Sequential Feature Selector and Clustering on Titanic Dataset (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyforest import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"~/Downloads/Titanic/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "test =pd.read_csv(\"~/Downloads/Titanic/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combining Train and Test\n",
    "combined = pd.concat([train,test], ignore_index = True, sort = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived     418\n",
       "Age          263\n",
       "Fare           1\n",
       "Cabin       1014\n",
       "Embarked       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.isnull().sum()[combined.isnull().sum()!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>61</th>\n",
       "      <td>62</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "61            62       1.0       1                        Icard, Miss. Amelie   \n",
       "829          830       1.0       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch  Ticket  Fare Cabin Embarked  \n",
       "61   female  38.0      0      0  113572  80.0   B28      NaN  \n",
       "829  female  62.0      0      0  113572  80.0   B28      NaN  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[combined.Embarked.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find Embarked\n",
    "df= pd.DataFrame(combined.groupby([\"Survived\", \"Sex\"])[\"Embarked\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123774668>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = \"barh\")\n",
    "\n",
    "# This plot shows that majority of Females who survived were from Southampton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling S in Embarked\n",
    "combined.loc[combined.Embarked.isnull(),\"Embarked\"]=\"S\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>1044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Storey, Mr. Thomas</td>\n",
       "      <td>male</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass                Name   Sex   Age  SibSp  \\\n",
       "1043         1044       NaN       3  Storey, Mr. Thomas  male  60.5      0   \n",
       "\n",
       "      Parch Ticket  Fare Cabin Embarked  \n",
       "1043      0   3701   NaN   NaN        S  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fare\n",
    "combined[combined.Fare.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12366b2e8>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets Find the Relation between Fare and PClass\n",
    "sns.distplot(combined.Fare.dropna(), color = \"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">0.0</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>110.604167</td>\n",
       "      <td>70.920264</td>\n",
       "      <td>28.7125</td>\n",
       "      <td>90.13125</td>\n",
       "      <td>151.55000</td>\n",
       "      <td>151.550000</td>\n",
       "      <td>151.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>6.969577</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>24.750000</td>\n",
       "      <td>26.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72.0</td>\n",
       "      <td>19.773093</td>\n",
       "      <td>14.572275</td>\n",
       "      <td>6.7500</td>\n",
       "      <td>8.53125</td>\n",
       "      <td>14.47915</td>\n",
       "      <td>26.075025</td>\n",
       "      <td>69.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>1</th>\n",
       "      <td>77.0</td>\n",
       "      <td>62.894910</td>\n",
       "      <td>60.052545</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>27.72080</td>\n",
       "      <td>42.40000</td>\n",
       "      <td>77.287500</td>\n",
       "      <td>263.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>91.0</td>\n",
       "      <td>19.488965</td>\n",
       "      <td>15.720548</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>11.50000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>73.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>300.0</td>\n",
       "      <td>12.204469</td>\n",
       "      <td>10.984904</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>7.75000</td>\n",
       "      <td>7.89580</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>69.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">1.0</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>1</th>\n",
       "      <td>91.0</td>\n",
       "      <td>105.978159</td>\n",
       "      <td>74.738897</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>57.48960</td>\n",
       "      <td>82.17080</td>\n",
       "      <td>134.075000</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70.0</td>\n",
       "      <td>22.288989</td>\n",
       "      <td>11.140937</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>26.250000</td>\n",
       "      <td>65.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72.0</td>\n",
       "      <td>12.464526</td>\n",
       "      <td>5.984714</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>7.75000</td>\n",
       "      <td>9.46875</td>\n",
       "      <td>15.912500</td>\n",
       "      <td>31.3875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>1</th>\n",
       "      <td>45.0</td>\n",
       "      <td>74.637320</td>\n",
       "      <td>101.091948</td>\n",
       "      <td>26.2875</td>\n",
       "      <td>29.70000</td>\n",
       "      <td>35.50000</td>\n",
       "      <td>79.200000</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.0</td>\n",
       "      <td>21.095100</td>\n",
       "      <td>9.832542</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>18.75000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>39.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.0</td>\n",
       "      <td>15.579696</td>\n",
       "      <td>15.232388</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>7.82500</td>\n",
       "      <td>8.05000</td>\n",
       "      <td>15.820850</td>\n",
       "      <td>56.4958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count        mean         std      min       25%  \\\n",
       "Survived Sex    Pclass                                                     \n",
       "0.0      female 1         3.0  110.604167   70.920264  28.7125  90.13125   \n",
       "                2         6.0   18.250000    6.969577  10.5000  13.00000   \n",
       "                3        72.0   19.773093   14.572275   6.7500   8.53125   \n",
       "         male   1        77.0   62.894910   60.052545   0.0000  27.72080   \n",
       "                2        91.0   19.488965   15.720548   0.0000  11.50000   \n",
       "                3       300.0   12.204469   10.984904   0.0000   7.75000   \n",
       "1.0      female 1        91.0  105.978159   74.738897  25.9292  57.48960   \n",
       "                2        70.0   22.288989   11.140937  10.5000  13.00000   \n",
       "                3        72.0   12.464526    5.984714   7.2250   7.75000   \n",
       "         male   1        45.0   74.637320  101.091948  26.2875  29.70000   \n",
       "                2        17.0   21.095100    9.832542  10.5000  13.00000   \n",
       "                3        47.0   15.579696   15.232388   0.0000   7.82500   \n",
       "\n",
       "                              50%         75%       max  \n",
       "Survived Sex    Pclass                                   \n",
       "0.0      female 1       151.55000  151.550000  151.5500  \n",
       "                2        17.00000   24.750000   26.0000  \n",
       "                3        14.47915   26.075025   69.5500  \n",
       "         male   1        42.40000   77.287500  263.0000  \n",
       "                2        13.00000   26.000000   73.5000  \n",
       "                3         7.89580    9.500000   69.5500  \n",
       "1.0      female 1        82.17080  134.075000  512.3292  \n",
       "                2        23.00000   26.250000   65.0000  \n",
       "                3         9.46875   15.912500   31.3875  \n",
       "         male   1        35.50000   79.200000  512.3292  \n",
       "                2        18.75000   26.000000   39.0000  \n",
       "                3         8.05000   15.820850   56.4958  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(combined.groupby([\"Survived\", \"Sex\", \"Pclass\"])[\"Fare\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Putting Median for male where the Pclass is 3\n",
    "combined.loc[(combined.Pclass ==3) & (combined.Sex==\"male\") & (combined.Fare.isnull()), \"Fare\"] = 7.89580"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'B78', 'C52', 'F E69', 'E33', 'A32', 'A19', 'C106', 'E101',\n",
       "       'C128', 'D21', 'A14', 'C95', 'C126', 'C124', 'D45', 'F38', 'B102',\n",
       "       'C47', 'C92', 'D34', 'E46', 'F E46', 'D'], dtype=object)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age\n",
    "combined[combined.Age.isnull()][\"Cabin\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6', 'C23 C25 C27',\n",
       "       'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33', 'F G73', 'E31',\n",
       "       'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101', 'F E69', 'D47',\n",
       "       'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4', 'A32', 'B4',\n",
       "       'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35', 'C87', 'B77',\n",
       "       'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19', 'B49', 'D',\n",
       "       'C22 C26', 'C106', 'C65', 'E36', 'C54', 'B57 B59 B63 B66', 'C7',\n",
       "       'E34', 'C32', 'B18', 'C124', 'C91', 'E40', 'T', 'C128', 'D37',\n",
       "       'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44', 'A34', 'C104',\n",
       "       'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14', 'B37', 'C30',\n",
       "       'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38', 'B39', 'B22',\n",
       "       'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68', 'B41', 'A20',\n",
       "       'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48', 'E58', 'C126',\n",
       "       'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63', 'C62 C64',\n",
       "       'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30', 'E121',\n",
       "       'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36', 'B102',\n",
       "       'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42', 'C148',\n",
       "       'B45', 'B36', 'A21', 'D34', 'A9', 'C31', 'B61', 'C53', 'D43',\n",
       "       'C130', 'C132', 'C55 C57', 'C116', 'F', 'A29', 'C6', 'C28', 'C51',\n",
       "       'C97', 'D22', 'B10', 'E45', 'E52', 'A11', 'B11', 'C80', 'C89',\n",
       "       'F E46', 'B26', 'F E57', 'A18', 'E60', 'E39 E41', 'B52 B54 B56',\n",
       "       'C39', 'B24', 'D40', 'D38', 'C105'], dtype=object)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Cabin.dropna().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cabin\n",
    "cabincount = ['C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6', 'C23 C25 C27',\n",
    "       'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33', 'F G73', 'E31',\n",
    "       'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101', 'F E69', 'D47',\n",
    "       'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4', 'A32', 'B4',\n",
    "       'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35', 'C87', 'B77',\n",
    "       'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19', 'B49', 'D',\n",
    "       'C22 C26', 'C106', 'C65', 'E36', 'C54', 'B57 B59 B63 B66', 'C7',\n",
    "       'E34', 'C32', 'B18', 'C124', 'C91', 'E40', 'T', 'C128', 'D37',\n",
    "       'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44', 'A34', 'C104',\n",
    "       'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14', 'B37', 'C30',\n",
    "       'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38', 'B39', 'B22',\n",
    "       'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68', 'B41', 'A20',\n",
    "       'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48', 'E58', 'C126',\n",
    "       'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63', 'C62 C64',\n",
    "       'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30', 'E121',\n",
    "       'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36', 'B102',\n",
    "       'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42', 'C148',\n",
    "       'B45', 'B36', 'A21', 'D34', 'A9', 'C31', 'B61', 'C53', 'D43',\n",
    "       'C130', 'C132', 'C55 C57', 'C116', 'F', 'A29', 'C6', 'C28', 'C51',\n",
    "       'C97', 'D22', 'B10', 'E45', 'E52', 'A11', 'B11', 'C80', 'C89',\n",
    "       'F E46', 'B26', 'F E57', 'A18', 'E60', 'E39 E41', 'B52 B54 B56',\n",
    "       'C39', 'B24', 'D40', 'D38', 'C105']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cabins(x):\n",
    "    if(x in cabincount):\n",
    "        return(\"Cabin Allotted\")\n",
    "    else:\n",
    "        return(\"No Cabin Found\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Cabin_Status\"] = pd.Series(combined.Cabin.apply(cabins))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>Cabin Allotted</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>Cabin Allotted</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "3            4       1.0       1   \n",
       "4            5       0.0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked    Cabin_Status  \n",
       "0      0         A/5 21171   7.2500   NaN        S  No Cabin Found  \n",
       "1      0          PC 17599  71.2833   C85        C  Cabin Allotted  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  No Cabin Found  \n",
       "3      0            113803  53.1000  C123        S  Cabin Allotted  \n",
       "4      0            373450   8.0500   NaN        S  No Cabin Found  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting Title from Name\n",
    "g = combined.Name.transform(lambda x:x.split(\", \")[1])\n",
    "titles = g.transform(lambda x:x.split(\". \")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Titles\"] = pd.Series(titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "titles_ignore = ['Don', 'Rev', 'Dr', 'Mme','Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'the Countess',\n",
    "                 'Jonkheer', 'Dona']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "def titleignore(x):\n",
    "    if(x in(titles_ignore)):\n",
    "        return(\"Others\")\n",
    "    else:\n",
    "        return(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Titles\"] = combined.Titles.apply(titleignore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SIBSP and Parch\n",
    "family = combined.SibSp+combined.Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "family.replace(to_replace = 0, value =1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Family\"] = pd.Series(family)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121f31fd0>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets evaluate Family and Survival\n",
    "pd.DataFrame(combined.groupby(\"Family\")[\"Survived\"].value_counts()).plot(kind = \"barh\")\n",
    "# Singles have better survival chances\n",
    "# Anything beyond 03 members had a rare chance of survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def family_cat(x):\n",
    "    if(x ==1):\n",
    "        return(\"Singles\")\n",
    "    elif((x>1) & (x<3)):\n",
    "        return(\"Small Family\")\n",
    "    else:\n",
    "        return(\"Large Family\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Family_On_Board\"] = pd.Series(combined.Family.apply(family_cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th colspan=\"3\" halign=\"left\">female</th>\n",
       "      <th colspan=\"3\" halign=\"left\">male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>133.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>158.00000</td>\n",
       "      <td>349.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.037594</td>\n",
       "      <td>27.499223</td>\n",
       "      <td>22.185329</td>\n",
       "      <td>41.029272</td>\n",
       "      <td>30.81538</td>\n",
       "      <td>25.962264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.272460</td>\n",
       "      <td>12.911747</td>\n",
       "      <td>12.205254</td>\n",
       "      <td>14.578529</td>\n",
       "      <td>13.97740</td>\n",
       "      <td>11.682415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.67000</td>\n",
       "      <td>0.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>29.50000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>38.75000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>70.00000</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex         female                                male                       \n",
       "Pclass           1           2           3           1          2           3\n",
       "count   133.000000  103.000000  152.000000  151.000000  158.00000  349.000000\n",
       "mean     37.037594   27.499223   22.185329   41.029272   30.81538   25.962264\n",
       "std      14.272460   12.911747   12.205254   14.578529   13.97740   11.682415\n",
       "min       2.000000    0.920000    0.170000    0.920000    0.67000    0.330000\n",
       "25%      24.000000   20.000000   16.000000   30.000000   23.00000   20.000000\n",
       "50%      36.000000   28.000000   22.000000   42.000000   29.50000   25.000000\n",
       "75%      48.000000   34.000000   30.000000   50.000000   38.75000   32.000000\n",
       "max      76.000000   60.000000   63.000000   80.000000   70.00000   74.000000"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age is Dependent on the gender in Titanic. \n",
    "combined.groupby([\"Sex\", \"Pclass\"])[\"Age\"].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Fare_Per_Person\"] = combined.Fare/combined.Family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Miss'"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\"McGowan, Miss. Anna Annie\").split()[1][:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "titles = []\n",
    "for i in combined.Name:\n",
    "    titles.append(i.split()[1][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mr'"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Name[1279].split()[1][:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.DataFrame(titles, columns = [\"titles\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"titles\"] = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "agena = combined[combined.Age.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin_Status</th>\n",
       "      <th>Titles</th>\n",
       "      <th>Family</th>\n",
       "      <th>Family_On_Board</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "      <th>titles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass                           Name     Sex  Age  \\\n",
       "5             6       0.0       3               Moran, Mr. James    male  NaN   \n",
       "17           18       1.0       2   Williams, Mr. Charles Eugene    male  NaN   \n",
       "19           20       1.0       3        Masselmani, Mrs. Fatima  female  NaN   \n",
       "26           27       0.0       3        Emir, Mr. Farred Chehab    male  NaN   \n",
       "28           29       1.0       3  O'Dwyer, Miss. Ellen \"Nellie\"  female  NaN   \n",
       "\n",
       "    SibSp  Parch  Ticket     Fare Cabin Embarked    Cabin_Status Titles  \\\n",
       "5       0      0  330877   8.4583   NaN        Q  No Cabin Found     Mr   \n",
       "17      0      0  244373  13.0000   NaN        S  No Cabin Found     Mr   \n",
       "19      0      0    2649   7.2250   NaN        C  No Cabin Found    Mrs   \n",
       "26      0      0    2631   7.2250   NaN        C  No Cabin Found     Mr   \n",
       "28      0      0  330959   7.8792   NaN        Q  No Cabin Found   Miss   \n",
       "\n",
       "    Family Family_On_Board  Fare_Per_Person titles  \n",
       "5        1         Singles           8.4583     Mr  \n",
       "17       1         Singles          13.0000     Mr  \n",
       "19       1         Singles           7.2250    Mrs  \n",
       "26       1         Singles           7.2250     Mr  \n",
       "28       1         Singles           7.8792   Miss  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agena.head()\n",
    "# Rule Formation:\n",
    "# 1. Person is Male or Female & PClass & Survival\n",
    "# 2. Title\n",
    "# 3. Family_On_Board and their Survival"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rule Formation:\n",
    "1. Person is Male or Female & PClass & Survival\n",
    "2. Title\n",
    "3. Family_On_Board and their Survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries for ANOVA\n",
    "import scipy.stats as stats\n",
    "#import statsmodels.api as sms\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=108.32631005902482, pvalue=1.796708368328782e-43)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stats.f_oneway(data_group1, data_group2, data_group3, data_groupN)\n",
    "\n",
    "stats.f_oneway(combined.loc[combined[\"Pclass\"]==1][\"Age\"].dropna(),\n",
    "               combined.loc[combined[\"Pclass\"]==2][\"Age\"].dropna(),\n",
    "               combined.loc[combined[\"Pclass\"]==3][\"Age\"].dropna()) \n",
    "\n",
    "# P value shows that Pclass has an impact on the Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_name = ols('outcome_variable ~ group1 + group2 + groupN', data=your_data).fit()\n",
    "model = ols(\"Age~Sex+Pclass\", data = combined).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>Age</td>       <th>  R-squared:         </th> <td>   0.182</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   116.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 26 Dec 2019</td> <th>  Prob (F-statistic):</th> <td>3.32e-46</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:43:26</td>     <th>  Log-Likelihood:    </th> <td> -4169.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1046</td>      <th>  AIC:               </th> <td>   8345.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1043</td>      <th>  BIC:               </th> <td>   8360.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>   43.6462</td> <td>    1.194</td> <td>   36.555</td> <td> 0.000</td> <td>   41.303</td> <td>   45.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sex[T.male]</th> <td>    3.7375</td> <td>    0.844</td> <td>    4.428</td> <td> 0.000</td> <td>    2.081</td> <td>    5.394</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Pclass</th>      <td>   -7.3008</td> <td>    0.485</td> <td>  -15.059</td> <td> 0.000</td> <td>   -8.252</td> <td>   -6.349</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>14.009</td> <th>  Durbin-Watson:     </th> <td>   1.939</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  14.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.242</td> <th>  Prob(JB):          </th> <td>0.000612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.324</td> <th>  Cond. No.          </th> <td>    8.42</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    Age   R-squared:                       0.182\n",
       "Model:                            OLS   Adj. R-squared:                  0.180\n",
       "Method:                 Least Squares   F-statistic:                     116.0\n",
       "Date:                Thu, 26 Dec 2019   Prob (F-statistic):           3.32e-46\n",
       "Time:                        09:43:26   Log-Likelihood:                -4169.6\n",
       "No. Observations:                1046   AIC:                             8345.\n",
       "Df Residuals:                    1043   BIC:                             8360.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "Intercept      43.6462      1.194     36.555      0.000      41.303      45.989\n",
       "Sex[T.male]     3.7375      0.844      4.428      0.000       2.081       5.394\n",
       "Pclass         -7.3008      0.485    -15.059      0.000      -8.252      -6.349\n",
       "==============================================================================\n",
       "Omnibus:                       14.009   Durbin-Watson:                   1.939\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               14.798\n",
       "Skew:                           0.242   Prob(JB):                     0.000612\n",
       "Kurtosis:                       3.324   Cond. No.                         8.42\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()\n",
    "# This shows that Gender has an impact on the Age Variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Printing Model Diagnostics\n",
    "\n",
    "1. The Durban-Watson tests is to detect the presence of autocorrelation (not provided when calling diagnostics this way), \n",
    "2. Jarque-Bera (jb; jbpv is p-value) tests the assumption of normality, \n",
    "3. Omnibus (omni; omnipv is p-value) tests the assumption of homogeneity of variance, and the \n",
    "4. Condition Number (condno) assess multicollinearity. Condition Number values over 20 are indicative of multicollinearity.\n",
    "\n",
    "If the omnibus test were to be significant, an option on how to handle it would be to use a heteroscedasticity corrected coefficient covariance matrix in the .anova_lm() method. This corrects the calculations to account for the heteroscedasticity present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'jb': 14.798044019974306, 'jbpv': 0.0006118508526413737, 'skew': 0.24201445096876661, 'kurtosis': 3.3244233236232192, 'omni': 14.009155048436545, 'omnipv': 0.000907717342875599, 'condno': 8.415821899016247, 'mineigval': 101.45754885423172}\n"
     ]
    }
   ],
   "source": [
    "print(model.diagn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>Age</td>       <th>  R-squared:         </th> <td>   0.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.285</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   84.40</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 26 Dec 2019</td> <th>  Prob (F-statistic):</th> <td>1.70e-74</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:43:27</td>     <th>  Log-Likelihood:    </th> <td> -4096.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1046</td>      <th>  AIC:               </th> <td>   8205.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1040</td>      <th>  BIC:               </th> <td>   8235.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>    5.4826</td> <td>    1.674</td> <td>    3.275</td> <td> 0.001</td> <td>    2.198</td> <td>    8.767</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Titles[T.Miss]</th>   <td>   16.2916</td> <td>    1.873</td> <td>    8.697</td> <td> 0.000</td> <td>   12.616</td> <td>   19.967</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Titles[T.Mr]</th>     <td>   26.7695</td> <td>    1.749</td> <td>   15.310</td> <td> 0.000</td> <td>   23.338</td> <td>   30.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Titles[T.Mrs]</th>    <td>   31.5115</td> <td>    1.917</td> <td>   16.437</td> <td> 0.000</td> <td>   27.750</td> <td>   35.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Titles[T.Ms]</th>     <td>   22.5174</td> <td>   12.300</td> <td>    1.831</td> <td> 0.067</td> <td>   -1.619</td> <td>   46.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Titles[T.Others]</th> <td>   37.6464</td> <td>    2.755</td> <td>   13.663</td> <td> 0.000</td> <td>   32.240</td> <td>   43.053</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>89.071</td> <th>  Durbin-Watson:     </th> <td>   1.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 110.504</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.759</td> <th>  Prob(JB):          </th> <td>1.01e-24</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.479</td> <th>  Cond. No.          </th> <td>    38.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    Age   R-squared:                       0.289\n",
       "Model:                            OLS   Adj. R-squared:                  0.285\n",
       "Method:                 Least Squares   F-statistic:                     84.40\n",
       "Date:                Thu, 26 Dec 2019   Prob (F-statistic):           1.70e-74\n",
       "Time:                        09:43:27   Log-Likelihood:                -4096.5\n",
       "No. Observations:                1046   AIC:                             8205.\n",
       "Df Residuals:                    1040   BIC:                             8235.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept            5.4826      1.674      3.275      0.001       2.198       8.767\n",
       "Titles[T.Miss]      16.2916      1.873      8.697      0.000      12.616      19.967\n",
       "Titles[T.Mr]        26.7695      1.749     15.310      0.000      23.338      30.201\n",
       "Titles[T.Mrs]       31.5115      1.917     16.437      0.000      27.750      35.273\n",
       "Titles[T.Ms]        22.5174     12.300      1.831      0.067      -1.619      46.653\n",
       "Titles[T.Others]    37.6464      2.755     13.663      0.000      32.240      43.053\n",
       "==============================================================================\n",
       "Omnibus:                       89.071   Durbin-Watson:                   1.989\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              110.504\n",
       "Skew:                           0.759   Prob(JB):                     1.01e-24\n",
       "Kurtosis:                       3.479   Cond. No.                         38.9\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if Title has an impact on Age\n",
    "model_age = ols(\"Age~Titles\", data = combined).fit()\n",
    "model_age.summary()\n",
    "\n",
    "# Apart from Ms, titles have significant impact on the Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>Age</td>       <th>  R-squared:         </th> <td>   0.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   39.69</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 26 Dec 2019</td> <th>  Prob (F-statistic):</th> <td>2.45e-17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:43:28</td>     <th>  Log-Likelihood:    </th> <td> -4236.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1046</td>      <th>  AIC:               </th> <td>   8479.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1043</td>      <th>  BIC:               </th> <td>   8493.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                    <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                       <td>   19.8208</td> <td>    1.351</td> <td>   14.673</td> <td> 0.000</td> <td>   17.170</td> <td>   22.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Family_On_Board[T.Singles]</th>      <td>   12.0055</td> <td>    1.438</td> <td>    8.349</td> <td> 0.000</td> <td>    9.184</td> <td>   14.827</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Family_On_Board[T.Small Family]</th> <td>    6.7132</td> <td>    1.780</td> <td>    3.772</td> <td> 0.000</td> <td>    3.221</td> <td>   10.206</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>67.941</td> <th>  Durbin-Watson:     </th> <td>   1.965</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  80.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.674</td> <th>  Prob(JB):          </th> <td>4.10e-18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.141</td> <th>  Cond. No.          </th> <td>    7.38</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    Age   R-squared:                       0.071\n",
       "Model:                            OLS   Adj. R-squared:                  0.069\n",
       "Method:                 Least Squares   F-statistic:                     39.69\n",
       "Date:                Thu, 26 Dec 2019   Prob (F-statistic):           2.45e-17\n",
       "Time:                        09:43:28   Log-Likelihood:                -4236.3\n",
       "No. Observations:                1046   AIC:                             8479.\n",
       "Df Residuals:                    1043   BIC:                             8493.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================================\n",
       "                                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------------\n",
       "Intercept                          19.8208      1.351     14.673      0.000      17.170      22.472\n",
       "Family_On_Board[T.Singles]         12.0055      1.438      8.349      0.000       9.184      14.827\n",
       "Family_On_Board[T.Small Family]     6.7132      1.780      3.772      0.000       3.221      10.206\n",
       "==============================================================================\n",
       "Omnibus:                       67.941   Durbin-Watson:                   1.965\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               80.073\n",
       "Skew:                           0.674   Prob(JB):                     4.10e-18\n",
       "Kurtosis:                       3.141   Cond. No.                         7.38\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Family on Board\n",
    "model_family = ols(\"Age~Family_On_Board\", data=combined).fit()\n",
    "model_family.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since ANOVA tests that Family On Board, Title, PClass and Gender are significant variables and hence we need to fill the median age basis these variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.loc[(combined[\"Pclass\"]==1) & (combined[\"Sex\"]==\"female\") & (combined[\"Titles\"]==\"Miss\") & (combined[\"Family_On_Board\"]==\"Singles\") & (combined[\"Age\"].isnull()), \"Age\"]=31.0\n",
    "combined.loc[(combined[\"Pclass\"]==1) & (combined[\"Sex\"]==\"female\") & (combined[\"Titles\"]==\"Mrs\") & (combined[\"Family_On_Board\"]==\"Singles\") & (combined[\"Age\"].isnull()), \"Age\"]=44.0\n",
    "combined.loc[(combined[\"Pclass\"]==1) & (combined[\"Sex\"]==\"male\") & (combined[\"Titles\"]==\"Mr\") & (combined[\"Family_On_Board\"]==\"Singles\") & (combined[\"Age\"].isnull()), \"Age\"] = 41.0\n",
    "combined.loc[(combined[\"Pclass\"]==1) & (combined[\"Sex\"]==\"male\") & (combined[\"Titles\"]==\"Others\") & (combined[\"Family_On_Board\"]==\"Singles\") & (combined[\"Age\"].isnull()), \"Age\"]=48.0\n",
    "combined.loc[(combined[\"Pclass\"]==2) & (combined[\"Sex\"]==\"female\") & (combined[\"Titles\"]==\"Miss\") & (combined[\"Family_On_Board\"]==\"Singles\") & (combined[\"Age\"].isnull()), \"Age\"]=26.5\n",
    "combined.loc[(combined[\"Pclass\"]==2) & (combined[\"Sex\"]==\"female\") & (combined[\"Titles\"]==\"Mrs\") & (combined[\"Family_On_Board\"]==\"Singles\") & (combined[\"Age\"].isnull()), \"Age\"]=29.5\n",
    "combined.loc[(combined[\"Pclass\"]==2) & (combined[\"Sex\"]==\"male\") & (combined[\"Titles\"]==\"Mr\") & (combined[\"Family_On_Board\"]==\"Singles\") & (combined[\"Age\"].isnull()), \"Age\"]=30.0\n",
    "combined.loc[(combined[\"Pclass\"]==3) & (combined[\"Sex\"]==\"female\") & (combined[\"Titles\"]==\"Miss\") & (combined[\"Family_On_Board\"]==\"Large Family\") & (combined[\"Age\"].isnull()), \"Age\"] = 8.0\n",
    "combined.loc[(combined[\"Pclass\"]==3) & (combined[\"Sex\"]==\"female\") & (combined[\"Titles\"]==\"Miss\") & (combined[\"Family_On_Board\"]==\"Singles\") & (combined[\"Age\"].isnull()), \"Age\"] = 22.0\n",
    "combined.loc[(combined[\"Pclass\"]==3) & (combined[\"Sex\"]==\"female\") & (combined[\"Titles\"]==\"Miss\") & (combined[\"Family_On_Board\"]==\"Small Family\") & (combined[\"Age\"].isnull()), \"Age\"] = 4.0\n",
    "combined.loc[(combined[\"Pclass\"]==3) & (combined[\"Sex\"]==\"female\") & (combined[\"Titles\"]==\"Mrs\") & (combined[\"Family_On_Board\"]==\"Large Family\") & (combined[\"Age\"].isnull()), \"Age\"] = 39.0\n",
    "combined.loc[(combined[\"Pclass\"]==3) & (combined[\"Sex\"]==\"female\") & (combined[\"Titles\"]==\"Mrs\") & (combined[\"Family_On_Board\"]==\"Singles\") & (combined[\"Age\"].isnull()), \"Age\"] = 31.0\n",
    "combined.loc[(combined[\"Pclass\"]==3) & (combined[\"Sex\"]==\"male\") & (combined[\"Titles\"]==\"Master\") & (combined[\"Family_On_Board\"]==\"Large Family\") & (combined[\"Age\"].isnull()), \"Age\"]=5.5\n",
    "combined.loc[(combined[\"Pclass\"]==3) & (combined[\"Sex\"]==\"male\") & (combined[\"Titles\"]==\"Master\") & (combined[\"Family_On_Board\"]==\"Singles\") & (combined[\"Age\"].isnull()), \"Age\"]=6.0\n",
    "combined.loc[(combined[\"Pclass\"]==3) & (combined[\"Sex\"]==\"male\") & (combined[\"Titles\"]==\"Master\") & (combined[\"Family_On_Board\"]==\"Small Family\") & (combined[\"Age\"].isnull()), \"Age\"] = 6.5\n",
    "combined.loc[(combined[\"Pclass\"]==3) & (combined[\"Sex\"]==\"male\") & (combined[\"Titles\"]==\"Mr\") & (combined[\"Family_On_Board\"]==\"Large Family\") & (combined[\"Age\"].isnull()), \"Age\"] = 27.5\n",
    "combined.loc[(combined[\"Pclass\"]==3) & (combined[\"Sex\"]==\"male\") & (combined[\"Titles\"]==\"Mr\") & (combined[\"Family_On_Board\"]==\"Singles\") & (combined[\"Age\"].isnull()), \"Age\"] = 26.0\n",
    "combined.loc[(combined[\"Pclass\"]==3) & (combined[\"Sex\"]==\"male\") & \n",
    "             (combined[\"Titles\"]==\"Mr\") & (combined[\"Family_On_Board\"]==\"Small Family\") & \n",
    "             (combined[\"Age\"].isnull()), \"Age\"]=25.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for Ms, if Less then Put it in Others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Titles</th>\n",
       "      <th>Family_On_Board</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"7\" valign=\"top\">female</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Miss</th>\n",
       "      <th>Large Family</th>\n",
       "      <td>7.0</td>\n",
       "      <td>18.571429</td>\n",
       "      <td>8.560708</td>\n",
       "      <td>2.00</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.500</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singles</th>\n",
       "      <td>47.0</td>\n",
       "      <td>32.468085</td>\n",
       "      <td>11.245968</td>\n",
       "      <td>15.00</td>\n",
       "      <td>23.5000</td>\n",
       "      <td>31.0</td>\n",
       "      <td>37.000</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>6.0</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>9.049862</td>\n",
       "      <td>18.00</td>\n",
       "      <td>19.7500</td>\n",
       "      <td>26.5</td>\n",
       "      <td>34.750</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mrs</th>\n",
       "      <th>Large Family</th>\n",
       "      <td>5.0</td>\n",
       "      <td>39.200000</td>\n",
       "      <td>14.754660</td>\n",
       "      <td>25.00</td>\n",
       "      <td>27.0000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>48.000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singles</th>\n",
       "      <td>57.0</td>\n",
       "      <td>42.157895</td>\n",
       "      <td>13.398813</td>\n",
       "      <td>17.00</td>\n",
       "      <td>35.0000</td>\n",
       "      <td>44.0</td>\n",
       "      <td>51.000</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>15.0</td>\n",
       "      <td>49.066667</td>\n",
       "      <td>10.081572</td>\n",
       "      <td>27.00</td>\n",
       "      <td>42.5000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>54.500</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <th>Singles</th>\n",
       "      <td>7.0</td>\n",
       "      <td>34.428571</td>\n",
       "      <td>11.148350</td>\n",
       "      <td>24.00</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>43.500</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">male</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Master</th>\n",
       "      <th>Large Family</th>\n",
       "      <td>3.0</td>\n",
       "      <td>8.306667</td>\n",
       "      <td>6.474730</td>\n",
       "      <td>0.92</td>\n",
       "      <td>5.9600</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.000</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.500</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mr</th>\n",
       "      <th>Large Family</th>\n",
       "      <td>5.0</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>19.710403</td>\n",
       "      <td>19.00</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>61.000</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singles</th>\n",
       "      <td>142.0</td>\n",
       "      <td>41.017606</td>\n",
       "      <td>11.944228</td>\n",
       "      <td>17.00</td>\n",
       "      <td>32.6250</td>\n",
       "      <td>41.0</td>\n",
       "      <td>47.000</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>12.0</td>\n",
       "      <td>45.333333</td>\n",
       "      <td>13.357554</td>\n",
       "      <td>17.00</td>\n",
       "      <td>37.0000</td>\n",
       "      <td>49.5</td>\n",
       "      <td>55.500</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Others</th>\n",
       "      <th>Singles</th>\n",
       "      <td>11.0</td>\n",
       "      <td>47.272727</td>\n",
       "      <td>8.210858</td>\n",
       "      <td>32.00</td>\n",
       "      <td>42.5000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>52.500</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>4.0</td>\n",
       "      <td>54.250000</td>\n",
       "      <td>11.146748</td>\n",
       "      <td>44.00</td>\n",
       "      <td>48.5000</td>\n",
       "      <td>51.5</td>\n",
       "      <td>57.250</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"7\" valign=\"top\">female</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Miss</th>\n",
       "      <th>Large Family</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.435556</td>\n",
       "      <td>9.796294</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.000</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singles</th>\n",
       "      <td>32.0</td>\n",
       "      <td>26.515625</td>\n",
       "      <td>9.950267</td>\n",
       "      <td>6.00</td>\n",
       "      <td>19.7500</td>\n",
       "      <td>26.5</td>\n",
       "      <td>31.375</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>9.0</td>\n",
       "      <td>11.666667</td>\n",
       "      <td>7.664855</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.000</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mrs</th>\n",
       "      <th>Large Family</th>\n",
       "      <td>8.0</td>\n",
       "      <td>33.875000</td>\n",
       "      <td>11.716137</td>\n",
       "      <td>22.00</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>31.5</td>\n",
       "      <td>39.000</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singles</th>\n",
       "      <td>35.0</td>\n",
       "      <td>33.357143</td>\n",
       "      <td>10.813643</td>\n",
       "      <td>14.00</td>\n",
       "      <td>27.5000</td>\n",
       "      <td>29.5</td>\n",
       "      <td>40.500</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>12.0</td>\n",
       "      <td>33.416667</td>\n",
       "      <td>9.110018</td>\n",
       "      <td>22.00</td>\n",
       "      <td>25.5000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>41.250</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <th>Singles</th>\n",
       "      <td>1.0</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.00</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.000</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">male</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">Master</th>\n",
       "      <th>Large Family</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.933000</td>\n",
       "      <td>2.804794</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.8725</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mr</th>\n",
       "      <th>Large Family</th>\n",
       "      <td>4.0</td>\n",
       "      <td>33.250000</td>\n",
       "      <td>11.954776</td>\n",
       "      <td>23.00</td>\n",
       "      <td>24.5000</td>\n",
       "      <td>30.5</td>\n",
       "      <td>39.250</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singles</th>\n",
       "      <td>133.0</td>\n",
       "      <td>32.157895</td>\n",
       "      <td>11.410118</td>\n",
       "      <td>14.00</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>36.000</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>13.0</td>\n",
       "      <td>31.653846</td>\n",
       "      <td>11.473492</td>\n",
       "      <td>19.00</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>31.0</td>\n",
       "      <td>36.500</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Others</th>\n",
       "      <th>Singles</th>\n",
       "      <td>9.0</td>\n",
       "      <td>41.888889</td>\n",
       "      <td>13.119112</td>\n",
       "      <td>23.00</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>42.0</td>\n",
       "      <td>54.000</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>1.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.00</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.000</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"7\" valign=\"top\">female</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Miss</th>\n",
       "      <th>Large Family</th>\n",
       "      <td>27.0</td>\n",
       "      <td>8.765556</td>\n",
       "      <td>7.594557</td>\n",
       "      <td>0.17</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.000</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singles</th>\n",
       "      <td>106.0</td>\n",
       "      <td>21.702830</td>\n",
       "      <td>6.129079</td>\n",
       "      <td>2.00</td>\n",
       "      <td>19.2500</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.000</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>17.0</td>\n",
       "      <td>5.941176</td>\n",
       "      <td>6.036044</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.000</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mrs</th>\n",
       "      <th>Large Family</th>\n",
       "      <td>14.0</td>\n",
       "      <td>37.785714</td>\n",
       "      <td>6.290146</td>\n",
       "      <td>24.00</td>\n",
       "      <td>34.2500</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.500</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singles</th>\n",
       "      <td>31.0</td>\n",
       "      <td>32.096774</td>\n",
       "      <td>10.520947</td>\n",
       "      <td>15.00</td>\n",
       "      <td>26.5000</td>\n",
       "      <td>31.0</td>\n",
       "      <td>37.000</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>17.0</td>\n",
       "      <td>28.647059</td>\n",
       "      <td>6.402550</td>\n",
       "      <td>16.00</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32.000</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <th>Singles</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">male</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Master</th>\n",
       "      <th>Large Family</th>\n",
       "      <td>25.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.807887</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>5.5</td>\n",
       "      <td>9.000</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singles</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.708333</td>\n",
       "      <td>4.524486</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2.1225</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.250</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>14.0</td>\n",
       "      <td>6.398571</td>\n",
       "      <td>3.613408</td>\n",
       "      <td>0.33</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.500</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mr</th>\n",
       "      <th>Large Family</th>\n",
       "      <td>17.0</td>\n",
       "      <td>27.088235</td>\n",
       "      <td>9.131394</td>\n",
       "      <td>14.00</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>27.5</td>\n",
       "      <td>31.000</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singles</th>\n",
       "      <td>407.0</td>\n",
       "      <td>27.710074</td>\n",
       "      <td>8.283729</td>\n",
       "      <td>11.00</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>29.000</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small Family</th>\n",
       "      <td>24.0</td>\n",
       "      <td>26.520833</td>\n",
       "      <td>8.875319</td>\n",
       "      <td>15.00</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.500</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      count       mean        std    min  \\\n",
       "Pclass Sex    Titles Family_On_Board                                       \n",
       "1      female Miss   Large Family       7.0  18.571429   8.560708   2.00   \n",
       "                     Singles           47.0  32.468085  11.245968  15.00   \n",
       "                     Small Family       6.0  27.500000   9.049862  18.00   \n",
       "              Mrs    Large Family       5.0  39.200000  14.754660  25.00   \n",
       "                     Singles           57.0  42.157895  13.398813  17.00   \n",
       "                     Small Family      15.0  49.066667  10.081572  27.00   \n",
       "              Others Singles            7.0  34.428571  11.148350  24.00   \n",
       "       male   Master Large Family       3.0   8.306667   6.474730   0.92   \n",
       "                     Small Family       2.0   5.000000   1.414214   4.00   \n",
       "              Mr     Large Family       5.0  42.000000  19.710403  19.00   \n",
       "                     Singles          142.0  41.017606  11.944228  17.00   \n",
       "                     Small Family      12.0  45.333333  13.357554  17.00   \n",
       "              Others Singles           11.0  47.272727   8.210858  32.00   \n",
       "                     Small Family       4.0  54.250000  11.146748  44.00   \n",
       "2      female Miss   Large Family       9.0  10.435556   9.796294   0.92   \n",
       "                     Singles           32.0  26.515625   9.950267   6.00   \n",
       "                     Small Family       9.0  11.666667   7.664855   2.00   \n",
       "              Mrs    Large Family       8.0  33.875000  11.716137  22.00   \n",
       "                     Singles           35.0  33.357143  10.813643  14.00   \n",
       "                     Small Family      12.0  33.416667   9.110018  22.00   \n",
       "              Ms     Singles            1.0  28.000000        NaN  28.00   \n",
       "       male   Master Large Family       1.0   1.000000        NaN   1.00   \n",
       "                     Small Family      10.0   2.933000   2.804794   0.67   \n",
       "              Mr     Large Family       4.0  33.250000  11.954776  23.00   \n",
       "                     Singles          133.0  32.157895  11.410118  14.00   \n",
       "                     Small Family      13.0  31.653846  11.473492  19.00   \n",
       "              Others Singles            9.0  41.888889  13.119112  23.00   \n",
       "                     Small Family       1.0  30.000000        NaN  30.00   \n",
       "3      female Miss   Large Family      27.0   8.765556   7.594557   0.17   \n",
       "                     Singles          106.0  21.702830   6.129079   2.00   \n",
       "                     Small Family      17.0   5.941176   6.036044   1.00   \n",
       "              Mrs    Large Family      14.0  37.785714   6.290146  24.00   \n",
       "                     Singles           31.0  32.096774  10.520947  15.00   \n",
       "                     Small Family      17.0  28.647059   6.402550  16.00   \n",
       "              Ms     Singles            0.0        NaN        NaN    NaN   \n",
       "       male   Master Large Family      25.0   6.000000   3.807887   1.00   \n",
       "                     Singles            6.0   5.708333   4.524486   0.42   \n",
       "                     Small Family      14.0   6.398571   3.613408   0.33   \n",
       "              Mr     Large Family      17.0  27.088235   9.131394  14.00   \n",
       "                     Singles          407.0  27.710074   8.283729  11.00   \n",
       "                     Small Family      24.0  26.520833   8.875319  15.00   \n",
       "\n",
       "                                          25%   50%     75%   max  \n",
       "Pclass Sex    Titles Family_On_Board                               \n",
       "1      female Miss   Large Family     16.0000  21.0  23.500  28.0  \n",
       "                     Singles          23.5000  31.0  37.000  63.0  \n",
       "                     Small Family     19.7500  26.5  34.750  39.0  \n",
       "              Mrs    Large Family     27.0000  36.0  48.000  60.0  \n",
       "                     Singles          35.0000  44.0  51.000  76.0  \n",
       "                     Small Family     42.5000  50.0  54.500  64.0  \n",
       "              Others Singles          24.0000  33.0  43.500  49.0  \n",
       "       male   Master Large Family      5.9600  11.0  12.000  13.0  \n",
       "                     Small Family      4.5000   5.0   5.500   6.0  \n",
       "              Mr     Large Family     30.0000  36.0  61.000  64.0  \n",
       "                     Singles          32.6250  41.0  47.000  80.0  \n",
       "                     Small Family     37.0000  49.5  55.500  60.0  \n",
       "              Others Singles          42.5000  48.0  52.500  60.0  \n",
       "                     Small Family     48.5000  51.5  57.250  70.0  \n",
       "2      female Miss   Large Family      3.0000   5.0  20.000  24.0  \n",
       "                     Singles          19.7500  26.5  31.375  50.0  \n",
       "                     Small Family      7.0000   8.0  18.000  25.0  \n",
       "              Mrs    Large Family     24.0000  31.5  39.000  54.0  \n",
       "                     Singles          27.5000  29.5  40.500  60.0  \n",
       "                     Small Family     25.5000  32.0  41.250  48.0  \n",
       "              Ms     Singles          28.0000  28.0  28.000  28.0  \n",
       "       male   Master Large Family      1.0000   1.0   1.000   1.0  \n",
       "                     Small Family      0.8725   2.0   3.000   8.0  \n",
       "              Mr     Large Family     24.5000  30.5  39.250  49.0  \n",
       "                     Singles          25.0000  30.0  36.000  70.0  \n",
       "                     Small Family     24.0000  31.0  36.500  60.0  \n",
       "              Others Singles          28.0000  42.0  54.000  57.0  \n",
       "                     Small Family     30.0000  30.0  30.000  30.0  \n",
       "3      female Miss   Large Family      5.0000   8.0   9.000  38.0  \n",
       "                     Singles          19.2500  22.0  23.000  45.0  \n",
       "                     Small Family      2.0000   4.0   9.000  22.0  \n",
       "              Mrs    Large Family     34.2500  39.0  40.500  48.0  \n",
       "                     Singles          26.5000  31.0  37.000  63.0  \n",
       "                     Small Family     26.0000  29.0  32.000  41.0  \n",
       "              Ms     Singles              NaN   NaN     NaN   NaN  \n",
       "       male   Master Large Family      3.0000   5.5   9.000  14.5  \n",
       "                     Singles           2.1225   6.0   8.250  12.0  \n",
       "                     Small Family      4.5000   6.5   8.500  13.0  \n",
       "              Mr     Large Family     18.0000  27.5  31.000  40.0  \n",
       "                     Singles          24.0000  26.0  29.000  74.0  \n",
       "                     Small Family     18.0000  25.0  34.500  41.0  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(combined.groupby([\"Pclass\", \"Sex\", \"Titles\", \"Family_On_Board\"])[\"Age\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Titles</th>\n",
       "      <th>Family_On_Board</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Master</th>\n",
       "      <th>False</th>\n",
       "      <td>55.0</td>\n",
       "      <td>5.542364</td>\n",
       "      <td>3.854088</td>\n",
       "      <td>0.33</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.000</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.708333</td>\n",
       "      <td>4.524486</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2.1225</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.250</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Miss</th>\n",
       "      <th>False</th>\n",
       "      <td>75.0</td>\n",
       "      <td>11.087867</td>\n",
       "      <td>9.572230</td>\n",
       "      <td>0.17</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.000</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>185.0</td>\n",
       "      <td>25.270270</td>\n",
       "      <td>9.516858</td>\n",
       "      <td>2.00</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>30.000</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Mr</th>\n",
       "      <th>False</th>\n",
       "      <td>75.0</td>\n",
       "      <td>31.940000</td>\n",
       "      <td>12.966943</td>\n",
       "      <td>14.00</td>\n",
       "      <td>22.5000</td>\n",
       "      <td>28.0</td>\n",
       "      <td>40.000</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>682.0</td>\n",
       "      <td>31.348240</td>\n",
       "      <td>11.100366</td>\n",
       "      <td>11.00</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>36.375</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Mrs</th>\n",
       "      <th>False</th>\n",
       "      <td>71.0</td>\n",
       "      <td>36.901408</td>\n",
       "      <td>11.321478</td>\n",
       "      <td>16.00</td>\n",
       "      <td>27.5000</td>\n",
       "      <td>36.0</td>\n",
       "      <td>45.000</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>123.0</td>\n",
       "      <td>37.117886</td>\n",
       "      <td>12.827526</td>\n",
       "      <td>14.00</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.000</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <th>True</th>\n",
       "      <td>1.0</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.00</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.000</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Others</th>\n",
       "      <th>False</th>\n",
       "      <td>5.0</td>\n",
       "      <td>49.400000</td>\n",
       "      <td>14.518953</td>\n",
       "      <td>30.00</td>\n",
       "      <td>44.0000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>53.000</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>27.0</td>\n",
       "      <td>42.148148</td>\n",
       "      <td>11.608033</td>\n",
       "      <td>23.00</td>\n",
       "      <td>32.5000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>51.500</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count       mean        std    min      25%   50%  \\\n",
       "Titles Family_On_Board                                                      \n",
       "Master False             55.0   5.542364   3.854088   0.33   2.0000   5.5   \n",
       "       True               6.0   5.708333   4.524486   0.42   2.1225   6.0   \n",
       "Miss   False             75.0  11.087867   9.572230   0.17   4.0000   8.0   \n",
       "       True             185.0  25.270270   9.516858   2.00  21.0000  22.0   \n",
       "Mr     False             75.0  31.940000  12.966943  14.00  22.5000  28.0   \n",
       "       True             682.0  31.348240  11.100366  11.00  25.0000  27.0   \n",
       "Mrs    False             71.0  36.901408  11.321478  16.00  27.5000  36.0   \n",
       "       True             123.0  37.117886  12.827526  14.00  28.0000  35.0   \n",
       "Ms     True               1.0  28.000000        NaN  28.00  28.0000  28.0   \n",
       "Others False              5.0  49.400000  14.518953  30.00  44.0000  50.0   \n",
       "       True              27.0  42.148148  11.608033  23.00  32.5000  45.0   \n",
       "\n",
       "                           75%   max  \n",
       "Titles Family_On_Board                \n",
       "Master False             8.000  14.5  \n",
       "       True              8.250  12.0  \n",
       "Miss   False            18.000  39.0  \n",
       "       True             30.000  63.0  \n",
       "Mr     False            40.000  64.0  \n",
       "       True             36.375  80.0  \n",
       "Mrs    False            45.000  64.0  \n",
       "       True             45.000  76.0  \n",
       "Ms     True             28.000  28.0  \n",
       "Others False            53.000  70.0  \n",
       "       True             51.500  60.0  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age Basis PClass and Sex\n",
    "pd.DataFrame(combined.groupby([\"Titles\", combined.Family_On_Board==\"Singles\"])[\"Age\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.loc[combined.Age.isnull(), \"Age\"] =28.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin_Status</th>\n",
       "      <th>Titles</th>\n",
       "      <th>Family</th>\n",
       "      <th>Family_On_Board</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "      <th>titles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>Cabin Allotted</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>Cabin Allotted</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "3            4       1.0       1   \n",
       "4            5       0.0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked    Cabin_Status Titles  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S  No Cabin Found     Mr   \n",
       "1      0          PC 17599  71.2833   C85        C  Cabin Allotted    Mrs   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  No Cabin Found   Miss   \n",
       "3      0            113803  53.1000  C123        S  Cabin Allotted    Mrs   \n",
       "4      0            373450   8.0500   NaN        S  No Cabin Found     Mr   \n",
       "\n",
       "   Family Family_On_Board  Fare_Per_Person titles  \n",
       "0       1         Singles           7.2500     Mr  \n",
       "1       1         Singles          71.2833    Mrs  \n",
       "2       1         Singles           7.9250   Miss  \n",
       "3       1         Singles          53.1000    Mrs  \n",
       "4       1         Singles           8.0500     Mr  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>titles</th>\n",
       "      <th></th>\n",
       "      <th>Billiard</th>\n",
       "      <th>Brito</th>\n",
       "      <th>Capt</th>\n",
       "      <th>Carlo</th>\n",
       "      <th>Col</th>\n",
       "      <th>Cruyssen</th>\n",
       "      <th>Don</th>\n",
       "      <th>Dr</th>\n",
       "      <th>Gordon</th>\n",
       "      <th>...</th>\n",
       "      <th>Palmquist</th>\n",
       "      <th>Pelsmaeker</th>\n",
       "      <th>Planke</th>\n",
       "      <th>Rev</th>\n",
       "      <th>Shawah</th>\n",
       "      <th>Steen</th>\n",
       "      <th>Velde</th>\n",
       "      <th>Walle</th>\n",
       "      <th>de</th>\n",
       "      <th>th</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>27.312500</td>\n",
       "      <td>19.50000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>44.125000</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>41.250000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.055583</td>\n",
       "      <td>18.35756</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.535534</td>\n",
       "      <td>5.477226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.973182</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.124038</td>\n",
       "      <td>12.020815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>6.50000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.750000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>25.250000</td>\n",
       "      <td>51.500000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>48.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.750000</td>\n",
       "      <td>11.50000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>27.750000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>48.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>51.750000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>40.50000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "titles             Billiard  Brito  Capt      Carlo        Col  Cruyssen  \\\n",
       "count    8.000000   3.00000    1.0   1.0   2.000000   4.000000       1.0   \n",
       "mean    27.312500  19.50000   32.0  70.0  26.500000  54.000000      47.0   \n",
       "std      7.055583  18.35756    NaN   NaN   3.535534   5.477226       NaN   \n",
       "min     17.000000   6.50000   32.0  70.0  24.000000  47.000000      47.0   \n",
       "25%     24.750000   9.00000   32.0  70.0  25.250000  51.500000      47.0   \n",
       "50%     28.750000  11.50000   32.0  70.0  26.500000  54.500000      47.0   \n",
       "75%     30.000000  26.00000   32.0  70.0  27.750000  57.000000      47.0   \n",
       "max     39.000000  40.50000   32.0  70.0  29.000000  60.000000      47.0   \n",
       "\n",
       "titles   Don         Dr     Gordon  ...  Palmquist  Pelsmaeker     Planke  \\\n",
       "count    1.0   8.000000   2.000000  ...        1.0         1.0   4.000000   \n",
       "mean    40.0  44.125000  48.500000  ...       26.0        16.0  24.000000   \n",
       "std      NaN  10.973182   0.707107  ...        NaN         NaN   8.124038   \n",
       "min     40.0  23.000000  48.000000  ...       26.0        16.0  16.000000   \n",
       "25%     40.0  41.000000  48.250000  ...       26.0        16.0  17.500000   \n",
       "50%     40.0  48.500000  48.500000  ...       26.0        16.0  24.500000   \n",
       "75%     40.0  50.750000  48.750000  ...       26.0        16.0  31.000000   \n",
       "max     40.0  54.000000  49.000000  ...       26.0        16.0  31.000000   \n",
       "\n",
       "titles        Rev  Shawah  Steen  Velde  Walle    de    th  \n",
       "count    8.000000     1.0    1.0    1.0    1.0   1.0   1.0  \n",
       "mean    41.250000    30.0   28.0   33.0   28.0  61.0  33.0  \n",
       "std     12.020815     NaN    NaN    NaN    NaN   NaN   NaN  \n",
       "min     27.000000    30.0   28.0   33.0   28.0  61.0  33.0  \n",
       "25%     29.500000    30.0   28.0   33.0   28.0  61.0  33.0  \n",
       "50%     41.500000    30.0   28.0   33.0   28.0  61.0  33.0  \n",
       "75%     51.750000    30.0   28.0   33.0   28.0  61.0  33.0  \n",
       "max     57.000000    30.0   28.0   33.0   28.0  61.0  33.0  \n",
       "\n",
       "[8 rows x 34 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.groupby(\"titles\")[\"Age\"].describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another Approach for Missing Age Values\n",
    "1. combined.loc[(combined.titles==\"Miss\") & (combined.Age.isnull()), \"Age\"]=22.0\n",
    "2. combined.loc[(combined.titles==\"Master\") & (combined.Age.isnull()), \"Age\"]=4.0\n",
    "3. combined.loc[(combined.titles==\"Mr\") & (combined.Age.isnull()), \"Age\"]=29.0\n",
    "4. combined.loc[(combined.titles==\"Mrs\") & (combined.Age.isnull()), \"Age\"]=36.0\n",
    "5. combined.loc[(combined.titles==\"Others\") & (combined.Age.isnull()), \"Age\"]=33.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping Name, Ticket, Cabin and Passenger ID\n",
    "combined_new = combined.drop(['PassengerId', 'Name', \"SibSp\", \"Parch\", 'Ticket', 'Cabin'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin_Status</th>\n",
       "      <th>Titles</th>\n",
       "      <th>Family</th>\n",
       "      <th>Family_On_Board</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "      <th>titles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>Cabin Allotted</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>Cabin Allotted</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age     Fare Embarked    Cabin_Status Titles  \\\n",
       "0       0.0       3    male  22.0   7.2500        S  No Cabin Found     Mr   \n",
       "1       1.0       1  female  38.0  71.2833        C  Cabin Allotted    Mrs   \n",
       "2       1.0       3  female  26.0   7.9250        S  No Cabin Found   Miss   \n",
       "3       1.0       1  female  35.0  53.1000        S  Cabin Allotted    Mrs   \n",
       "4       0.0       3    male  35.0   8.0500        S  No Cabin Found     Mr   \n",
       "\n",
       "   Family Family_On_Board  Fare_Per_Person titles  \n",
       "0       1         Singles           7.2500     Mr  \n",
       "1       1         Singles          71.2833    Mrs  \n",
       "2       1         Singles           7.9250   Miss  \n",
       "3       1         Singles          53.1000    Mrs  \n",
       "4       1         Singles           8.0500     Mr  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data in train and test\n",
    "newtrain = combined_new.loc[0:train.shape[0]-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Embarked', 'Cabin_Status',\n",
       "       'Titles', 'Family', 'Family_On_Board', 'Fare_Per_Person', 'titles'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtest = combined_new.loc[891:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Embarked', 'Cabin_Status',\n",
       "       'Titles', 'Family', 'Family_On_Board', 'Fare_Per_Person', 'titles'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtest.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Pclass', 'Sex', 'Age', 'Fare', 'Embarked', 'Cabin_Status', 'Titles',\n",
      "       'Family', 'Family_On_Board', 'Fare_Per_Person', 'titles'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py:3940: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "newtest.drop(['Survived'], axis=1,inplace=True)\n",
    "print(newtest.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "newtrain[\"Survived\"] = newtrain.Survived.astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Embarked', 'Cabin_Status',\n",
      "       'Titles', 'Family', 'Family_On_Board', 'Fare_Per_Person', 'titles'],\n",
      "      dtype='object')\n",
      "Index(['Pclass', 'Sex', 'Age', 'Fare', 'Embarked', 'Cabin_Status', 'Titles',\n",
      "       'Family', 'Family_On_Board', 'Fare_Per_Person', 'titles'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(newtrain.columns)\n",
    "print(newtest.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtrain.to_csv(\"TitanicTrain.csv\", index = False)\n",
    "newtest.to_csv(\"TitanicTest.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/mukul.singh\n"
     ]
    }
   ],
   "source": [
    "cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "<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>Cabin_Status</th>\n",
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       "      <th>Family</th>\n",
       "      <th>Family_On_Board</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "      <th>titles</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>Cabin Allotted</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>Cabin Allotted</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>No Cabin Found</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>Singles</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age     Fare Embarked    Cabin_Status Titles  \\\n",
       "0         0       3    male  22.0   7.2500        S  No Cabin Found     Mr   \n",
       "1         1       1  female  38.0  71.2833        C  Cabin Allotted    Mrs   \n",
       "2         1       3  female  26.0   7.9250        S  No Cabin Found   Miss   \n",
       "3         1       1  female  35.0  53.1000        S  Cabin Allotted    Mrs   \n",
       "4         0       3    male  35.0   8.0500        S  No Cabin Found     Mr   \n",
       "\n",
       "   Family Family_On_Board  Fare_Per_Person titles  \n",
       "0       1         Singles           7.2500     Mr  \n",
       "1       1         Singles          71.2833    Mrs  \n",
       "2       1         Singles           7.9250   Miss  \n",
       "3       1         Singles          53.1000    Mrs  \n",
       "4       1         Singles           8.0500     Mr  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = newtrain.drop([\"Survived\",\"titles\"], axis = 1)\n",
    "y = newtrain.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dummies\n",
    "ohe = pd.get_dummies(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pclass', 'Age', 'Fare', 'Family', 'Fare_Per_Person', 'Sex_female',\n",
       "       'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S',\n",
       "       'Cabin_Status_Cabin Allotted', 'Cabin_Status_No Cabin Found',\n",
       "       'Titles_Master', 'Titles_Miss', 'Titles_Mr', 'Titles_Mrs', 'Titles_Ms',\n",
       "       'Titles_Others', 'Family_On_Board_Large Family',\n",
       "       'Family_On_Board_Singles', 'Family_On_Board_Small Family'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trying Sequential Feature Selector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbm = GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.8s finished\n",
      "\n",
      "[2019-12-26 09:47:36] Features: 1/21 -- score: 0.7867564534231201[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    1.1s finished\n",
      "\n",
      "[2019-12-26 09:47:38] Features: 2/21 -- score: 0.8608305274971941[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    1.3s finished\n",
      "\n",
      "[2019-12-26 09:47:39] Features: 3/21 -- score: 0.8933782267115601[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    1.4s finished\n",
      "\n",
      "[2019-12-26 09:47:40] Features: 4/21 -- score: 0.898989898989899[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    1.3s finished\n",
      "\n",
      "[2019-12-26 09:47:42] Features: 5/21 -- score: 0.9079685746352413[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    1.2s finished\n",
      "\n",
      "[2019-12-26 09:47:43] Features: 6/21 -- score: 0.9102132435465768[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    1.2s finished\n",
      "\n",
      "[2019-12-26 09:47:44] Features: 7/21 -- score: 0.9124579124579124[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    1.1s finished\n",
      "\n",
      "[2019-12-26 09:47:45] Features: 8/21 -- score: 0.9124579124579124[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    1.0s finished\n",
      "\n",
      "[2019-12-26 09:47:46] Features: 9/21 -- score: 0.9124579124579124[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    1.0s finished\n",
      "\n",
      "[2019-12-26 09:47:47] Features: 10/21 -- score: 0.9124579124579124[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    1.1s finished\n",
      "\n",
      "[2019-12-26 09:47:48] Features: 11/21 -- score: 0.9124579124579124[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    1.0s finished\n",
      "\n",
      "[2019-12-26 09:47:49] Features: 12/21 -- score: 0.9113355780022446[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.9s finished\n",
      "\n",
      "[2019-12-26 09:47:50] Features: 13/21 -- score: 0.9090909090909091[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.7s finished\n",
      "\n",
      "[2019-12-26 09:47:51] Features: 14/21 -- score: 0.9090909090909091[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.7s finished\n",
      "\n",
      "[2019-12-26 09:47:52] Features: 15/21 -- score: 0.9124579124579124[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.6s finished\n",
      "\n",
      "[2019-12-26 09:47:52] Features: 16/21 -- score: 0.9090909090909091[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.5s finished\n",
      "\n",
      "[2019-12-26 09:47:53] Features: 17/21 -- score: 0.9057239057239057[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.4s finished\n",
      "\n",
      "[2019-12-26 09:47:53] Features: 18/21 -- score: 0.9057239057239057[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.4s finished\n",
      "\n",
      "[2019-12-26 09:47:53] Features: 19/21 -- score: 0.9034792368125701[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "\n",
      "[2019-12-26 09:47:54] Features: 20/21 -- score: 0.9046015712682379[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s finished\n",
      "\n",
      "[2019-12-26 09:47:54] Features: 21/21 -- score: 0.898989898989899"
     ]
    }
   ],
   "source": [
    "sfs1 = SFS(gbm, \n",
    "           k_features=ohe.shape[1], \n",
    "           forward=True, \n",
    "           floating=False, \n",
    "           verbose=2,\n",
    "           scoring='accuracy',\n",
    "           cv=0)\n",
    "sfs1 = sfs1.fit(ohe, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: {'feature_idx': (5,),\n",
       "  'cv_scores': array([0.78675645]),\n",
       "  'avg_score': 0.7867564534231201,\n",
       "  'feature_names': ('Sex_female',)},\n",
       " 2: {'feature_idx': (4, 5),\n",
       "  'cv_scores': array([0.86083053]),\n",
       "  'avg_score': 0.8608305274971941,\n",
       "  'feature_names': ('Fare_Per_Person', 'Sex_female')},\n",
       " 3: {'feature_idx': (1, 4, 5),\n",
       "  'cv_scores': array([0.89337823]),\n",
       "  'avg_score': 0.8933782267115601,\n",
       "  'feature_names': ('Age', 'Fare_Per_Person', 'Sex_female')},\n",
       " 4: {'feature_idx': (1, 2, 4, 5),\n",
       "  'cv_scores': array([0.8989899]),\n",
       "  'avg_score': 0.898989898989899,\n",
       "  'feature_names': ('Age', 'Fare', 'Fare_Per_Person', 'Sex_female')},\n",
       " 5: {'feature_idx': (0, 1, 2, 4, 5),\n",
       "  'cv_scores': array([0.90796857]),\n",
       "  'avg_score': 0.9079685746352413,\n",
       "  'feature_names': ('Pclass', 'Age', 'Fare', 'Fare_Per_Person', 'Sex_female')},\n",
       " 6: {'feature_idx': (0, 1, 2, 4, 5, 15),\n",
       "  'cv_scores': array([0.91021324]),\n",
       "  'avg_score': 0.9102132435465768,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Titles_Mrs')},\n",
       " 7: {'feature_idx': (0, 1, 2, 4, 5, 15, 18),\n",
       "  'cv_scores': array([0.91245791]),\n",
       "  'avg_score': 0.9124579124579124,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Titles_Mrs',\n",
       "   'Family_On_Board_Large Family')},\n",
       " 8: {'feature_idx': (0, 1, 2, 4, 5, 6, 15, 18),\n",
       "  'cv_scores': array([0.91245791]),\n",
       "  'avg_score': 0.9124579124579124,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Titles_Mrs',\n",
       "   'Family_On_Board_Large Family')},\n",
       " 9: {'feature_idx': (0, 1, 2, 4, 5, 6, 15, 16, 18),\n",
       "  'cv_scores': array([0.91245791]),\n",
       "  'avg_score': 0.9124579124579124,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Family_On_Board_Large Family')},\n",
       " 10: {'feature_idx': (0, 1, 2, 4, 5, 6, 15, 16, 18, 19),\n",
       "  'cv_scores': array([0.91245791]),\n",
       "  'avg_score': 0.9124579124579124,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Family_On_Board_Large Family',\n",
       "   'Family_On_Board_Singles')},\n",
       " 11: {'feature_idx': (0, 1, 2, 4, 5, 6, 13, 15, 16, 18, 19),\n",
       "  'cv_scores': array([0.91245791]),\n",
       "  'avg_score': 0.9124579124579124,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Titles_Miss',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Family_On_Board_Large Family',\n",
       "   'Family_On_Board_Singles')},\n",
       " 12: {'feature_idx': (0, 1, 2, 4, 5, 6, 9, 13, 15, 16, 18, 19),\n",
       "  'cv_scores': array([0.91133558]),\n",
       "  'avg_score': 0.9113355780022446,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Embarked_S',\n",
       "   'Titles_Miss',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Family_On_Board_Large Family',\n",
       "   'Family_On_Board_Singles')},\n",
       " 13: {'feature_idx': (0, 1, 2, 4, 5, 6, 7, 9, 13, 15, 16, 18, 19),\n",
       "  'cv_scores': array([0.90909091]),\n",
       "  'avg_score': 0.9090909090909091,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Embarked_C',\n",
       "   'Embarked_S',\n",
       "   'Titles_Miss',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Family_On_Board_Large Family',\n",
       "   'Family_On_Board_Singles')},\n",
       " 14: {'feature_idx': (0, 1, 2, 4, 5, 6, 7, 9, 13, 15, 16, 18, 19, 20),\n",
       "  'cv_scores': array([0.90909091]),\n",
       "  'avg_score': 0.9090909090909091,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Embarked_C',\n",
       "   'Embarked_S',\n",
       "   'Titles_Miss',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Family_On_Board_Large Family',\n",
       "   'Family_On_Board_Singles',\n",
       "   'Family_On_Board_Small Family')},\n",
       " 15: {'feature_idx': (0, 1, 2, 4, 5, 6, 7, 9, 13, 15, 16, 17, 18, 19, 20),\n",
       "  'cv_scores': array([0.91245791]),\n",
       "  'avg_score': 0.9124579124579124,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Embarked_C',\n",
       "   'Embarked_S',\n",
       "   'Titles_Miss',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Titles_Others',\n",
       "   'Family_On_Board_Large Family',\n",
       "   'Family_On_Board_Singles',\n",
       "   'Family_On_Board_Small Family')},\n",
       " 16: {'feature_idx': (0, 1, 2, 3, 4, 5, 6, 7, 9, 13, 15, 16, 17, 18, 19, 20),\n",
       "  'cv_scores': array([0.90909091]),\n",
       "  'avg_score': 0.9090909090909091,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Family',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Embarked_C',\n",
       "   'Embarked_S',\n",
       "   'Titles_Miss',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Titles_Others',\n",
       "   'Family_On_Board_Large Family',\n",
       "   'Family_On_Board_Singles',\n",
       "   'Family_On_Board_Small Family')},\n",
       " 17: {'feature_idx': (0,\n",
       "   1,\n",
       "   2,\n",
       "   3,\n",
       "   4,\n",
       "   5,\n",
       "   6,\n",
       "   7,\n",
       "   9,\n",
       "   10,\n",
       "   13,\n",
       "   15,\n",
       "   16,\n",
       "   17,\n",
       "   18,\n",
       "   19,\n",
       "   20),\n",
       "  'cv_scores': array([0.90572391]),\n",
       "  'avg_score': 0.9057239057239057,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Family',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Embarked_C',\n",
       "   'Embarked_S',\n",
       "   'Cabin_Status_Cabin Allotted',\n",
       "   'Titles_Miss',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Titles_Others',\n",
       "   'Family_On_Board_Large Family',\n",
       "   'Family_On_Board_Singles',\n",
       "   'Family_On_Board_Small Family')},\n",
       " 18: {'feature_idx': (0,\n",
       "   1,\n",
       "   2,\n",
       "   3,\n",
       "   4,\n",
       "   5,\n",
       "   6,\n",
       "   7,\n",
       "   9,\n",
       "   10,\n",
       "   11,\n",
       "   13,\n",
       "   15,\n",
       "   16,\n",
       "   17,\n",
       "   18,\n",
       "   19,\n",
       "   20),\n",
       "  'cv_scores': array([0.90572391]),\n",
       "  'avg_score': 0.9057239057239057,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Family',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Embarked_C',\n",
       "   'Embarked_S',\n",
       "   'Cabin_Status_Cabin Allotted',\n",
       "   'Cabin_Status_No Cabin Found',\n",
       "   'Titles_Miss',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Titles_Others',\n",
       "   'Family_On_Board_Large Family',\n",
       "   'Family_On_Board_Singles',\n",
       "   'Family_On_Board_Small Family')},\n",
       " 19: {'feature_idx': (0,\n",
       "   1,\n",
       "   2,\n",
       "   3,\n",
       "   4,\n",
       "   5,\n",
       "   6,\n",
       "   7,\n",
       "   9,\n",
       "   10,\n",
       "   11,\n",
       "   13,\n",
       "   14,\n",
       "   15,\n",
       "   16,\n",
       "   17,\n",
       "   18,\n",
       "   19,\n",
       "   20),\n",
       "  'cv_scores': array([0.90347924]),\n",
       "  'avg_score': 0.9034792368125701,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Family',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Embarked_C',\n",
       "   'Embarked_S',\n",
       "   'Cabin_Status_Cabin Allotted',\n",
       "   'Cabin_Status_No Cabin Found',\n",
       "   'Titles_Miss',\n",
       "   'Titles_Mr',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Titles_Others',\n",
       "   'Family_On_Board_Large Family',\n",
       "   'Family_On_Board_Singles',\n",
       "   'Family_On_Board_Small Family')},\n",
       " 20: {'feature_idx': (0,\n",
       "   1,\n",
       "   2,\n",
       "   3,\n",
       "   4,\n",
       "   5,\n",
       "   6,\n",
       "   7,\n",
       "   9,\n",
       "   10,\n",
       "   11,\n",
       "   12,\n",
       "   13,\n",
       "   14,\n",
       "   15,\n",
       "   16,\n",
       "   17,\n",
       "   18,\n",
       "   19,\n",
       "   20),\n",
       "  'cv_scores': array([0.90460157]),\n",
       "  'avg_score': 0.9046015712682379,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Family',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Embarked_C',\n",
       "   'Embarked_S',\n",
       "   'Cabin_Status_Cabin Allotted',\n",
       "   'Cabin_Status_No Cabin Found',\n",
       "   'Titles_Master',\n",
       "   'Titles_Miss',\n",
       "   'Titles_Mr',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Titles_Others',\n",
       "   'Family_On_Board_Large Family',\n",
       "   'Family_On_Board_Singles',\n",
       "   'Family_On_Board_Small Family')},\n",
       " 21: {'feature_idx': (0,\n",
       "   1,\n",
       "   2,\n",
       "   3,\n",
       "   4,\n",
       "   5,\n",
       "   6,\n",
       "   7,\n",
       "   8,\n",
       "   9,\n",
       "   10,\n",
       "   11,\n",
       "   12,\n",
       "   13,\n",
       "   14,\n",
       "   15,\n",
       "   16,\n",
       "   17,\n",
       "   18,\n",
       "   19,\n",
       "   20),\n",
       "  'cv_scores': array([0.8989899]),\n",
       "  'avg_score': 0.898989898989899,\n",
       "  'feature_names': ('Pclass',\n",
       "   'Age',\n",
       "   'Fare',\n",
       "   'Family',\n",
       "   'Fare_Per_Person',\n",
       "   'Sex_female',\n",
       "   'Sex_male',\n",
       "   'Embarked_C',\n",
       "   'Embarked_Q',\n",
       "   'Embarked_S',\n",
       "   'Cabin_Status_Cabin Allotted',\n",
       "   'Cabin_Status_No Cabin Found',\n",
       "   'Titles_Master',\n",
       "   'Titles_Miss',\n",
       "   'Titles_Mr',\n",
       "   'Titles_Mrs',\n",
       "   'Titles_Ms',\n",
       "   'Titles_Others',\n",
       "   'Family_On_Board_Large Family',\n",
       "   'Family_On_Board_Singles',\n",
       "   'Family_On_Board_Small Family')}}"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Via the subsets_ attribute, we can take a look at the selected feature indices at each step:\n",
    "sfs1.subsets_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.feature_selection import ExhaustiveFeatureSelector as EFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Features: 6917/7546"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy score: 0.84\n",
      "Best subset (indices): (3, 10, 14, 17)\n",
      "Best subset (corresponding names): ('Family', 'Cabin_Status_Cabin Allotted', 'Titles_Mr', 'Titles_Others')\n"
     ]
    }
   ],
   "source": [
    "efs1 = EFS(gbm, \n",
    "           min_features=1,\n",
    "           max_features=4,\n",
    "           scoring='accuracy',\n",
    "           print_progress=True,\n",
    "           cv=5)\n",
    "\n",
    "efs1 = efs1.fit(ohe, y)\n",
    "\n",
    "print('Best accuracy score: %.2f' % efs1.best_score_)\n",
    "print('Best subset (indices):', efs1.best_idx_)\n",
    "print('Best subset (corresponding names):', efs1.best_feature_names_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(ohe,y,test_size = 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/anaconda3/lib/python3.6/site-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "scaled_train = pd.DataFrame(sc.fit_transform(xtrain), columns = xtrain.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "scaled_test = pd.DataFrame(sc.transform(xtest), columns = xtest.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score 0.8435754189944135\n",
      "Cohen Kappa 0.6625370320495556\n",
      "Matthew's Correlation 0.6656382860451193\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbm = GradientBoostingClassifier()\n",
    "pred = gbm.fit(scaled_train,ytrain).predict(scaled_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score, cohen_kappa_score, matthews_corrcoef\n",
    "print(\"Accuracy Score\", accuracy_score(ytest, pred))\n",
    "print(\"Cohen Kappa\", cohen_kappa_score(ytest, pred))\n",
    "print(\"Matthew's Correlation\", matthews_corrcoef(ytest, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
    "k = KMeans(n_clusters=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_train[\"KMeans_Clusters\"] = k.fit_predict(scaled_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_test[\"KMeans_Clusters\"] = k.fit_predict(scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score 0.8491620111731844\n",
      "Cohen Kappa 0.6754851272409856\n",
      "Matthew's Correlation 0.6778885859702577\n"
     ]
    }
   ],
   "source": [
    "# After Clustering\n",
    "#from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbm1 = GradientBoostingClassifier()\n",
    "pred = gbm1.fit(scaled_train,ytrain).predict(scaled_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score, cohen_kappa_score, matthews_corrcoef\n",
    "print(\"Accuracy Score\", accuracy_score(ytest, pred))\n",
    "print(\"Cohen Kappa\", cohen_kappa_score(ytest, pred))\n",
    "print(\"Matthew's Correlation\", matthews_corrcoef(ytest, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping K Means Labels\n",
    "scaled_train = scaled_train.drop(\"KMeans_Clusters\", axis = 1)\n",
    "scaled_test = scaled_test.drop(\"KMeans_Clusters\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg = AgglomerativeClustering(n_clusters=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_train[\"Agg_Clusters\"] = agg.fit_predict(scaled_train)\n",
    "scaled_test[\"Agg_Clusters\"] = agg.fit_predict(scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score 0.8491620111731844\n",
      "Cohen Kappa 0.6736884747822565\n",
      "Matthew's Correlation 0.6777094010108027\n"
     ]
    }
   ],
   "source": [
    "# After Clustering\n",
    "#from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbm2 = GradientBoostingClassifier()\n",
    "pred = gbm2.fit(scaled_train,ytrain).predict(scaled_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score, cohen_kappa_score, matthews_corrcoef\n",
    "print(\"Accuracy Score\", accuracy_score(ytest, pred))\n",
    "print(\"Cohen Kappa\", cohen_kappa_score(ytest, pred))\n",
    "print(\"Matthew's Correlation\", matthews_corrcoef(ytest, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "Not much of a difference is found when we compare K Means and Agglomerative Clustering on Titanic Dataset but yes, before clustering and after clustering, the accuracy has improved although the improvement is small but it shows a positive sign.\n",
    "\n",
    "Moreover, through clustering, it would have become easy for the machine to learn the pattern through groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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