Musharraf\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:166: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "73.4"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stochastic Gradient Descent\n",
    "\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
    "acc_sgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.64"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.64"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now rank our evaluation of all the models to choose the best one for our problem. While both Decision Tree and Random Forest score the same, we choose to use Random Forest as they correct for decision trees' habit of overfitting to their training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>86.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>83.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>83.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>81.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>79.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>78.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>76.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>73.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Score\n",
       "3               Random Forest  86.64\n",
       "8               Decision Tree  86.64\n",
       "1                         KNN  83.61\n",
       "0     Support Vector Machines  83.28\n",
       "2         Logistic Regression  81.26\n",
       "7                  Linear SVC  79.57\n",
       "5                  Perceptron  78.56\n",
       "4                 Naive Bayes  76.88\n",
       "6  Stochastic Gradient Decent  73.40"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
    "              'Stochastic Gradient Decent', 'Linear SVC', \n",
    "              'Decision Tree'],\n",
    "    'Score': [acc_svc, acc_knn, acc_log, \n",
    "              acc_random_forest, acc_gaussian, acc_perceptron, \n",
    "              acc_sgd, acc_linear_svc, acc_decision_tree]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_df[\"PassengerId\"],\n",
    "        \"Survived\": Y_pred\n",
    "    })\n",
    "submission.to_csv('submission.csv', index=False)"
   ]
  }
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