Titanic ML with 0.80 Kaggle & RFE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyforest import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"~/Downloads/Titanic/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "test =pd.read_csv(\"~/Downloads/Titanic/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combining Train and Test\n",
    "combined = pd.concat([train,test], ignore_index = True, sort = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.isnull().sum()[combined.isnull().sum()!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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",
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      ],
      "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[combined.Embarked.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find Embarked\n",
    "df= pd.DataFrame(combined.groupby([\"Survived\", \"Sex\"])[\"Embarked\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1132f53c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.plot(kind = \"barh\")\n",
    "\n",
    "# This plot shows that majority of Females who survived were from Southampton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling S in Embarked\n",
    "combined.loc[combined.Embarked.isnull(),\"Embarked\"]=\"S\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <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",
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      ],
      "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fare\n",
    "combined[combined.Fare.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b7bb668>"
      ]
     },
     "execution_count": 12,
     "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": 13,
   "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></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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(combined.groupby([\"Survived\", \"Sex\", \"Pclass\"])[\"Fare\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "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": 15,
     "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": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Cabin.dropna().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Cabin_Status\"] = pd.Series(combined.Cabin.apply(cabins))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Titles\"] = pd.Series(titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 24,
   "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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Titles\"] = combined.Titles.apply(titleignore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SIBSP and Parch\n",
    "family = combined.SibSp+combined.Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "family.replace(to_replace = 0, value =1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Family\"] = pd.Series(family)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b9593c8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jwPdTm7HAPkBnvY7OmDKJxxzHYGbWEnknI10n6ULg62T3Ut4PrGtwnz1xDBFxF1mhqaRaHMNqtr+8Vv7drLL3ncCfV2h2JHBdSmA1M7OC5C1AHyQ7o1iaPv+I7DmbfouIeyXdLqmt7FmgSu1aFccwhm33tszMrCC5ClBEbJb0H8CqiHh4oDstOI7h2qL2bWZm2+S6ByRpAbAGuDF9npVn2hwzM7Nq8g5C+BQwh/QMT0SsAdpb1CczMxsF8hagLRHhaQHMzKxp8g5CeFDS8WTP8EwD/g64q3XdMjOzkU7ZXJ11GkmvAf4JOCwtugn4bERsbmHfCjdut2mx28lf6dc6fm7IzEYzSV0R0ZGnbd5LcH8WEf8UEQek1ycaLT5NyAMaJ+nqlPPzU0ntVdrNl/RwaresbPmKdBZnZmYFyluAvizp55L+VdKbB7jPgeYBnQI8GxH7AGcDn+/dIBW3rwHvAvYDjpO0X/r6fOBjA/wNZmY2QLkKUJoPbi7wO+CClDT6iQb3OaA8IOAo4LL0/jrg0FLaaZk5wPqI+EVEvASsSOsB3Am8U1Le+19mZtYCufOAIuK3EfFV4G/Jngn65/7urEl5QFOAx1OftgCbgMnV2iQ9eUARsZVsfriZVfroPCAzs0GQ90HUfSV9WtKDZHO53UWV+djqqJYH1AEcD3xF0p/W606FZb1HUgwoDyjN+t3R9ppJdbpiZmaNynsZ6hLgG8BhpfyeBjUjD6gb2BPoTpfRJgHPVGlT4jwgM7MhJu89oIMi4pwBFp9m5QGtBE5O748Bbou+Y8lXA9MkTU2X/Ran9UqmA2sxM7PC1IvkviYijpX0ANtfwhIQEbF/A/ss5QHdQpYHtFzSVrJi2DsPqNKw7IuAKyStJzvzWZz6ujtwYUQcERFbJJ1O9rxSG3BxRKxN7XYhG3m3oV5HZ0yZRKef6zEza4maD6JK2i0iNkjaq9L3EfGrfu9QegtwRkScWKfdTa2IZJD0UeC5iLioXtuOjo7o7KybW2dmZkl/HkSteQZUdpbwV8A1EfGbgXZuCOQBbQSuaNG2zcwsp7zDsCcCP5B0p6QPp8tYDYuIi2sVn1aKiEuchmpmVry8gxA+ExFvBj5MNnz5Dkm3tLRnZmY2ouV+EDV5Evgt8DTwJ83vjpmZjRZ5H0Q9NT2jcyvZw6R/0+AIODMzMyD/GdAbgb+PiDdHxKfKhkqPaA/8ZhPty75XdDfMzEakugVI0quA96QY7gHrHceQlk2U9BtJ5+VYf6GktZK2Sqo61M9xDGZmQ1vdApQm77xP0hubtM/ecQwA/wrckXP9B8mGhf+oWgPHMZiZDX1554LbDVgr6WfA70sLc+b39HYC2cSjAEiaDewC3AjUfXgpItal9Wo164ljSG1LcQwPkcUxXCppjIdjm5kVJ28B+kwzdtY7jiFd3vsScCJwaDP2kVSKYzgQsjO6NI3PTKCrQh+XAEsA2ibu3MQumZlZuVwFKCLyXh6rp3ccw2nAqoh4vM4ZTX/ljWPoU4Ai4gLgAoBxu02rPk+RmZkNSK4CJOl5tv0D/mpgLPD7iJjYz/31jmN4G3CIpNOAHYBXS3ohIpZVXDs/xzGYmQ1xec+AXlf+OSWXzunvziLiWUltksZHxOaIOKFsmx8AOkrFR9LlwHkR8bP+7oeyOAbgN2QzZh9f9r3jGMzMCtbfmRAAiIhvA/Ma3GcpjqGe/YE+kQmS3iupm+zs6XuSbkrLd5e0KvVvC1CKY1hHNpFqQ3EMjzmOwcysJfJegvurso+vIhut1uj9kfOAM8jygHpExKXApWl/E4FHIuLx3itHxLeAb1VY/gRwRNnnVcCqCvs/HljeYN/NzKxJ8o6Ce0/Z+y3AY2TDmvstTxxDRDwHLGxk+zk4jsHMbAjIew/og83caURc3Mzt9XPflxS1bzMz26bmPSBJf1OatkaZiyVtknS/pLcOThfNzGwkqjcIYSnZ5TaA48ge3tyb7B7OOa3rlpmZjXT1CtCWiHg5vT8SuDwino6IW4DXtrZrZmY2ktUrQFsl7SZpPNlUOeUj1ya0rltmZjbS1RuE8M9AJ9AGrCx7luYvgV+0uG+Fq5cH5GeEzMwaV/MMKCJuAPYC9o2Ivyn7qhNY1OhOm5AJ9AZJN0t6JP19fZV2J6c2j0g6uWz5LdXWMTOzwZEnD2hLRDzba/G+wKQB7HegmUDLgFsjYhpZTHifueMkvQH4FNks2HOAT5UVnSvIJkI1M7OCNDQVD/AR4AZJVze4/gnAd0ofyjKBfpBz/aOAy9L7y4CjK7Q5HLg5Ip5JBfRmYH76biXZqD4zMytI3pkQthMRJwNIel29tr01KRNol9JcbhGxQdKfVGhTKRNoSlrnWUnjJE2OiKd79c95QGZmgyDXGZCkb0p6dyoWPSLi+Qb2WTUTqIFt1ZI3E2j7BhEXRERHRHS0vWYgVxnNzKyWvJfgziebxPMRSWdKetMA9lkpE+h0SY8BZwEnSTqzzjb+R9JuAOnvkxXaOBPIzGwIy1WAIuKWlN3zVrKZEW6WdJekD0oa258dpvsxbenZIiLihIh4Y0S0A/9A9rBrTyaQpEq5QyuB0qi2kym7n1TmJuAwSa9Pgw8OS8tQFr+6K9tmeTAzs0GW+x6QpMnA+8nu1dwLXEmW63MyMLef+y1lAt1Sp13FTCDgTOAaSacAvybNnC2pA/jbiPjriHhG0r+ShdMB/EtEPJPezwZ+knKDqpoxZRKdftbHzKwlFFE/1kfS9cCbyIYvX1oe5iapMyI6+rVT6S3AGRFxYo02E4GLIqLpsQySziF7sPbWWu06Ojqis7Oz2bs3MxuxJHXlrQl5z4DOi4jbKn3R3+KT1ik6E+jBesXHzMxaq2YBKk9C7ZWKCkBEXN/ojgvOBPrPovZtZmaZemdA76nxXQANFyAzMxvdahagZiehmpmZldS7BPf+iPi6pDMqfR8RX25Nt8zMbKSrdwmuFDrX7yl3RoJKcQyOYDAza456l+CWp7+faeZOJU0AbgTmkc1QcD1Z5tBY4NyI+I86678BuBpoJ3uY9NgKM3aTIhg+kT5+NiIuS8tvARZWWsfMzAZH3rngpkr6sqTrJa0svQaw3/I4hg3A/4qIWWTRCcsk9ZmjrRfHMZiZDXN5nwP6NnAR8F1gaxP2ewLZ3HJExEtly8eRrygexbbZFy4Dfgj8Y682PXEMAJJKcQzfIJvK507gcw313szMBixvAdocEV9txg57xzGkZXsC3wP2Af5vRDxRZfUSxzGYmQ1zeWfDPkfSpyS9TdJbS68G99k7joGIeDwi9icrQCdL2qXBbZdzHIOZ2RCW9wxoBtkkpPPYdgku0uf+6h3H0CMinpC0FjgEuK7GNv5H0m7p7KdWHMPcss97kF2qK3Ecg5lZgfKeAb2X7LLZX0bEO9KrkeLTJ45B0h5pVBxpkMDBwMPps+MYzMxGqLxnQPcBO1L5TKMR5XEM+wJfkhRkl83OiogHUjvHMZiZjVB5C9AuwM8lrQb+WFoYEQsa3O95wBnALRFxM1mh2U6KY3ikUlR3GjhwaIXlncBfl32+GKg06emJwP9rsO9mZtYEeQvQp5q5U8cxmJlZrgIUEXc0e8eOYzAzG93yzoRwkKTVkl6Q9JKkVyQ91+rOmZnZyJV3FNx5wHHAI8AEsvss57WqU2ZmNvLlvQdERKwvu2dziaS7WtgvMzMb4fIWoBfTFDprJH2BbGj0a+usY2ZmVlXeAnQi2eW604GPAnsC72tVp4aKSnlAeTk3yMystpr3gCS9ESAifhURmyPiuYj4TEScERHrG9mhpAmS7pDUJmkvSV2S1khaK+lvc6y/MLXdmh48rdZuvqSHJa2XtKxs+QpJ0xrpu5mZNU+9QQjfLr2R9M0m7XOgWUAPAn8F/KhaA0ltwNeAdwH7AcdJ2i99fT7wsYH9BDMzG6h6Bah8Rum9m7TPE0hzt0XESxFRmlkhVxZQRKyLiIfrNJsDrI+IX6S8oRVkGUKQ5QC9U1LuARhmZtZ89f7BjyrvG1ItC0jS/WTZPZ/PkQWUR60soK3AemBmlT4ukdQpqfOVFzc1oStmZlZJvQI0U9Jzkp4H9k/vn5P0fIMPog7pLKDUH+cBmZkNgpqXoSKircn7a0YWUB7dZCP1SvYAys+snAVkZlawvDMhNEWTsoDyWA1MkzQ1XfZbTJYhVDIdWNvgts3MrAmKuBE/oCwgSe8FzgV2Br4naU1EHJ5Gz10YEUdExBZJp5MF0LUBF0fE2rT+LsAfIqJSztB2nAdkZtY6RRSggWYBfQv4VoXlTwBHlH1eBayqsP/jgeUN997MzJpiUC/BQZYFBNyentWp1ua5iGhVFtBG4LIWbdvMzHIq5FmYgrOALilq32Zmts2gnwGZmZmBC5CZmRXEBcjMzArhAmRmZoUY9EEI6cHTG4F5wAyy2aknAq8An4uIq+usPw64HJgNPA0sKp9brqzdfOAcsueALoyIM9PyFcAnI+KRen0dSB7QQDhLyMxGgyLOgMrjGF4EToqINwPzga9I2rHO+qcAz0bEPsDZwOd7N3Acg5nZ0FdEASqPY/jv0plIepD0SbIZDmo5im3P8VwHHCqp9+SjjmMwMxviBrUAVYpjKPtuDvBq4NE6m+mJWoiILcAmYHK1NonjGMzMhpjBPgPqE8cAIGk34Argg6lA1FIvaiFPG8cxmJkVbLALUJ84hjTv2/eAT0TET3JsoydqIV1GmwQ8U61N4jgGM7Mhpug4hleTTSx6eURcW95W0r+nma97WwmcnN4fA9wWEb3PgBzHYGY2xBUdx3As8HZgsqQPpO8/EBFryIZor6yw/kXAFZLWk535LAZwHIOZ2fCivicPLd6h9BbgjIg4sU67myLi8Bbs/6PAcxFxUb22HR0d0dnZ2ewumJmNWJK6IqIjT9shGceQ2jW9+CSOYzAzGwIcx2BmZoXwXHBmZlYIFyAzMyuEC5CZmRXCBcjMzArhCTlrKCqOob8c32Bmw9GgnwFJmiDpDkltkmZJulvSWkn3S1qUY/23S7pH0hZJx9RoN1vSA5LWS/pqacZsSWdJmtfM32RmZv03HPOAfg18ALiqTrvzgSXAtPSan5afCyxrrOtmZtYswy4PKCIei4j7gaqzZqfZtSdGxN1pnrjLgaPT+r8im/pn12b8GDMza8xwzAPKYwrZjNglPXlAyT3AwVX66DwgM7NBMBzzgPJwHpCZ2RA3HPOA8ugmywAqcR6QmdkQMxzzgPLsZwPwvKSD0ui3k0j3nZLpwIONbNvMzJpj2OUBSTqArGi9HniPpM+kUXRIWhMRs1LTU4FLgQnA99MLSWOBfYC6OQvOAzIza50iCtB5wBnALRHxdeDrVdqNjYi7ey+MiNVsf3mt/LtZZe87gT+v0OxI4LqI2NLfjpuZWfOMxjygMcCXWrRtMzPLaTTmAV1bv5WZmbWaJyM1M7NCuACZmVkhXIDMzKwQLkBmZlaIQR+EIGkCcCMwLyJekXQjcBDwXxFxZI713w58BdgfWBwR11VpN5ttzwGtApZGREg6C1gVEbfV29dwyQMyy8O5UTbUFB3HAPBF4MR+rO84BjOzEaDQOAaAiLgVeD7vyo5jMDMbGYZMHEOTNRzHYGZmg2NIxDG0QMNxDM4DMjMbHIXHMbRIw3EMzgMyMxschcYx1OI4BjOzka3oOAYk3Qm8CdhBUjdwSkTchOMYzMxGtELjGAAi4pAq7RzHYGY2gjmOwczMCuE4BjMzK4TngjMzs0K4AJmZWSFcgMzMrBAuQGZmVohCBiEMF45jMLPRZjBjOwb9DEjSBEl3lIZhS7pR0kZJN+Rcf5ykqyWtl/RTSe1V2s2X9HBqt6xs+QpJ05rxW8zMrHHDMQ/oFODZiNgHOBv4fO8Gqbh9DXgXsB9wnKT90tfnAx9rsO9mZtYkwy4PCDgKuCy9vw44NM33Vm4OsD4ifhERLwEr0noAdwLvlOTLj2ZmBRqOeUBTgMcB0nQ6m4DJ1dokPXlAEbEVWA/MrNJHxzGYmQ2C4ZgHVC/rJ0+bqnlAjmMwMxscwzEPqBvYEyBdRpsEPFOtTZI7D8jMzAbHcMwDWgmcnN4fA9wWEb3PgFYD0yRNTZf9FrN9tMN0YG2/f4CZmTXPElbJAAADqElEQVTNsMsDAi4CrpC0nuzMZ3Hazu7AhRFxRERskXQ6cBPQBlwcEWtTu12AP6TQupqcB2Rm1jrDMQ9oM7CwwvIngCPKPq8CVlXY7vHA8v5328zMmmk05gFtZNswbjMzK8hozAO6pKh9m5nZNup7/95KJD0PPFx0P4aonYCniu7EEObjU52PTW3D/fjsFRE752no2QBqezgiOoruxFAkqdPHpjofn+p8bGobTcfHcQxmZlYIFyAzMyuEC1BtFxTdgSHMx6Y2H5/qfGxqGzXHx4MQzMysED4DMjOzQrgAVVAtTXU0kXSxpCclPVi27A2Sbpb0SPr7+rRckr6ajtf9kt5aXM9bT9Kekm6XtE7SWklL0/JRf3wkjZf0M0n3pWPzmbR8akowfiQlGr86Lc+VcDzSSGqTdG8pCXq0Hh8XoF7qpKmOJpcC83stWwbcGhHTgFvTZ8iO1bT0WkKWOjuSbQH+T0TsCxwEfDj9N+LjA38E5kXETGAWMF/SQWTJxWenY/MsWbIx5Eg4HqGWAuvKPo/K4+MC1FetNNVRIyJ+RN+Yi/I02suAo8uWXx6ZnwA7StptcHo6+CJiQ0Tck94/T/YPyRR8fEi/8YX0cWx6BTCPLMEY+h6begnHI4qkPYB3Axemz2KUHh8XoL6qpqkau5RmEU9//yQtH7XHLF0SeQvwU3x8gJ7LS2vIgh9vBh4FNqYEY9j+9+dJOB5pvgJ8DNiaPk9mlB4fF6C+8iSu2vZG5TGTtAPwTeDvI+K5Wk0rLBuxxyciXomIWWRBkHOAfSs1S39H1bGRdCTwZER0lS+u0HRUHB8XoL7qpamOZv9TunSU/j6Zlo+6YyZpLFnxuTIirk+LfXzKRMRG4Idk98l2TAnGsP3vz5NwPJIcDCyQ9BjZ5f15ZGdEo/L4uAD1VS9NdTQrT6M9GfhO2fKT0mivg4BNeQL/hqt0Df4iYF1EfLnsq1F/fCTtLGnH9H4C8E6ye2S3kyUYQ99jUy/heMSIiI9HxB4R0U72b8ttEXECo/X4RIRfvV5kwXb/TXbt+p+K7k9Bx+AbwAbgZbL/F3YK2bXnW4FH0t83pLYiGzn4KPAA0FF0/1t8bP6C7DLI/cCa9DrCxycA9gfuTcfmQeCf0/K9gZ8B64FrgXFp+fj0eX36fu+if8MgHqu5wA2j+fh4JgQzMyuEL8GZmVkhXIDMzKwQLkBmZlYIFyAzMyuEC5CZmRXCBcjMzArhAmRmZoVwATIzs0L8f7phrXrQ3Z34AAAAAElFTkSuQmCC\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": 30,
   "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": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Family_On_Board\"] = pd.Series(combined.Family.apply(family_cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 32,
     "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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Fare_Per_Person\"] = combined.Fare/combined.Family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Miss'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\"McGowan, Miss. Anna Annie\").split()[1][:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "titles = []\n",
    "for i in combined.Name:\n",
    "    titles.append(i.split()[1][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mr'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Name[1279].split()[1][:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.DataFrame(titles, columns = [\"titles\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"titles\"] = d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "agena = combined[combined.Age.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 40,
     "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": 41,
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=108.32631005902482, pvalue=1.796708368328782e-43)"
      ]
     },
     "execution_count": 42,
     "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": 43,
   "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": 44,
   "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>Sun, 22 Dec 2019</td> <th>  Prob (F-statistic):</th> <td>3.32e-46</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:40:44</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:                Sun, 22 Dec 2019   Prob (F-statistic):           3.32e-46\n",
       "Time:                        21:40:44   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": 44,
     "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": 45,
   "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": 46,
   "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>Sun, 22 Dec 2019</td> <th>  Prob (F-statistic):</th> <td>1.70e-74</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:40:44</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:                Sun, 22 Dec 2019   Prob (F-statistic):           1.70e-74\n",
       "Time:                        21:40:44   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": 46,
     "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": 47,
   "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>Sun, 22 Dec 2019</td> <th>  Prob (F-statistic):</th> <td>2.45e-17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>21:40:44</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:                Sun, 22 Dec 2019   Prob (F-statistic):           2.45e-17\n",
       "Time:                        21:40:44   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": 47,
     "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": 48,
   "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": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for Ms, if Less then Put it in Others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(combined.groupby([\"Pclass\", \"Sex\", \"Titles\", \"Family_On_Board\"])[\"Age\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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",
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       "    </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": 51,
     "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": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.loc[combined.Age.isnull(), \"Age\"] =28.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": 54,
     "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": 55,
   "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": 56,
   "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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 58,
   "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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtrain.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtest = combined_new.loc[891:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtest.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 62,
   "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": 63,
   "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": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtrain.to_csv(\"TitanicTrain.csv\", index = False)\n",
    "newtest.to_csv(\"TitanicTest.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/mukul.singh\n"
     ]
    }
   ],
   "source": [
    "cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Without considering Multicollinearity in Categorical Variables\n",
    "\n",
    "In a regression model, these values can be represented by dummy variables - variables containing values such as 1 or 0 representing the presence or absence of the categorical value.\n",
    "\n",
    "By including dummy variable in a regression model however, one should be careful of the Dummy Variable Trap. The Dummy Variable trap is a scenario in which the independent variables are multicollinear - a scenario in which two or more variables are highly correlated; in simple terms one variable can be predicted from the others.\n",
    "\n",
    "The solution to the dummy variable trap is to drop one of the categorical variables (or alternatively, drop the intercept constant) - if there are m number of categories, use m-1 in the model, the value left out can be thought of as the reference value and the fit values of the remaining categories represent the change from this reference.\n",
    "\n",
    "If the proportion of cases in the reference category is small, the indicator variables will necessarily have high VIFs, even if the categorical variable is not associated with other variables in the regression model.\n",
    "\n",
    "Suppose, for example, that a marital status variable has three categories: currently married, never married, and formerly married. You choose formerly married as the reference category, with indicator variables for the other two. What happens is that the correlation between those two indicators gets more negative as the fraction of people in the reference category gets smaller. For example, if 45 percent of people are never married, 45 percent are married, and 10 percent are formerly married, the VIFs for the married and never-married indicators will be at least 3.0.\n",
    "\n",
    "Is this a problem? Well, it does mean that p-values for the indicator variables may be high. But the overall test that all indicators have coefficients of zero is unaffected by the high VIFs. And nothing else in the regression is affected. If you really want to avoid the high VIFs, just choose a reference category with a larger fraction of the cases. That may be desirable in order to avoid situations where none of the individual indicators is statistically significant even though the overall set of indicators is significant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummytrain = pd.get_dummies(newtrain)\n",
    "dummytest = pd.get_dummies(newtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 53)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummytrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtr = dummytrain.drop(\"Survived\", axis =1)\n",
    "ytr = dummytrain.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 679,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 681,
   "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(xtr), columns = xtr.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "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(dummytest), columns = dummytest.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtree = DecisionTreeClassifier(criterion = \"entropy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 660,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = dtree.fit(xtr,ytr).predict(dummytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": pred})\n",
    "solution.to_csv(\"DTREE NEW.csv\", index = False) # 0.74162"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/mukul.singh\n"
     ]
    }
   ],
   "source": [
    "cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(criterion=\"entropy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = rf.fit(scaled_train,ytr).predict(scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": pred})\n",
    "solution.to_csv(\"RF NEW.csv\", index = False) # 0.77990 | 0.76"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 691,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12dcc1f98>"
      ]
     },
     "execution_count": 691,
     "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": [
    "pd.DataFrame(rf.feature_importances_, index = dummytest.columns, columns = [\"Features\"]).sort_values(by = \"Features\").plot(kind = \"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"n_estimators\" :[50,100, 200, 500],\n",
    "         \"criterion\":[\"gini\", \"entropy\"],\n",
    "         \"max_depth\" : [2,4,5,6,7,8],\n",
    "         \"max_features\":[\"auto\", \"sqrt\", \"log2\"],\n",
    "         \"oob_score\":[True, False]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "grid = GridSearchCV(estimator=rf, param_grid=params, cv = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 700,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = grid.fit(scaled_train, ytr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 701,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'max_depth': 8, 'max_features': 'auto', 'n_estimators': 100, 'oob_score': False}\n"
     ]
    }
   ],
   "source": [
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(criterion='gini', max_depth=8, \n",
    "                            max_features='auto', n_estimators=100, \n",
    "                            oob_score=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = rf.fit(scaled_train,ytr).predict(scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": pred})\n",
    "solution.to_csv(\"RF NEW.csv\", index = False) # 0.78947"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = knn.fit(xtr,ytr).predict(dummytest)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": pred})\n",
    "solution.to_csv(\"KNN Titanic Model.csv\", index = False) # 0.66985"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 706,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 707,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "pred = lg.fit(xtr,ytr).predict(scaled_test)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": pred})\n",
    "solution.to_csv(\"Log Titanic Model.csv\", index = False) # 0.77033 | 0.71770"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 708,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbm = GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = gbm.fit(scaled_train,ytr).predict(scaled_test)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": pred})\n",
    "solution.to_csv(\"GBM Titanic Model.csv\", index = False) # 0.77511 |0.755"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "vc = VotingClassifier(estimators=[(\"RF\", rf),(\"LG\", lg), (\"GBM\", gbm), (\"bagg\", bag)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "pred = vc.fit(scaled_train,ytr).predict(scaled_test)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": pred})\n",
    "solution.to_csv(\"VC Titanic Model.csv\", index = False) # 0.77511 | 0.78468"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 733,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "pred_vc = vc.fit(newdata, dummytrain.Survived).predict(newtest)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": pred_vc})\n",
    "solution.to_csv(\"VC Selective Feature Model.csv\", index = False) # 0.78468"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CvmcBC2xfVPpZubsGIYQQOp7oDIRyFtoeBCBpNHAC8Mv2bFAkEIZIvwuh9cQAwlDNw8CWAJK+ImmapKcl3Vy6o6RvSHoif36HpLXz9sMkzcjbH8rbtpf0uKSpucy+bXpWIYQQlok7A6EiSasDnwH+Kml74HRgqO23JG1Q5pA7bf8mH/tTUjzxZcCZwP62X81jCCDdbbjE9mhJa5DyCUIIIbSDuDMQyllL0lRgMvB34DpSsNDttt8CsP2vMsf1k/SwpOnACGD7vH0CMErSN1h+0Z8I/EjSD4DNbC8sLSziiEMIoW1EZyCUs9D2oPzfSTkbQKRkwcaMAr5tuz/wE6AHgO0TgDOATYCpkja0fQtwELAQGCtpn9LCbF9je4jtId3W7tViJxdCCGFF8Zgg1Oo+4C5JF9t+W9IGZe4OrAu8nlMFRwCvAkjawvYkYJKkzwGbSOoFvGT7UkmfBAYA91eqPBIIQwih9URnINTE9kxJ5wIP5qmHU4BjS3b7H1LS4CukqYnr5u0X5gGCInUqngZOA74s6QPgn8DZrX4SIYQQypIdi8GFjm/IkCGePHlyezcjhBA6FUlP2h5Sbb8YMxBCCCF0cdEZCMtIWpLn/Rf+a2iBMk+Q9JX8epSkQ5tbZgghhJYVYwZCsWXJgy3F9lUtUU4kEHYckQQYwqon7gyERklqyNkBT+X/dsvbh0l6UNLvJT0v6XxJI3Kq4HRJW+T9zpJ0akmZwyXdVfR+X0l3tu2ZhRBCKIjOQCi2VtEjgsLF+k1gX9s7AkcAlxbtPxD4LtAfOBrYyvbOwLXASY3Ucz+wraSN8vvjgBta8DxCCCHUIR4ThGLlHhN0By6XNAhYAmxV9NkTtl8HkDQbGJe3Twf2rlSJbee1Db4s6QZgV+ArpftJOh44HqDbehuVfhxCCKGFRGcgVHMK8AbpLsBqwKKizxYXvV5a9H4p1f+3dQNwTy5vjO0PS3ewfQ1wDcCavfvGHNgQQmgl0RkI1fQC5tpeKukYWmhBIduvSXqNFFO8b0uUGUIIoWmiMxCquQK4Q9JhwAPAey1Y9mhgI9vPVNsx4ohDCKH1RAJhaDeSLgem2L6u2r6RQBhCCPWrNYEw7gyEdiHpSdJdhu+3d1tCCKGri85AaBe2B7d3G0IIISSRMxBCCCF0cXFnYBUgaUPS0sAAHyflAczL79+3vVteZ2A327fkY4YBp9o+sIXaYOC3to/O71cHXgcmtUQdEUfcdBEfHEKoJjoDqwDbbwODIMX/AgtsX1SyWwNwFHBLKzXjPaCfpLVsLyRNF3y13I6SVi+XKxBCCKF9xGOCVZykBfnl+cAeOWr4lJJ91pF0vaQnJE2R9Pm8ffu81sBUSdMk9a1S3V+Aws/QI4Fbi+o4S9I1ksYBN9VStqTjJU2WNHnJ+/Ob+A2EEEKoJjoDXcdpwMO2B9m+uOSz04H7be9EihG+UNI6wAnAJTmieAgwt0odvwO+JKkHMACYVPL5YODzto+qpWzb19geYntIt7V71XWyIYQQahePCQLAfsBBRasL9gA2BSYCp0vqA9xp+4XGCrE9LY9NOBL4c5ld7s6PEKi37BBCCK0nOgMBQMAXbT9Xsn2WpEmkW/9jJX3d9v1VyrobuAgYBmxY8tmy9ELbt9RTdiQQhhBC64nHBF3Hu8C6FT4bC5wkSQCSdsj/fhJ4yfalpIv8gBrquR442/b0xnZqYtkhhBBaQXQGuo5pwIeSni4dQAicQ1qqeJqkGfk9wBHADElTgW2Am6pVYnuu7UtqaE/dZYcQQmgdsTZB6BRibYIQQqhfrWsTxJ2BEEIIoYuLAYSdUHslDpbUOxD4P+DvwHBgPkWJg5IOArazfX5T6ysWCYSVRcJgCKG5ojPQCbVX4mBJvQuAV0gdjoWSPkNR4qDtu0kDA0MIIXRw8ZhgFdOBEgePlXR5fn2YpBl58OJDTawrhBBCK4nOwKqrIyQOFpwJ7G97IHBQ3la1rogjDiGEthGdga5pP+C0PK1vPCsmDv5I0g+AzYrSAsuyPY30OKJS4mDBBGCUpG8A3fK2qnVFHHEIIbSNGDPQNbVV4iAAtk+QtEsud6qkQZFAGEIIHUfcGVh1dZjEQUlb2J5k+0zgLWCTSCAMIYSOIzoDq66OlDh4oaTpua6HgKebUlcIIYTWEQmEoVOIBMIQQqhfJBCGEEIIoSatPoBQ0seBXwE7AYuBOcDJtp+vsH8D8Cfb/cp8di3wS9vP1NmGrYGrgfWBNUlT7o6XNAj4hO3GRsJT6351tqk76fb8F0nfy/vAj23/pZFj5gBDbL9Vsv0EUvJgXbfa8+OD84CP2Z6ftw0jJxVK+hZwFkVhQtnwHECEpIOB5wt/E0nHAuNsv1ZHOxqo8DcviATC8iJ9MITQElq1M5AHqN0F3Gj7S3nbIOBjQNnOQGNsf72JTbkUuNj2H3Mb+ufthTnu1S7yte5Xj3OA3kA/24slfQzYqykF2b6qiW04EngCOAQYVebz94DbbH+7kTIOBv4EFDpoxwIzgJo7AyGEENpXaz8m2Bv4oPhiZXuq7Ycl9ZR0n6Sn8uCyzxcdt7qkG3My3e2S1gaQNF7SkPx6gaRz8wC5x/LFtJLeFIXa2J4uaQ3gbOCInIJ3hKSdJT2aU/kelbR1hf3OknRqobycrteQk/3uzW2aIemIco3J5/MN4CTbi3Ob3rD9+/z5lTlsZ6akn5QcPjIn9z0uacu8/7L25O/ogvz585L2qNCGLYCewBmkTkGjJG2W/17T8r+bStqNFCJ0Yf5ufkDqNI3O79eSNFjSg5KelDRWUu9c3uD8PU0EvlWt/hBCCK2ntTsD/YAnK3y2CDjE9o6kTsMvClPdgK2Ba2wPAP4NnFjm+HWAx3Kq3UOki2slFwP3S/qLpFMkrW/7P6RkvNtySt9twLPAnrZ3yJ/9rMJ+lRwAvGZ7YL7l/dcK+20J/N32vyt8fnoe8DEA2EtS8bS7f9veGbic9PilnNXzPicDP66wTyE++GFga0kfrXhWyeXATflvMhq41PajpGmBI/N3cwEwGRiRkwU/BC4DDrU9mDQN8dxc3g3Ad2zvWqnCSCAMIYS20Z4DCAX8TNI04H+BjUmPDwD+YXtCfv1bYPcyx/+HdHsaUoejoVJFtm8AtgXGkMJxHpO0ZpldewFj8hS4i4Ht6zgfgOnAp/Mv8z0Kz+Gb4HBJTwFTchu2K/rs1qJ/K11I78z/Nva9fAn4ne2lef/DqrRpV5YvenQz5f8mpbYmdQj/lqcQngH0kdQLWN/2g0XlrSQSCEMIoW20dmdgJjC4wmcjgI2AwflX5BukWFyA0vmO5eY/fuDl8yKXUGX8g+3XbF9v+/OkX6zlBqudAzyQf9V/rqg9pT5kxe+uR67jedL5TgfOk3RmheNfBDaVtFIokKTNgVNJg/QGAPeWtMMVXhdbnP8t+73kOw19SRfpOaSOQdVHBSVqmZMqYGa+azDIdn/b++XtMac1hBA6iNaeTXA/6df/N2z/BkDSTsDapF/hb9r+QNLewGZFx20qaVfbE0kXqUea0whJBwD35bo+TorNfZX0q7n4gtyL5SPnjy3aXprmNwc4MJe9I7B5fv0J4F+2f6u0emBxGcvYfl/SdcClkr5p+z/5WfpwUkfiPWB+HgfxGdL6AQVHkFYkPIKU798URwJn2T6vsEHSy5I2a+SYR0mdhptJHbnC36T0uyl+/xywUeFvmWdQbGV7pqT5kna3/Ugur1ERRxxCCK2nVe8M5F/uhwD7SpotaSZpqtprpOfOQyRNJl0Mni06dBZwTH6EsAFwZTObsh8p7e5pUhTvSNv/BB4AtisMDAR+TvpFP4HlC+pQZr87gA3yre//j+UzI/oDj+ftpwM/baRNZwDzgGfyY4k/APNsP016PDCT9Ix9Qslxaypl+n8XKE0WrNWXSLM8it2Vt1fyHeC4/Dc5OtcPaeXCkXnQ5RakWQlX5e+gG3AocEH+7qcCu+XjjgN+nQcQNrogUgghhNYVCYShU4gEwhBCqJ8igTCEEEIItVilljCWdDorj4ofY/vccvu3BUl3kccUFPmB7bHt0Z4QQgihVLM6A4qo4Upljgd65mlxhygFJV1ke1gdZWxF+m63Aj4gDSw8yfYbFfYfRo4RLvPZn4GjbL9TY91nkXIb5uVNf7V9Wq1tr1eub4HtiyrtE3HEET0cQmg9Te4M5ICgiBqu7KOSPtPYWgOVSOpBmlL4Pdv35G17k6Zilu0MNMb2Z+s9hvSdVrw4hxBCWHU0Z8xARA037kLSjIEVSOoh6Yb8vUzJF/lSRwETCx2BfF4P2C605eH83T6lFAlcsJ6kuyQ9I+kqSavlOudI+kg+dpak3yhFHY+TtFaV8yhu+/Dc5umSrlcObiqUn18PyXdGCjHJ1+e/7UuSvlNU1umSnpP0v6RwonL1RQJhCCG0geZ0BiJquHETgcVlLvbfArDdnzTf/8Z8J6BYY9/tm8C++bs9gnRnpGBn4PukKY5bAF8oc3xf4Ne2twfeIa2aWM4puYM0VdL+uY2jgCNy21cnTausZhtg/9y2H0vqLmkwaRrjDrmNO5U7MBIIQwihbbTWbIKIGk5+ysp3B3Ynx+/afhZ4hTQuoFbdgd9Imk465+Ko4sdtv2R7CSmuuNx3+7Ltqfl1Y9/txUXJgWNJnbiXi8aD3AjsWUN777W92GnZ5TdJ/zvYA7jL9vt5fYa7aygnhBBCK2nOAMKZpECZcoqjhj9Qirxt1ahhUkDP9fli31jU8CF5IOP4CsVVjBrOv2g/SwomGmf77Crtul/SOcCnijar0v5FZlJ5OeNTSOMGBuZ2LiqusrQJZY5fXPR6CVDrY4LG2l38nZXe5Sitr/C3rCvgIhIIQwih9TTnzsD9pDS8ZbfwJe0kaS9qiBrOr1skalgp5rYwu6EQNVwak1tP1PCOubzSqOH3bf8WuKiwTw3OBf676P1D5PjdPGNgU1Jsb7FbgN0kLbv65fPsn8/j9bzA0NGsmJS4s6TN81iBI2jmd1viWaBBednkXHdhoaE5LF+DotJjh2IPAYcoLXG8LmkdiBBCCO2kyZ2BiBpuNGp4mTxlcV7RpiuAbvk2/23AsbYXlxyzkLT2wUmSXpD0DKkD82Y+/hhJj5EeL7xXdOhE0roFM4CXWTlyuMlsLyJFCI/JbV8KFAaP/gS4RNLDpF//1cp6inTuU0nf98Mt1c4QQgj1izji0ClEHHEIIdRPEUccQgghhFpUHUAoaQlpJH3BwbbnNKdSSSeQnr/fJGkUKZXw9irHlEYNr0a6Jb1efj+BlNBX94T0PC++N2n1vDVJI+mvqeG4uqKG1UgCY/58GBVSBNtCHuj5Lstv9Z9o+9FmlnkQsJ3t81VD0mAlnSGBMBICQwidVS2zCRbaHtSSlRYHFdVxzLmkwXgASLodmGH7rPz+J8C1rLw2Qa1G2J4saQNgtqRROYegsTYd0tjnkrrlaX6tTtLqtj9sgaL2ztMAW4Ttu4mpgyGE0KE16TFBpRQ8ScMkPSjp95Kel3S+pBGSHs+pdVvk/VZI+cvbhudf2oX3+0q6s0L9W5JGr59TtPls0qDFLXI7xislHD4raXRR6FE1PUmD8pbkuo7MbZ8h6YKiNlyZ0/Fm5o5IYfscSWdKegQ4TNJgpdTCieTAoXrl8p7IbbimcC75HH8m6UHgu/ncH8v7ni1pQVEZI/P2acXtraHusmmS+X8Dz0q6NrdrtKRPS5qQBz3unPc7VtLlJWVuIempovd9JVUKWQohhNDKaukMrKXlSXSFi3VjKXgDge+SRt8fDWxle2fSr/aTGqnnfmBbSRvl98cBN1TYdztgavGv7vx6KsvDhHYATs77fhIYWuU8R+cZDs8B59heojSd8AJgH9IaBjtJOjjvf3oelDEA2EvSgKKyFtne3fbv8jl8x/auNN3ltnfKjxfWIs00KFjf9l62fwFcAlxieyfSrA4AJO1HSh7cOZ/HYEmVAoMeyH/rSYVzoXKa5Ja5zgGkpMGjSEFHpwI/qnQytmcD85XWsoD0tx5Vup8ijjiEENpELZ2BhUVJdIXb4o2l4D1h+/U8XW42MC5vn07jSYImJfN9WdL6wK5ApUV+RPnQmuLtj9uem+fjT22s7mxEjkjI/kLGAAAgAElEQVTeFDhV0makmNzxtuflW/CjWZ66d3j+dTuF1AEp/g5uA5DUi3SxLszHv7lKGyrZW9Kk/H3vw4rpicURyruS/h6QsgoK9sv/TQGeIl24+1aqK/+td8nvG0uTfNn29PwdzwTuy3/HRv/W2bXAcZK6kTqUt5TuEHHEIYTQNpqaQNhYCl7xnPmlRe+X1lDfDcA9ubwxjTwDnwnsIGm1fCFCKWhnICnHoA+Vk+8aZXtevsjvQopFXomkzUm/fney/X9KgyCLk/cKc/8rdVpqprQmwBXAENv/yIPwytXVaDHAebavbkITGkuTbM7f+g7gx6Q7Qk/afruxnSOBMIQQWk9TpxY2loLXZDlW+DVSnv+oRvZ7kfQrtzj3/wzgqfxZkymtorgD6a7GJNIjgI/kX7BHklL31iNdhOcrraj4mQrtfCfvU1gjYEQTmlS48L4lqSeVI6ABHmN5AuCXiraPBb6aj0fSxpI+WmP9jaVJNlkOMRpLCp2q9DgohBBCG2jqnYErgDskHUZK8Kvl12mtRgMb2X6myn5fAy6T9CLpl+/EvK3J9UoqTC0cZftJAEk/JJ2jgD/b/mPePoV0h+Il0rTGSo4jrZnwPuniV81wSXOL3h8G/IZ0630O8EQjx54M/FbS94F7gfkAtsdJ2haYmB/3LwC+TBr7Uc1o4B6lNMmprJgm2VyjSasWjqu2YwghhNbT4RII88jzKbava++2dDb5rsZC25b0JeBI259v73ZVkmeU9LL9P9X2jQTCEEKon2pMIGzOqoUtLk8vew/4fnu3pZMaDFyeR/u/A3y1ndtTUZ6ZsgVpQGQIIYR21KE6A7YHl27LU9zWLNl8tO3ppftWozoTA1uLpP1JUxYLugGbkB4DfJw04LGwuNH7tndTSi/czfYtuYxhlKQV2n6YNIiyKW0y8FvbR+f3qwOvA5PqTUTMs0GOsn1FpX2qBTaFEEJoOx2qM1BO0RS3liirQ1yAcuejUmTxWZSP7G0gzeNfaQpeC3kP6Cdprbxq4r4sX/K5XusDJ5LGltQk381QYXZIqY4YRxzxwyGEVUUsVNTBFaUIng/skQOBTinZZx1J1+eEwSlFKYHbK6U/Ts3Jg5WyBQr+AhSucEcCtxbVsbOkR3P5j0raupE6zge2yNsuzPutlICYUwxnSbqClH+wSXO+qxBCCE0TnYHO4zTg4RwIdHHJZ6cD9+fkwb2BCyWtA5xASiQcBAwB5tK43wFfytkGA0hTKwueBfa0vQNwJvCzvL1cHacBs3NbR1ZJQNwauMn2DrZfKW5MJBCGEELb6PCPCUJN9gMO0vL1HnqQkhQnAqdL6gPcafuFxgqxPS2PTTgS+HPJx72AG/Mvf5NSKClXh1ZeBqI4ARHS+g99gb8Dr9h+rEJ7rgGuAVizd9+ONe0lhBBWIXFnYNUg4ItFsdGb2p6VBxseRFqaeaykWkbu3w1cRNEjguwc4IG8PsLnyGFINdZRSEAstG/LoqmjLZlREUIIoQnizkDn8S6wboXPxgInSTopZwzsYHuKpE8CL9m+NL8eQIr/bcz1wHzb0/OMhYJeLB9QeGxhY4U6ni5p61jgHEmjbS+QtDHwQS0nXRBxxCGE0HrizkDnMQ34UGk55FNKPjuHdNt+mqQZLF/a+QhghqSppMWJbqpWSV7c6ZIyH/0cOE/SBFaMn16pjrzOwASlpY0vtD2ONAtiYl5s6XYqd2xCCCG0sQ6XQBhCOZFAGEII9as1gTDuDIQQQghdXIwZ6EIkbQjcV+aj4dWWEA4hhLDqqqkzIGkJadW8goNtz2lOxZJOIEXt3iRpFPAn27fXWUYv4DJgaN40ATjJdt2T0iWNB3qTRsWvCVycp7a1qDx17095VH65z9cmrVI4gDQK/x3gANsLyu1fR73DSPHFgyQdCwyx/e2SfY4FLmT5QMFptr/SnHpzuX8mxRO/I2mB7Z71ltEeCYSRMBhC6CpqvTOwMIfKtBjbV7VAMdcBMwoXrJxsdy1p2d+mGGF7sqQNgNmSRtn+T3MaKKmb7SV1HPJd4A3b/fPxW1PnyPtmuq20k9Bctj/bkuWFEEJoWU0eM5CjZB+W9FT+b7e8fZikByX9XtLzks6XNCJH1k6XtEXe76yikJxCmcPzYkKF9/tKurNC/VuSVuk7p2jz2cAQSVvkdoyXdLukZyWNVpk0nAp6kua/L8l1HZnbPkPSsgWGJF2ZE/JmFiJ28/Y5ks6U9AhwmKTBeRbAROBbVeruTdGaALafs704f9/PSro2t2O0pE9LmiDpBUk757rLxgY3h6Rv5CjhpyXdke9eIGlU/g4ekPSSpL2UYpFn5bs9xd/HR0rKvFk5Njm/Hy3poJJ9IoEwhBDaQK2dgbWUcuanFl2s3wT2tb0jaXrZpUX7DyT9wu0PHA1sZXtn0q/2kxqp535gW0kb5ffHATdU2Hc7YGrxr+78eiqwfd60A3By3veTLH+cUMloSdOA54BzbC+R9AnSCoP7kKJ0d5J0cN7/9DxKcwCwl6QBRWUtsr277d/lc/iO7V2r1A9pnv8PJE2U9FOtuJ7AlsAlub5tSAsX7Q6cCvwo71MpNrhWRxT9rY/L2+60vZPtgcAs4GtF+/8/0ndzCnAPcDHp++8vqbG7SdeS/r6Fxz27UZJ6aPsa20NsD+m2dq86TyOEEEKtau0MLCxKjyus/Ncd+E2eNz6GdMEteML267YXA7OBcXn7dNLqe2U5zXO8Gfiy0jK4u5IWzylHpFjcxrY/nufNLyV1EirWnY2wPYAU5XuqpM2AnYDxtufZ/hAYDRRy9Q+X9BQpZnd7VvwOboNlF7r1bT+Yt9/cWANsTyV1XC4ENgCekLRt/vhl29Pz+cwE7svfWfH32gsYk/MGChfmetxW9LcudMT65btA04ERJWXeU9SGN0ra10AF+fvYUtJHSfHHd+TvN4QQQhtrzmyCU4A3SHcBVgMWFX22uOj10qL3S2uo8wbSL8xFwJhGLhAzgR0krVZY9lbSark9s4A+Je1YUkPdANiely/yuwBlxwxI2pz0i3wn2/+Xb4v3KNqlELNbqdPSWP0LgDuBOyUtBT4L3EFt32shNviQPFhxfD11VzCKNGj06TzIcFjRZ8VtKG1fte/7ZlLn4kvAVxvbMRIIQwih9TQnZ6AX8Hq+EB/Niql0TWb7NeA14AzSRajSfi+SfpGfUbT5DOCp/FmT5WfiO5DuakwiPQL4iKRupF+xDwLrkS748yV9DPhMhXa+k/fZPW8aUaXuoZL+X369BuluwyuNHVOibGxwM60LvC6pO1XaX6dRpMc42J7ZguWGEEKoQ3PuDFwB3CHpMOABWnbBmdHARrafqbLf14DLJL1I+gU+kRWfZ9ddr6TC1MJRtp8EkPRD0jkK+LPtP+btU0h3KF4iTWus5Djgeknvk3L6G7MFcGUe7LgacC/prsBmNZ7Dz0mrC36P6usQ1Op/SJ2iV0iPA1okStj2G5JmAX9oifJCCCE0TYeMI5Z0OTClaGW7sArKd2CmAztWy4aIOOIQQqifOmscsaQnSaPlf9vebQmtR9KnSTMfLmtKSFQIIYSW0yHvDJSSNIl0677Y0banl9u/Sll3AZuXbP6B7Wq371uUpP1JUxaLvVw0W6Nc8uPvbJ9fY/nDSImDBzajjeNzGXX/JK9Wfx5ncR2wCWlmypzGwonW7N3XvY/5Vb3NaLJIHwwhrApqvTPQKdYmsL1LC5Z1SPW9Wl/ufFTrgLR48mOt8mDJ1nQ28LfCcsklGQ0hhBDaUId7TBCqy4l+P8vBRJMl7ShprKTZSms+FKwn6S5Jz0i6Kk+9rDk5sWj7apJulPTT/H6/XPdTksZI6pm3H6CUkvgI8IUqp9EbmFt4Y3tas7+YEEIITRKdgY6tOPlxqqQjij77R040fJg0Re9Q4FOkX9wFOwPfJyVBbsHyC3StyYmQ7h6NBp63fUaOFT4D+HROn5wMfE9SD9ICS58D9gA+XuXcfg1cl6OMT89JjyuIOOIQQmgbneIxQRfW2GOCu/O/04Gett8F3pW0KKc3QkpgfAlA0q2k6OLbScmJx5P+/r1JWQaFX+a3ldRzNfB72+fm95/K+09Isx9ZgzSlcxvSmIcXcn2/BY6vdGK2x0r6JHAAKaNhiqR+tucV7XMNcA2kMQOVygohhNA80RnovGpJ/iu9gLqO5MSCR4G9Jf3C9iJS1sLfbB9ZvFNeh6DepMV/AbcAt0j6Eynm+Y5y+0YCYQghtJ54TLBq21nS5nmswBHAI9SYnFjkOtICQmMkrQ48BgxVWjUSSWtL2oo0TXBz5VUpSUmNFUnaR8tXP1yX9Bjj7005yRBCCM0TdwY6trUkTS16/1fbp9Vx/ETgfNKYgYeAu2wvrSM5EQDbv8wLLhXWEjgWuFVSYbrnGbafz48e7pX0Fqnj0a+RYgcDl0v6kNQpvdb2E3WcWwghhBbSKXIGQogEwhBCqF+nTSAMIYQQQtuKzkAHJWlJybTCmh8PSBqWB+Q1p/7xkqr2JqvVL+m4kvOYKunXkj6Tpw3OytkEFzWnvSGEEJouxgx0XKtE+qDtG4AbSsrvB/wR+C/bz+aBiRWnIQJMf3U+Dafd21LNqihiiEMIXVHcGehkVpH0wf8GzrX9LIDtD21f0SJfUAghhLpFZ6DjWpXTB/sBT1b7AiKBMIQQ2kY8Jui4Vtn0wVpFAmEIIbSNuDPQOTU3fXC47QHAvdSWPljYp5A+OCj/t53tr1WorzEzSTkDIYQQOoC4M7Dq2jlf/F8hpQ9eQ/n0wfGNlHEdKSJ4jKRDSOmDv5a0pe0Xc4JgH4rSB23Ppkr6IHAhcKekR3JY0WrAybZ/WemAiCMOIYTWE52BjmuVTR+0PU3SybmctUl3FVp/qkAIIYSyIoEwdAqRQBhCCPWLBMIQQggh1CQeE4RWI+k44LslmyfY/lZ7tCeEEEJ58Zigg5C0IXBffvtxYAkwL79/3/ZukhqA3Wzfko8ZBpxq+8AWakMf4Nek6YOrAX8CRtr+j6RBwCds/znvexawwHabxAiv2buvex/zqxYvNxIHQwirsnhM0MnYfrswZQ+4Cri4aArfbnm3BuCo1qhfKTjgTuAPtvsCWwE9gULGwCDgsy1YX4tFHocQQmie6Ax0ApIW5JfnA3vkRMJTSvZZR9L1kp6QNEXS5/P27SU9no+ZJqlvhWr2ISUQ3gBgewlwCvBVSeuR0g2PKElD3C4vaPSSpO8UteXLRXVeXbjwS1og6WxJk4BdJZ2f45KnxUJFIYTQfqIz0LmcBjyc7xZcXPLZ6cD9tncC9gYulLQOcAJwSb7jMASYW6Hs7SmJCLb9b+DvpDsSZwK35boLSYXbAPuToo9/LKm7pG1JuQZDc51LSFMSAdYBZtjeBXgGOATYPgcg/bS0QRFHHEIIbSMGEK469gMOknRqft8D2JSUN3B6Hg9wZyEyuAxRPkWw0naAe20vBhZLehP4GDCclC74RI4sXgt4M++/BLgjv/43sAi4VtK9pPEJK4g44hBCaBvRGVh1CPii7edKts/Kt+X/Cxgr6eu27y9z/EzgiysUmB4PbALMpnx8cHEU8hLS/54E3Gj7h2X2X5QfP2D7Q0k7kzoPXwK+TXpUUVYkEIYQQuuJxwSdy7vAuhU+GwuclAcCImmH/O8ngZdsX0pa4GhAhePvA9aW9JV8XDfgF8Ao2+9Xqbu0nEMlfTSXs4GkzUp3yksf98qzE04mDVAMIYTQDqIz0LlMAz6U9HTpAELgHKA7ME3SjPwe0vP7GTnaeBvgpnIFO80xPQQ4TNILwPOk2/g/yrs8QBowWLqccmk5z5CWOR4naRrwN9LqiKXWBf6U93mQNFgxhBBCO4icgdApRBxxCCHUL3IGQgghhFCTDjmAUNISYHrRpoNtz2lmmSeQkvxukjQK+JPt2+ssoxdwGTA0b5oAnGS77nlvksaTbp8vBNYkhQxdU285NdTTQDrXfvl9cdIhpAF/mwD/BJYC7wAH2F4g6dGiwKN66z0WGGL7201v/XLTX51Pw2nNX9gwEgdDCGFlHbIzACzMc9RbjO2rWqCY60jz5AuD7H4CXAsc1sTyRtieLGkDYLakUbb/05wGSupWGLFfju23KRqsJ+mHwEa2v5ffbw18kPdtUkcghBBC59JpHhNIapD0sKSn8n+75e3DJD0o6feSns+pdiNyAt50SVvk/c4qmoNfKHO4pLuK3u8r6c4K9W9Jml53TtHms4EhkrbI7Rgv6XZJz0oaXRjZX4OewHuk6XlIOjK3fYakC4racGUO4ZmZOyKF7XMknSnpEdIAwMF5kOFEoNqiQL2BVwtvbD+XswOWJR82dm6SPpu3PSLpUkkr5QVI2kjSHUrpiE9IGpq375UHJE5VSk2sZbZCCCGEFtZR7wyslUe/A7xs+xBScM2+thcpRereSkrUAxgIbAv8C3gJuNb2zpK+C5xEmrpWzv3AryVtZHsecBxwQ4V9twOmFv/qtr0kt3N7UojODvn1a6RHCEOBRxo5z9GSFgN9gZNzeZ8ALiB1PP6PNCr/YNt/AE63/a887e8+SQNsT8tlLbK9O0AeoX+S7QclXdhI/QDX5zoOJT0+uLFCMNFK5yZpMnA1sKftlyXdWqGOS0iPQR6RtClpGuS2wKnAt2xPyFMNFxUfJOl44HiAbuttVOU0QgghNFVHvTOwsGiRnkPytu7AbyRNB8aQLs4FT9h+Pf+inQ2My9unk6J0y8rT6W4GvixpfWBX4C8Vdq8loe9x23NtLwWmNlZ3NiJH8W4KnJrn4+8EjLc9z/aHwGhgz7z/4ZKeAqaQLszF38FtsGxcw/q2H8zbb26sAbanAp8ELgQ2ICUHbltm13Lntg0pw+DlvE+lzsCngctzx+luYL18F2AC8EuldQ3Wz+db3LZrbA+xPaTb2r0aO40QQgjN0FHvDJRzCvAG6S7Aaqz4K7I4CW9p0fulVD/HG4B7cnljSi9IRWYCO0haLV8QkbRabs8soA/lE/mqsj0vX+R3AcqOGZC0OemX9E62/y8PguxRtMt7hV2pHB9cqf4FpBUL75S0lLQ64ayS3SqlDdZiNWBX2wtLtp+vFEX8WeAxSZ+2/Ww9bQ8hhNB8nakz0AuYa3uppGOAFlkC1/Zrkl4jBeXs28h+L0qakvc7O28+A3gqf9anqW2QtDbpNvzPSbfhL5H0EdJjgiNJMxjWI13w50v6GPAZYHyZdr4jab6k3W0/wvJFgirVPRR4Jncw1iDdbVip3AqeBT4pqSHP9qgURjSOFDd8Ya5zkO2pkrawPR2YLmlX0p2Gsp2BiCMOoWv44IMPmDt3LosWLaq+c1imR48e9OnTh+7duzfp+M7UGbgCuEPSYaQ0vPeq7F+P0aQR9c9U2e9rwGWSXiT9Kp6YtzW5XkmFqYWjbD8Jy0b4P5Dr+LPtP+btU0h3KF4i3WKv5Djgeknvk57PN2YL4Mo8IHA14F6WLybUKNsLJZ0I/FXSW8DjFXb9DmlsxjTS/+YeIq2meLKkvUl3Gp6h8iOaEEIXMXfuXNZdd10aGhqofQx212abt99+m7lz57L55ps3qYxIIAQkXQ5MsX1de7els5HUM2cSCPg18EKZ5ZWbLRIIQ+gaZs2axTbbbBMdgTrZ5tlnn2XbbVcc8qVIIKyNpCdJi/f8tr3b0kl9Iw8MnEl6lHN1O7cnhNDJRUegfs39zjrTY4JWYXulpXmVlvxds2Tz0fn5dl1yjkHpfZsf2K52+75FSdqfNGWxWGHaZpPluwAtficghBDaS7du3ejfv/+y93/4wx9oaGioq4x33nmHW265hRNPPLGFW9c64jFBB6SV45h/Z/v8Go8dBpxq+8Bm1D8+l1H3ffla6pd0MGkQ5hrAh8BZ1aKh1+zd172P+VW9zVlBRBGH0PHNmjVrhVvdLRFDXqyW/x/o2bMnCxYsaF49c+Zw4IEHMmPGjLqOW7JkCd26NW18fOl3B/GYoLMrzlkYVGtHoCXkQKPWLH8gcBHwedvbAJ8DLpC00h2aEELoKJYsWcLIkSPZaaedGDBgAFdfnZ6ILliwgOHDh7PjjjvSv39//vjHPwJw2mmnMXv2bAYNGsTIkSMZP348Bx64/DfSt7/9bUaNGgVAQ0MDZ599Nrvvvjtjxoxh9uzZHHDAAQwePJg99tiDZ59Nk6zGjBlDv379GDhwIHvuuSctqcs/JuhMJM0BbgH2JoUwHQ+cB2wJXFi0/sJ6+fHE1qSR+yfmKZlXkkKN1gJut/3jonKvB/YDLi+qbzVSDsM/bJ8haT/gJ6RHKLOB4/LgwQOAXwFvAU9VOY1TgZ8VgopycuHPgO8DR5WcbyQQhhDa3MKFCxk0KC3hsvnmm3PXXXdx3XXX0atXL5544gkWL17M0KFD2W+//dhkk0246667WG+99Xjrrbf41Kc+xUEHHcT555/PjBkzmDo1hemOHz++0Tp79OjBI4+kwNrhw4dz1VVX0bdvXyZNmsSJJ57I/fffz9lnn83YsWPZeOONeeedd1r0nKMz0DEVxzEDnGf7tvz6H7Z3lXQxMIoUedyDNICv0BnYmZQX8ArwV+ALwO3UHmd8Aul/G6NJCzOdm3MPzgA+bfs9ST8Avifp58BvgH2AF8lJiI3YnnRnoNhkUmz0CvIqjtdAekxQpdwQQmgRa6211rKLeMG4ceOYNm0at9+enmjOnz+fF154gT59+vCjH/2Ihx56iNVWW41XX32VN954o+46jzgixbQsWLCARx99lMMOW77+3eLFKfNt6NChHHvssRx++OF84QtfaOrplRWdgY6psVUb787/Tgd62n4XeFfSohypDCk6+CWAvF7A7qTOwOH51/bqpAWKtgMKnYHSi/jVwO9tn5vffyrvPyGPWl2DlLOwDWkg4gu5vt+Sf81XUC4hMYYOhxA6NNtcdtll7L///itsHzVqFPPmzePJJ5+ke/fuNDQ0lA1MWn311Vm6dOmy96X7rLPOOgAsXbqU9ddff6XOCMBVV13FpEmTuPfeexk0aBBTp05lww03bInTi85AJ1QctVwaw1z4e5ZebF1HnHHBo8Dekn5hexHpgv0320cW7yRpUJn6GjOTtMDUtKJtO5LuDlQUCYQhhPa0//77c+WVV7LPPvvQvXt3nn/+eTbeeGPmz5/PRz/6Ubp3784DDzzAK6+8AsC6667Lu+++u+z4zTbbjGeeeYbFixezaNEi7rvvPnbfffeV6llvvfXYfPPNGTNmDIcddhi2mTZtGgMHDmT27Nnssssu7LLLLtxzzz384x//aLHOQAwgXDXtLGnz/Mz/CNLKieXijBtzHfBnYIyk1YHHSCsVbgkpQlnSVqT44M2Vl4omxSc35iLgh5IacjkNpFUlq62uGEII7ebrX/862223HTvuuCP9+vXjm9/8Jh9++CEjRoxg8uTJDBkyhNGjR7PNNtsAsOGGGzJ06FD69evHyJEj2WSTTTj88MMZMGAAI0aMYIcddqhY1+jRo7nuuusYOHAg22+//bJBiSNHjqR///7069ePPffck4EDB7bY+cXUwg6ozNTCv9o+LQ/0G2L7LUnH5tffzsfMIf3i7gecCcwD+rPiAMJRpMWQXiLdVbjb9qjicnNZ48lTCyX9BNiKtMbBMFJWQSGD4Qzbd5cMIHwE6FdlauEXWD4QsQHY23Zj8cqRQBhCF1FuelyoTXOmFkZnILQrSeeTOij72y67YiNEZyCEriI6A03XnM5AjBkI7cr2ae3dhhBC6OpizMAqSNKGkqbm//4p6dWi94/mfRokHVV0zDBJf2rBNljSv4rqnSppYVPrmP7q/JZqWgghhBJxZ2AVZPttYBCApLOABbZL5/Y3kEJ+bmmlZrxHyjnYLS91/BlSQFIIITTKdixWVKfmPvKPOwNdjKRC4Pb5wB75F/spJfusI+l6SU9ImiLp83n79pIez8dMk9S3SnV/AQrzAY8Ebi2qY6+iOwZTJK3bMmcYQujMevTowdtvv93si1tXYpu3336bHj16VN+5grgz0HWdRtGCQnmBoYLTgfttfzUHGT0u6X+BE4BLbI+WtAZQbR2D3wFn5kcDA0iRx3vkz04FvmV7gqSewEopHRFHHELX06dPH+bOncu8efPauymdSo8ePejTp0+Tj4/OQChnP+AgSafm9z2ATUmJg6dL6gPcWUgdrMT2tJwjcCQps6DYBOCXkkbnsuaWOT7iiEPoYrp3787mm5eu+h5aWzwmCOUI+GLRqomb2p5l+xbgIGAhMFbSPjWUdTcpaOjW4o15JcavkxZNekzSNo0V0n/jXk05jxBCCDWIzkDX9S5Q6Tn9WOAk5RE8knbI/34SeMn2paSL/IAa6rkeONt2cYgSkrawPd32BaQo4kY7AyGEEFpPdAa6rmnAh5KeLh1ACJxDWiJ5mqQZ+T2kaOMZeUXFbYCbqlVie67tS8p8dLKkGZKeJt1p+EtTTySEEELzRAJh6BQkvQs8197taIKPkGKaO5PO2GbonO2ONredztjulmjzZrarjsCOAYShs3iulkjNjkbS5M7W7s7YZuic7Y42t53O2O62bHN0BkKTSdoQuK/MR8Nz8FEIIYROIDoDocmKkw5DCCF0XjGAMHQW17R3A5qoM7a7M7YZOme7o81tpzO2u83aHAMIQwghhC4u7gyEEEIIXVx0BkK7k3SApOckvSjptDKfrynptvz5pBxxXPjsh3n7c5L27+ht/v/bO78Qq4o4jn++ZSoa5hYUmckq6IOFWaEV9AeK7M9DBgmJhVaP4kMPWZg+ZA9RYlTgQ6/aS6EhCElmBRFSFGaubX9sNVEzkDIKFfzXt4eZ4HR3V3f33Hvukfv7wHDnnjNz+NzDzL0zc+fMSLpf0k5Je/LrUFZxbLt34fwUSccLS1XX2lnSLElfSOrN93zkO7lU5C3pMknrs+8PklbUyPluSd9IOitpQcO5JZJ+zmFJ3Z0lzS6UjR5Jj9j2pCsAAAPnSURBVFflXMa7cH6C0vb065oiZDtChLYF0mZH+4BpwGhgNzCzIc1S4O0cXwi8l+Mzc/oxwNR8nUtr7nwzMCnHbwR+vRjudeH8+8BG0iZXtXYmTZDuAW7K76+qonw0wXsR8G6OjwMOAN01ce4mrTy6AVhQOH4lsD+/duV4V82dZwDTc3wS8BswsUblY0Dvwvm3SFvQr2uGU4wMBO1mLtBne7/t06SdDuc3pJkPrM/xTcB9eank+aQvzVO2fwH68vVq62x7l+0j+XgvMFbSmAqcody9RtKjpC/53op8oZzzPKDH9m5IT7/YPncReBsYL2kUae+O08DfdXC2fcB2D/BPQ94HgO22j9n+E9gOPFhnZ9t7nTdby3XyKFDV9qhl7jWSbgWuAT5qllA0BoJ2cx1wqPD+cD42YBrbZ4G/SL28oeRtBWWcizwG7LJ9qkWejYzYW9J44AVgdQWeA/pkhnOvZwCWtC0Ptz5fgW8/p8xwvDcBJ0g91YPAWtvHWi1MufpU57p4QSTNJfXQ9zXJ60KM2FvSJcDrwPJmCsU6A0G70QDHGh9xGSzNUPK2gjLO6aR0A/AaqfdaFWW8VwNv2D6eBwqqoozzKOBOYA5wEvhE0k7bAy2U1WzKeM8FzpGGrruAzyV9bHt/cxX7UaY+1bkunv8C0rXAO8AS2/164S2ijPdSYKvtQ82sizEyELSbw8D1hfeTgSODpclDp1cAx4aYtxWUcUbSZGAzsNh2VT2R/zllhuN9G7BG0gHgWeBFSctaLUz58vGZ7d9tnwS2Are03LjBKTMc70XAh7bP2D4K7ACqWJK2TH2qc10cFEkTgA+AVba/bLLb+SjjfQewLNfFtcBiSa+WFYrGQNBuvgamS5oqaTRpItWWhjRbgP9mJy8APnWaQbMFWJhnZU8FpgNf1dlZ0kTSl88K2zsqcC0yYm/bd9nutt0NvAm8Yrs5s5hb5EzainuWpHH5x/Ye4PsKnMt6HwTuVWI8cDvwY02cB2MbME9Sl6Qu0ojXthZ5Fhmxc06/Gdhge2MLHQdixN62n7A9JdfF50j+/Z5GGDZVzJyMEOF8AXgY2Ev6v25lPvYy8EiOjyXNYO8j/dhPK+RdmfP9BDxUd2dgFen/4G8L4eq6ezdc4yUqepqgCeXjSdKEx++ANRdDuQYuz8d7SY2X5TVynkPq1Z4A/gB6C3mfyZ+lD3i67s65bJxpqIuz6+7dcI2naNLTBLECYRAEQRB0OPE3QRAEQRB0ONEYCIIgCIIOJxoDQRAEQdDhRGMgCIIgCDqcaAwEQRAEQYcTjYEgCIIg6HCiMRAEQRAEHU40BoIgCIKgw/kX3enYwyx/dVMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(rf.feature_importances_, index = dummytest.columns, columns = [\"Features\"]).sort_values(by=\"Features\").plot(kind = \"barh\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Titles_Mr', 'Sex_male', 'Fare_Per_Person', 'Sex_female', 'Fare', 'Age',\n",
       "       'Pclass', 'Family', 'Titles_Mrs', 'Titles_Miss',\n",
       "       'Cabin_Status_Cabin Allotted', 'Cabin_Status_No Cabin Found',\n",
       "       'Family_On_Board_Large Family', 'Titles_Master',\n",
       "       'Family_On_Board_Small Family', 'Embarked_S', 'Embarked_C',\n",
       "       'Titles_Others', 'Family_On_Board_Singles', 'Embarked_Q', 'Titles_Ms'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 722,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(rf.feature_importances_, index = dummytest.columns, columns = [\"Features\"]).sort_values(by = \"Features\", ascending = False).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdata = scaled_train.loc[:, ['Titles_Mr', 'Sex_male', 'Fare_Per_Person', 'Sex_female', 'Fare', 'Age',\n",
    "       'Pclass', 'Family', 'Titles_Mrs', 'Titles_Miss']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtest = scaled_test.loc[:, ['Titles_Mr', 'Sex_male', 'Fare_Per_Person', 'Sex_female', 'Fare', 'Age',\n",
    "       'Pclass', 'Family', 'Titles_Mrs', 'Titles_Miss']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred1 = rf.fit(newdata, dummytrain.Survived).predict(newtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": pred1})\n",
    "solution.to_csv(\"RF1 Model.csv\", index = False) # 0.77990"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GBM Model\n",
    "pred_gbm = gbm.fit(newdata, dummytrain.Survived).predict(newtest)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": pred_gbm})\n",
    "solution.to_csv(\"GBM Selective Feature Model.csv\", index = False) # 0.77033"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "pd.DataFrame(gbm.feature_importances_, index = newtest.columns, columns = [\"Features\"]).sort_values(by=\"Features\").plot(kind = \"barh\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "bag = BaggingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_bag = bag.fit(newdata, dummytrain.Survived).predict(newtest)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": pred_bag})\n",
    "solution.to_csv(\"BAGG Selective Feature Model.csv\", index = False) # 0.73684"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999837883\n"
     ]
    }
   ],
   "source": [
    "# H0: There exists no relaitonship between the Survived and PClass\n",
    "chi_sq_Stat, p_value, deg_freedom, exp_freq = stats.chi2_contingency([dummytrain.Pclass, dummytrain.Survived])\n",
    "print(p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(dummytrain.drop(\"Survived\", axis = 1),\n",
    "                                            dummytrain.Survived, test_size = 0.20, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "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": [
    "xtrain_scaled = pd.DataFrame(sc.fit_transform(xtrain), columns = xtrain.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "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": [
    "xtest_scaled = pd.DataFrame(sc.transform(xtest), columns = xtest.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(criterion = 'gini', \n",
    "                            max_depth=6, \n",
    "                            max_features='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=6, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = rf.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, cohen_kappa_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7932960893854749"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(ytest, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(ytest, pred), annot = True, cmap = \"YlGnBu\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.ylabel(\"Actual Values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About Confusion Matrix\n",
    "\n",
    "1. A False Positive (type I error) — when you reject a true null hypothesis. For e.g. Null Hypothesis is that In Titanic, Jack Did Not Survive whereas the we assume that Jack Survived. This is False Positive. Another Example is that we know that Superheros don't exist in real world but we assume that they exist while watching movies like Spiderman, Superman etc. In Simple words, Assuming something when it is actually not.\n",
    "\n",
    "\n",
    "2. A False Negative (type II error) — when you accept a false null hypothesis - Someone is telling a Lie and You Accept the Lie. It means you really did some mischief and you say that you did not.\n",
    "\n",
    "\n",
    "3. Precision - Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. The question that this metric answer is of all passengers that labeled as survived, how many actually survived? High precision relates to the low false positive rate. We have got 0.788 precision which is pretty good.\n",
    "\n",
    "###### Precision = TP/TP+FP\n",
    "\n",
    "\n",
    "4. Recall (Sensitivity) - Recall is the ratio of correctly predicted positive observations to the all observations in actual class - yes. The question recall answers is: Of all the passengers that truly survived, how many did we label? We have got recall of 0.631 which is good for this model as it’s above 0.5.\n",
    "\n",
    "###### Recall = TP/TP+FN\n",
    "\n",
    "5. F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. Intuitively it is not as easy to understand as accuracy, but F1 is usually more useful than accuracy, especially if you have an uneven class distribution. Accuracy works best if false positives and false negatives have similar cost. If the cost of false positives and false negatives are very different, it’s better to look at both Precision and Recall. In our case, F1 score is 0.701.\n",
    "\n",
    "###### F1 Score = 2*(Recall * Precision) / (Recall + Precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Precision of 0 is 0.78\n",
      "The Precision of 1 is 0.83\n"
     ]
    }
   ],
   "source": [
    "# precision is TP/(TP+FP)\n",
    "print(\"The Precision of 0 is {}\".format(round(97/(97+28),2))) # Precision of 0\n",
    "\n",
    "# Precision of 1\n",
    "print(\"The Precision of 1 is {}\".format(round(45/(45+9),2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Recall of 0 is 0.92\n",
      "The Recall of 1 is 0.62\n",
      "0.725\n"
     ]
    }
   ],
   "source": [
    "print(\"The Recall of 0 is\", round(97/(97+9),2))\n",
    "print(\"The Recall of 1 is\", round(45/(45+28),2))\n",
    "\n",
    "# F1 Score = 2*(Recall * Precision) / (Recall + Precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.92      0.84       106\n",
      "           1       0.83      0.62      0.71        73\n",
      "\n",
      "   micro avg       0.79      0.79      0.79       179\n",
      "   macro avg       0.80      0.77      0.77       179\n",
      "weighted avg       0.80      0.79      0.79       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5539766987675938"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohen_kappa_score(ytest, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using K Fold Cross Validation Set\n",
    "\n",
    "from sklearn.model_selection import KFold \n",
    "kf = RepeatedKFold(n_splits=5, n_repeats=10, random_state=None) \n",
    "\n",
    "\n",
    "for train_index, test_index in kf.split(X):\n",
    "      print(\"Train:\", train_index, \"Validation:\",test_index)\n",
    "      X_train, X_test = X[train_index], X[test_index] \n",
    "      y_train, y_test = y[train_index], y[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 750,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Int64Index([  0,   3,   4,   5,   6,   7,   8,   9,  10,  11,\\n            ...\\n            880, 881, 882, 883, 884, 885, 886, 887, 888, 889],\\n           dtype='int64', length=712)] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-750-791e21204451>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtrain_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_index\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscaled_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m#print(\"Train:\", train_index, \"Validation:\",test_index)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaled_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaled_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mytr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mytr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2932\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2933\u001b[0m             indexer = self.loc._convert_to_indexer(key, axis=1,\n\u001b[0;32m-> 2934\u001b[0;31m                                                    raise_missing=True)\n\u001b[0m\u001b[1;32m   2935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2936\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter, raise_missing)\u001b[0m\n\u001b[1;32m   1352\u001b[0m                 kwargs = {'raise_missing': True if is_setter else\n\u001b[1;32m   1353\u001b[0m                           raise_missing}\n\u001b[0;32m-> 1354\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1355\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1356\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1159\u001b[0m         self._validate_read_indexer(keyarr, indexer,\n\u001b[1;32m   1160\u001b[0m                                     \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1161\u001b[0;31m                                     raise_missing=raise_missing)\n\u001b[0m\u001b[1;32m   1162\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1244\u001b[0m                 raise KeyError(\n\u001b[1;32m   1245\u001b[0m                     u\"None of [{key}] are in the [{axis}]\".format(\n\u001b[0;32m-> 1246\u001b[0;31m                         key=key, axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m   1247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1248\u001b[0m             \u001b[0;31m# We (temporarily) allow for some missing keys with .loc, except in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of [Int64Index([  0,   3,   4,   5,   6,   7,   8,   9,  10,  11,\\n            ...\\n            880, 881, 882, 883, 884, 885, 886, 887, 888, 889],\\n           dtype='int64', length=712)] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RepeatedKFold \n",
    "kf = RepeatedKFold(n_splits=5, n_repeats=10, random_state=None) \n",
    "\n",
    "for train_index, test_index in kf.split(scaled_train):\n",
    "    #print(\"Train:\", train_index, \"Validation:\",test_index)\n",
    "    X_train, X_test = scaled_train[train_index], scaled_train[test_index] \n",
    "    y_train, y_test = ytr[train_index], ytr[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, cross_val_score, cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8258888888888889"
      ]
     },
     "execution_count": 759,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rf = RandomForestClassifier(criterion='gini', max_depth=8, max_features='auto', n_estimators=100, oob_score=False)\n",
    "#np.mean(cross_val_score(rf, scaled_train, ytr, cv = 100)) # cv = 50 - 0.8317483660130719, 100 - 0.83552\n",
    "np.mean(cross_val_score(gbm, scaled_train, ytr, cv = 100)) # cv: 100 - 0.82588"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune GBM and Try on Whole Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"loss\":[\"deviance\", \"exponential\"],\n",
    "         \"learning_rate\":[0.1, 0.5, 0.9, 1],\n",
    "         \"n_estimators\":[100, 200, 300, 500],\n",
    "         \"max_depth\":[2,4,5,6,7,8],\n",
    "         \"max_features\":[\"auto\", \"sqrt\", \"log2\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(estimator=gbm, param_grid=params, cv = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.1, 'loss': 'exponential', 'max_depth': 2, 'max_features': 'auto', 'n_estimators': 300}\n"
     ]
    }
   ],
   "source": [
    "grid.fit(scaled_train, ytr)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 765,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm = GradientBoostingClassifier(learning_rate=0.1, loss='exponential', \n",
    "                                 max_depth=2, max_features='auto', n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 766,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_gbmfit = gbm.fit(scaled_train,ytr).predict(scaled_test)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": model_gbmfit})\n",
    "solution.to_csv(\"GBM Tuned.csv\", index = False) # 0.7655 on 300 Trees, 0.77990 for 100 Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Voting Classifier on Tuned RF and GBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 802,
   "metadata": {},
   "outputs": [],
   "source": [
    "vc = VotingClassifier(estimators=[(\"RF\", rf),(\"GBM\", gbm), (\"xgb\", model)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 804,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_vcfit = vc.fit(scaled_train,ytr).predict(scaled_test)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": model_vcfit})\n",
    "solution.to_csv(\"VC Tuned.csv\", index = False) # 0.79425"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adaboost - Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 770,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding the Parameters of Adaboost\n",
    "boostparam={\n",
    "    \"base_estimator\":[rf],\n",
    "    \"n_estimators\":[50,75,100],\n",
    "    \"learning_rate\":[1,2,3,4,5],\n",
    "    \"algorithm\":[\"SAMME\", \"SAMME.R\"],\n",
    "    \"random_state\" : [123]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 772,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ada = AdaBoostClassifier()\n",
    "grid = GridSearchCV(estimator = ada, param_grid=boostparam, cv = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 798,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost.sklearn import XGBClassifier\n",
    "model = XGBClassifier(learning_rate=0.001,n_estimators=2500,\n",
    "                                max_depth=5, min_child_weight=0,\n",
    "                                gamma=0, subsample=0.7,\n",
    "                                colsample_bytree=0.7,\n",
    "                                scale_pos_weight=1, seed=27,\n",
    "                                reg_alpha=0.00006)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 799,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_XGBfit = model.fit(scaled_train,ytr).predict(scaled_test)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": model_XGBfit})\n",
    "solution.to_csv(\"XGB Tuned.csv\", index = False) #0.79904"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 810,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8387996618765848"
      ]
     },
     "execution_count": 810,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cross_val_score(model, scaled_train, ytr, cv = 65))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_XGBfit = model.predict(scaled_test)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": model_XGBfit})\n",
    "solution.to_csv(\"XGB Tuned.csv\", index = False) #0.79904"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 813,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"learning_rate\":[0.1, 0.01, 0.001],\"n_estimators\":[1000, 500, 2000, 2500, 2800, 3000],\n",
    "                                \"max_depth\":[3,4,5,6], \"min_child_weight\":[0,1]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 814,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(estimator=model, param_grid=params, cv = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 815,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "       estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.7, gamma=0, learning_rate=0.001,\n",
       "       max_delta_step=0, max_depth=5, min_child_weight=0, missing=None,\n",
       "       n_estimators=2500, n_jobs=1, nthread=None,\n",
       "       objective='binary:logistic', random_state=0, reg_alpha=6e-05,\n",
       "       reg_lambda=1, scale_pos_weight=1, seed=27, silent=True,\n",
       "       subsample=0.7),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'learning_rate': [0.1, 0.01, 0.001], 'n_estimators': [1000, 500, 2000, 2500, 2800, 3000], 'max_depth': [3, 4, 5, 6], 'min_child_weight': [0, 1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 815,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(scaled_train, ytr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 816,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.001,\n",
       " 'max_depth': 6,\n",
       " 'min_child_weight': 0,\n",
       " 'n_estimators': 1000}"
      ]
     },
     "execution_count": 816,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 844,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBClassifier(learning_rate=0.001,max_depth=8,n_estimators=1000, \n",
    "                    min_child_weight=0,gamma=0, subsample=0.7,colsample_bytree=0.7,scale_pos_weight=1, seed=2019,\n",
    "                                reg_alpha=0.00006)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 845,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.841756327318125"
      ]
     },
     "execution_count": 845,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cross_val_score(xgb, scaled_train, ytr, cv = 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 846,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_XGBfit = xgb.fit(scaled_train,ytr).predict(scaled_test)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": model_XGBfit})\n",
    "solution.to_csv(\"XGB Tuned.csv\", index = False) #0.80382 when n estimators is 1000 and depth is 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Considering Multicollinearity in cat variables.\n",
    "\n",
    "We will drop first cat col to deter from Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "newtrain = newtrain.drop(\"titles\" , axis = 1)\n",
    "newtest = newtest.drop(\"titles\" , axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pclass', 'Sex', 'Age', 'Fare', 'Embarked', 'Cabin_Status', 'Titles',\n",
       "       'Family', 'Family_On_Board', 'Fare_Per_Person'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newtest.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummytrain = pd.get_dummies(newtrain,drop_first = True)\n",
    "dummytest = pd.get_dummies(newtest, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 17)\n",
      "(418, 16)\n"
     ]
    }
   ],
   "source": [
    "print(dummytrain.shape)\n",
    "print(dummytest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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",
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:9: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "xtr = dummytrain.drop(\"Survived\", axis =1)\n",
    "ytr = dummytrain.Survived\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "\n",
    "scaled_train = pd.DataFrame(sc.fit_transform(xtr), columns = xtr.columns)\n",
    "\n",
    "scaled_test = pd.DataFrame(sc.transform(dummytest), columns = dummytest.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost.sklearn import XGBClassifier\n",
    "xgb = XGBClassifier(learning_rate=0.001,max_depth=8,n_estimators=1000, \n",
    "                    min_child_weight=0,gamma=0, subsample=0.7,colsample_bytree=0.7,scale_pos_weight=1, seed=2019,\n",
    "                                reg_alpha=0.00006)\n",
    "\n",
    "model_XGBfit = xgb.fit(scaled_train,ytr).predict(scaled_test)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": model_XGBfit})\n",
    "solution.to_csv(\"XGB Tuned.csv\", index = False) #0.79425"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Variance Inflation Factor\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"]= [variance_inflation_factor(xtr.values, i) for i in range(xtr.shape[1])]\n",
    "vif[\"features\"] = xtr.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>VIF Factor</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48.842873</td>\n",
       "      <td>Sex_male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>37.314450</td>\n",
       "      <td>Family_On_Board_Singles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.894882</td>\n",
       "      <td>Pclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19.435436</td>\n",
       "      <td>Titles_Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16.329017</td>\n",
       "      <td>Titles_Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.416153</td>\n",
       "      <td>Fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.562370</td>\n",
       "      <td>Fare_Per_Person</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.205314</td>\n",
       "      <td>Titles_Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.297883</td>\n",
       "      <td>Age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.622599</td>\n",
       "      <td>Cabin_Status_No Cabin Found</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.073908</td>\n",
       "      <td>Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.423225</td>\n",
       "      <td>Embarked_S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.991834</td>\n",
       "      <td>Family_On_Board_Small Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.968425</td>\n",
       "      <td>Titles_Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.677289</td>\n",
       "      <td>Embarked_Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.093642</td>\n",
       "      <td>Titles_Ms</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    VIF Factor                      features\n",
       "5    48.842873                      Sex_male\n",
       "14   37.314450       Family_On_Board_Singles\n",
       "0    24.894882                        Pclass\n",
       "10   19.435436                     Titles_Mr\n",
       "9    16.329017                   Titles_Miss\n",
       "2    14.416153                          Fare\n",
       "4    12.562370               Fare_Per_Person\n",
       "11   12.205314                    Titles_Mrs\n",
       "1    10.297883                           Age\n",
       "8     9.622599   Cabin_Status_No Cabin Found\n",
       "3     9.073908                        Family\n",
       "7     5.423225                    Embarked_S\n",
       "15    3.991834  Family_On_Board_Small Family\n",
       "13    1.968425                 Titles_Others\n",
       "6     1.677289                    Embarked_Q\n",
       "12    1.093642                     Titles_Ms"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif.sort_values(by=\"VIF Factor\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "newxtr = xtr.drop([\"Sex_male\", \"Family_On_Board_Singles\", \"Pclass\", \"Titles_Mr\",\n",
    "                  \"Titles_Miss\", \"Fare\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdummytest = dummytest.drop([\"Sex_male\", \"Family_On_Board_Singles\", \"Pclass\", \"Titles_Mr\",\n",
    "                  \"Titles_Miss\", \"Fare\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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",
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc1 = StandardScaler()\n",
    "\n",
    "scaled_train = pd.DataFrame(sc1.fit_transform(newxtr), columns = newxtr.columns)\n",
    "\n",
    "scaled_test = pd.DataFrame(sc1.transform(newdummytest), columns = newdummytest.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "       estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'learning_rate': [0.1, 0.01, 0.001], 'n_estimators': [1000, 500, 2000, 2500], 'max_depth': [3, 4, 5, 6], 'min_child_weight': [0, 1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "params = {\"learning_rate\":[0.1, 0.01, 0.001],\n",
    "          \"n_estimators\":[1000, 500, 2000, 2500],\n",
    "          \"max_depth\":[3,4,5,6], \n",
    "          \"min_child_weight\":[0,1]}\n",
    "xgb = XGBClassifier()\n",
    "\n",
    "grid = GridSearchCV(estimator=xgb, param_grid=params, cv = 10)\n",
    "grid.fit(scaled_train, ytr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.01,\n",
       " 'max_depth': 6,\n",
       " 'min_child_weight': 1,\n",
       " 'n_estimators': 500}"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBClassifier(learning_rate=0.01,max_depth=6,n_estimators=500, \n",
    "                    min_child_weight=1)\n",
    "\n",
    "model_XGBfit = xgb.fit(scaled_train,ytr).predict(scaled_test)\n",
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": model_XGBfit})\n",
    "solution.to_csv(\"XGB Tuned.csv\", index = False) #0.68899"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "1. Multicollinearity treatment in the case of titanic did not work well. Lost the accuracy.\n",
    "2. Need to check the variables if they are contributing significantly to the model else we will use vital information from the data and hence model will be a worse model like we see above.\n",
    "\n",
    "3. Multicollinearity in categorical variables is a reality but we need to see if the variables are contributing to the model. If yes, then we need not remove the variable per se.\n",
    "\n",
    "\n",
    "### Recursive Feature Elimination\n",
    "\n",
    "1. Recursive - Doing same thing several times.\n",
    "\n",
    "2. Feature - Set of Columns\n",
    "\n",
    "3. Cross-Validation — a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. Use cross-validation to detect overfitting, ie, failing to generalize a pattern.\n",
    "\n",
    "\n",
    "##### How RFE Works\n",
    "\n",
    "1. As previously noted, recursive feature elimination (RFE, Guyon et al. (2002)) is basically a backward selection of the predictors. This technique begins by building a model on the entire set of predictors and computing an importance score(grid score) for each predictor. \n",
    "\n",
    "2. The least important predictor(s) are then removed, the model is re-built, and importance scores are computed again. In practice, the analyst specifies the number of predictor subsets to evaluate as well as each subset’s size. \n",
    "\n",
    "3. Therefore, the subset size is a tuning parameter for RFE. The subset size that optimizes the performance criteria is used to select the predictors based on the importance rankings. The optimal subset is then used to train the final model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding Correlated Features\n",
    "\n",
    "correlated_features = set()\n",
    "correlation_matrix = dummytrain.drop(\"Survived\", axis =1).corr()\n",
    "\n",
    "for i in range(len(correlation_matrix.columns)):\n",
    "    for j in range(i):\n",
    "        if abs(correlation_matrix.iloc[i, j]) > 0.8:\n",
    "            colname = correlation_matrix.columns[i]\n",
    "            correlated_features.add(colname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Fare_Per_Person', 'Titles_Mr'}"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlated_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Recursive Feature Elimination\n",
    "\n",
    "1. You will need to declare two variables — X and target where first represents all the features, and the second represents the target variable. \n",
    "\n",
    "2. Then you’ll make an instance of the Machine learning algorithm (I’m using RandomForests). In it, you can optionally pass a random state seed for reproducibility. Now you can create an instance of RFECV, here are needed arguments:\n",
    "\n",
    "   * estimator — your model instance\n",
    "\n",
    "   * step — number of features to remove at each iteration\n",
    "\n",
    "   * cv — your Cross-Validation, use StratifiedKFold and set K to 10\n",
    "\n",
    "   * scoring — scoring metric, what you want to optimize for. I’ve used ‘accuracy’ but you can select any other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFECV(cv=StratifiedKFold(n_splits=10, random_state=None, shuffle=False),\n",
       "   estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1),\n",
       "   min_features_to_select=1, n_jobs=None, scoring='accuracy', step=1,\n",
       "   verbose=0)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Applying RFE\n",
    "\n",
    "X = dummytrain.drop(['Survived', 'Fare_Per_Person', 'Titles_Mr'], axis=1)\n",
    "target = dummytrain['Survived']\n",
    "\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "rf = RandomForestClassifier(random_state=101)\n",
    "rfecv = RFECV(estimator=xgb, step=1, cv=StratifiedKFold(10), scoring='accuracy')\n",
    "rfecv.fit(X, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal No of Features 5\n"
     ]
    }
   ],
   "source": [
    "print(\"Optimal No of Features {}\".format(rfecv.n_features_))\n",
    "# These are the Ideal Features for the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. rfecv.grid_scores_ - These are the cross validation scores for every feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Accuracy with every feature\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title('Recursive Feature Elimination with Cross-Validation', fontsize=18, \n",
    "          fontweight='bold', pad=20)\n",
    "plt.xlabel('Number of features selected', fontsize=14, labelpad = 10)\n",
    "plt.ylabel('% Correct Classification', fontsize=14, labelpad = 10)\n",
    "plt.plot(range(1, len(rfecv.grid_scores_)+1), \n",
    "         rfecv.grid_scores_, color='red', linewidth=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print out which features are considered to be least important and drop them with this snippet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5  6  7  8  9 10 11 12 13]\n"
     ]
    }
   ],
   "source": [
    "print(np.where(rfecv.support_ == False)[0])\n",
    "\n",
    "X.drop(X.columns[np.where(rfecv.support_ == False)[0]], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family</th>\n",
       "      <th>Sex_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age     Fare  Family  Sex_male\n",
       "0       3  22.0   7.2500       1         1\n",
       "1       1  38.0  71.2833       1         0\n",
       "2       3  26.0   7.9250       1         0\n",
       "3       1  35.0  53.1000       1         0\n",
       "4       3  35.0   8.0500       1         1"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pclass', 'Age', 'Fare', 'Family', 'Sex_male'], dtype='object')"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc2 = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_x = sc2.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_test = sc2.transform(dummytest.loc[:, X.columns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost.sklearn import XGBClassifier\n",
    "xgb = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = xgb.fit(scaled_x, target).predict(scaled_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"PassengerId\":test.PassengerId, \"Survived\": pred})\n",
    "solution.to_csv(\"XGB POST RFE.csv\", index = False) #0.77033"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.67036914, 0.71393769, 0.73079191, 0.77101691, 0.83953382,\n",
       "       0.8250017 , 0.83057003, 0.8339411 , 0.83510243, 0.8339533 ,\n",
       "       0.83397855, 0.83285495, 0.83285495, 0.83285495])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfecv.grid_scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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