Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.max_columns = 100\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)\n",
    "import seaborn as sns\n",
    "import pylab as plot\n",
    "params = { \n",
    "    'axes.labelsize': \"large\",\n",
    "    'xtick.labelsize': 'x-large',\n",
    "    'legend.fontsize': 20,\n",
    "    'figure.dpi': 150,\n",
    "    'figure.figsize': [25, 7]\n",
    "}\n",
    "plot.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\".../train.csv\")\n",
    "check = pd.read_csv(\".../test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape\n",
    "#train - P_ID <= 891"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check.shape\n",
    "#test > P_ID > 891"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.append(check)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>16.0</td>\n",
       "      <td>D28</td>\n",
       "      <td>S</td>\n",
       "      <td>39.4000</td>\n",
       "      <td>Lines, Miss. Mary Conover</td>\n",
       "      <td>1</td>\n",
       "      <td>854</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>Andersson, Master. Sigvard Harald Elias</td>\n",
       "      <td>2</td>\n",
       "      <td>851</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Ford, Mr. Arthur</td>\n",
       "      <td>0</td>\n",
       "      <td>1181</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A/5 1478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>25.4667</td>\n",
       "      <td>Lefebre, Master. Henry Forbes</td>\n",
       "      <td>1</td>\n",
       "      <td>177</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>Phillips, Mr. Escott Robert</td>\n",
       "      <td>1</td>\n",
       "      <td>1194</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S.O./P.P. 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>36.7500</td>\n",
       "      <td>Nicholls, Mr. Joseph Charles</td>\n",
       "      <td>1</td>\n",
       "      <td>146</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C.A. 33112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>0</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>Cor, Mr. Bartol</td>\n",
       "      <td>0</td>\n",
       "      <td>948</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>Sinkkonen, Miss. Anna</td>\n",
       "      <td>0</td>\n",
       "      <td>748</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7333</td>\n",
       "      <td>Charters, Mr. David</td>\n",
       "      <td>0</td>\n",
       "      <td>422</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5. 13032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked     Fare                                     Name  \\\n",
       "853  16.0   D28        S  39.4000                Lines, Miss. Mary Conover   \n",
       "850   4.0   NaN        S  31.2750  Andersson, Master. Sigvard Harald Elias   \n",
       "289   NaN   NaN        S   8.0500                         Ford, Mr. Arthur   \n",
       "176   NaN   NaN        S  25.4667            Lefebre, Master. Henry Forbes   \n",
       "302  43.0   NaN        S  21.0000              Phillips, Mr. Escott Robert   \n",
       "145  19.0   NaN        S  36.7500             Nicholls, Mr. Joseph Charles   \n",
       "887  19.0   B42        S  30.0000             Graham, Miss. Margaret Edith   \n",
       "56   35.0   NaN        S   7.8958                          Cor, Mr. Bartol   \n",
       "747  30.0   NaN        S  13.0000                    Sinkkonen, Miss. Anna   \n",
       "421  21.0   NaN        Q   7.7333                      Charters, Mr. David   \n",
       "\n",
       "     Parch  PassengerId  Pclass     Sex  SibSp  Survived       Ticket  \n",
       "853      1          854       1  female      0       1.0     PC 17592  \n",
       "850      2          851       3    male      4       0.0       347082  \n",
       "289      0         1181       3    male      0       NaN     A/5 1478  \n",
       "176      1          177       3    male      3       0.0         4133  \n",
       "302      1         1194       2    male      0       NaN  S.O./P.P. 2  \n",
       "145      1          146       2    male      1       0.0   C.A. 33112  \n",
       "887      0          888       1  female      0       1.0       112053  \n",
       "56       0          948       3    male      0       NaN       349230  \n",
       "747      0          748       2  female      0       1.0       250648  \n",
       "421      0          422       3    male      0       0.0   A/5. 13032  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age             263\n",
       "Cabin          1014\n",
       "Embarked          2\n",
       "Fare              1\n",
       "Name              0\n",
       "Parch             0\n",
       "PassengerId       0\n",
       "Pclass            0\n",
       "Sex               0\n",
       "SibSp             0\n",
       "Survived        418\n",
       "Ticket            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age            float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "Fare           float64\n",
       "Name            object\n",
       "Parch            int64\n",
       "PassengerId      int64\n",
       "Pclass           int64\n",
       "Sex             object\n",
       "SibSp            int64\n",
       "Survived       float64\n",
       "Ticket          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.881138</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.413493</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.486592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         Fare        Parch  PassengerId       Pclass  \\\n",
       "count  1046.000000  1308.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean     29.881138    33.295479     0.385027   655.000000     2.294882   \n",
       "std      14.413493    51.758668     0.865560   378.020061     0.837836   \n",
       "min       0.170000     0.000000     0.000000     1.000000     1.000000   \n",
       "25%      21.000000     7.895800     0.000000   328.000000     2.000000   \n",
       "50%      28.000000    14.454200     0.000000   655.000000     3.000000   \n",
       "75%      39.000000    31.275000     0.000000   982.000000     3.000000   \n",
       "max      80.000000   512.329200     9.000000  1309.000000     3.000000   \n",
       "\n",
       "             SibSp    Survived  \n",
       "count  1309.000000  891.000000  \n",
       "mean      0.498854    0.383838  \n",
       "std       1.041658    0.486592  \n",
       "min       0.000000    0.000000  \n",
       "25%       0.000000    0.000000  \n",
       "50%       0.000000    0.000000  \n",
       "75%       1.000000    1.000000  \n",
       "max       8.000000    1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>37.0</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>Beckwith, Mr. Richard Leonard</td>\n",
       "      <td>1</td>\n",
       "      <td>249</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>47.0</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>1</td>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked     Fare  \\\n",
       "248  37.0   D35        S  52.5542   \n",
       "871  47.0   D35        S  52.5542   \n",
       "\n",
       "                                                 Name  Parch  PassengerId  \\\n",
       "248                     Beckwith, Mr. Richard Leonard      1          249   \n",
       "871  Beckwith, Mrs. Richard Leonard (Sallie Monypeny)      1          872   \n",
       "\n",
       "     Pclass     Sex  SibSp  Survived Ticket  \n",
       "248       1    male      1       1.0  11751  \n",
       "871       1  female      1       1.0  11751  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data.loc((data[\"Ticket\"] == \"CA. 2343\"), [\"Name\", \"Parch\", \"SibSp\"] )\n",
    "data.loc[(data[\"Ticket\"]==\"11751\"),]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPLORATION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Dead']= 1-data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x164f248f0f0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x164f23f1128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Male Vs Female Survival chances\n",
    "data.groupby('Sex').agg('mean')[['Survived', 'Dead']].plot(kind ='bar', stacked = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x164f44fc208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fare wrt sex\n",
    "sns.violinplot(x='Sex', y='Fare', hue='Survived', data=data, split=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x164f44d8940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Age with Sex\n",
    "sns.violinplot(x='Sex', y='Age', hue='Survived', data=data, split=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Dead</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>Salutation</th>\n",
       "      <th>Deck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2631</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Emir</td>\n",
       "      <td>Mr</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>740</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349218</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Nankoff</td>\n",
       "      <td>Mr</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>16.0</td>\n",
       "      <td>S</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>3</td>\n",
       "      <td>87</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>W./C. 6608</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Ford</td>\n",
       "      <td>Mr</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>30.0</td>\n",
       "      <td>S</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>419</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28228</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Matthews</td>\n",
       "      <td>Mr</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>32.0</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>430</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>SOTON/O.Q. 392078</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pickard</td>\n",
       "      <td>Mr</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>1196</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>383123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>McCarthy</td>\n",
       "      <td>Miss</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>20.0</td>\n",
       "      <td>S</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>0</td>\n",
       "      <td>990</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Braf</td>\n",
       "      <td>Miss</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45.0</td>\n",
       "      <td>S</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>0</td>\n",
       "      <td>936</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kimball</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>774</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2674</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Elias</td>\n",
       "      <td>Mr</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>63.0</td>\n",
       "      <td>S</td>\n",
       "      <td>221.7792</td>\n",
       "      <td>0</td>\n",
       "      <td>1006</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Straus</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Embarked      Fare  Parch  PassengerId  Pclass     Sex  SibSp  \\\n",
       "26    NaN        C    7.2250      0           27       3    male      0   \n",
       "739   NaN        S    7.8958      0          740       3    male      0   \n",
       "86   16.0        S   34.3750      3           87       3    male      1   \n",
       "418  30.0        S   13.0000      0          419       2    male      0   \n",
       "429  32.0        S    8.0500      0          430       3    male      0   \n",
       "304   NaN        Q    7.7500      0         1196       3  female      0   \n",
       "98   20.0        S    7.8542      0          990       3  female      0   \n",
       "44   45.0        S   52.5542      0          936       1  female      1   \n",
       "773   NaN        C    7.2250      0          774       3    male      0   \n",
       "114  63.0        S  221.7792      0         1006       1  female      1   \n",
       "\n",
       "     Survived             Ticket  Dead Last_Name Salutation Deck  \n",
       "26        0.0               2631   1.0      Emir         Mr  NaN  \n",
       "739       0.0             349218   1.0   Nankoff         Mr  NaN  \n",
       "86        0.0         W./C. 6608   1.0      Ford         Mr  NaN  \n",
       "418       0.0              28228   1.0  Matthews         Mr  NaN  \n",
       "429       1.0  SOTON/O.Q. 392078   0.0   Pickard         Mr    E  \n",
       "304       NaN             383123   NaN  McCarthy       Miss  NaN  \n",
       "98        NaN             347471   NaN      Braf       Miss  NaN  \n",
       "44        NaN              11753   NaN   Kimball        Mrs    D  \n",
       "773       0.0               2674   1.0     Elias         Mr  NaN  \n",
       "114       NaN           PC 17483   NaN    Straus        Mrs    C  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#splitting Cabin and Last names \n",
    "data[\"Last_Name\"], data[\"First_Name\"] = data[\"Name\"].str.split(\",\",1).str\n",
    "data[\"Salutation\"], data[\"Rest\"] = data[\"First_Name\"].str.split(\". \", 1).str\n",
    "data[\"Deck\"] = data[\"Cabin\"].str[:1]\n",
    "data['Salutation'] = data['Salutation'].str.strip()\n",
    "data2 = data.drop(columns=['First_Name', 'Rest', 'Name', 'Cabin'])\n",
    "data2.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#m = {\"Miss\":  ['Ms ', 'Mlle ','Mme', 'Dona', 'Lady'] , \"Officer\": ['Capt', 'Col','Major', 'Dr', 'Rev','Don','Sir','Jonkheer','the Countess']}\n",
    "#m2 = {v: k for k,vv in m.items() for v in vv}\n",
    "#m2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr         757\n",
       "Miss       266\n",
       "Mrs        197\n",
       "Master      61\n",
       "Officer     23\n",
       "Royalty      5\n",
       "Name: Salutation, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2['Salutation'] = data2['Salutation'].map(lambda x: 'Miss' if x in ['Ms', 'Mlle','Mme', 'Dona'] else x)\n",
    "data2['Salutation'] = data2['Salutation'].map(lambda x: 'Officer' if x in ['Capt', 'Col','Major', 'Dr', 'Rev'] else x)\n",
    "data2['Salutation'] = data2['Salutation'].map(lambda x: 'Royalty' if x in ['Don','Sir','Jonkheer','th','Lady'] else x)\n",
    "data2['Salutation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(data2.groupby('Deck')[['Survived']].mean()) \n",
    "#print(data2.groupby('Deck')[['Survived']].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(data2.groupby('Salutation')[['Survived']].mean())\n",
    "#print(data2.groupby('Salutation')[['Survived']].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2['Family'] = data2['SibSp'] + data2['Parch']+1\n",
    "data2.head()\n",
    "data2['Single'] = data2['Family'].map(lambda x: 1 if x == 1 else 0)\n",
    "data2['Small_Family'] = data2['Family'].map(lambda x: 1 if 2 <= x <= 4 else 0)\n",
    "data2['Big_Family'] = data2['Family'].map(lambda x: 1 if x > 4 else 0)\n",
    "data2['Big_Family'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Dead</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>Salutation</th>\n",
       "      <th>Deck</th>\n",
       "      <th>Family</th>\n",
       "      <th>Single</th>\n",
       "      <th>Small_Family</th>\n",
       "      <th>Big_Family</th>\n",
       "      <th>Ticket_pref</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A5 21171</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Braund</td>\n",
       "      <td>Mr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cumings</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STONO2 3101282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>Miss</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STONO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Futrelle</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>XXX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Mr</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>XXX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Embarked     Fare  Parch  PassengerId  Pclass     Sex  SibSp  \\\n",
       "0  22.0        S   7.2500      0            1       3    male      1   \n",
       "1  38.0        C  71.2833      0            2       1  female      1   \n",
       "2  26.0        S   7.9250      0            3       3  female      0   \n",
       "3  35.0        S  53.1000      0            4       1  female      1   \n",
       "4  35.0        S   8.0500      0            5       3    male      0   \n",
       "\n",
       "   Survived          Ticket  Dead  Last_Name Salutation Deck  Family  Single  \\\n",
       "0       0.0        A5 21171   1.0     Braund         Mr  NaN       2       0   \n",
       "1       1.0        PC 17599   0.0    Cumings        Mrs    C       2       0   \n",
       "2       1.0  STONO2 3101282   0.0  Heikkinen       Miss  NaN       1       1   \n",
       "3       1.0          113803   0.0   Futrelle        Mrs    C       2       0   \n",
       "4       0.0          373450   1.0      Allen         Mr  NaN       1       1   \n",
       "\n",
       "   Small_Family  Big_Family Ticket_pref  \n",
       "0             1           0          A5  \n",
       "1             1           0          PC  \n",
       "2             0           0      STONO2  \n",
       "3             1           0         XXX  \n",
       "4             0           0         XXX  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2['Ticket'] = data2['Ticket'].str.replace('/','')\n",
    "data2['Ticket'] = data2['Ticket'].str.replace('.','')\n",
    "data2['Ticket_pref'] = data2['Ticket'].str.split(\" \",1).str[0]\n",
    "data2['Ck'] = data2['Ticket'].str.isnumeric()\n",
    "\n",
    "data2['Ticket_pref'] = np.where(data2['Ck'] ==True, 'XXX', data2['Ticket_pref'])\n",
    "data2 = data2.drop(columns='Ck')\n",
    "data2.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMPUTATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age              263\n",
       "Embarked           2\n",
       "Fare               1\n",
       "Parch              0\n",
       "PassengerId        0\n",
       "Pclass             0\n",
       "Sex                0\n",
       "SibSp              0\n",
       "Survived         418\n",
       "Ticket             0\n",
       "Dead             418\n",
       "Last_Name          0\n",
       "Salutation         0\n",
       "Deck            1014\n",
       "Family             0\n",
       "Single             0\n",
       "Small_Family       0\n",
       "Big_Family         0\n",
       "Ticket_pref        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filling Missing Values\n",
    "data2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data2['Embarked'].fillna()\n",
    "data2['Embarked'].fillna(data2['Embarked'].mode()[0], inplace = True)\n",
    "data2['Deck'].fillna('U', inplace = True)\n",
    "#print(data2['Age'].mean())\n",
    "#data2['Age'].fillna(data2['Age'].median(), inplace = True)\n",
    "data2['Fare'].fillna(data2['Fare'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x164f4615c88>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x164f465b6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot(column= \"Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Salutation</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>Master</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>Mr</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>Officer</td>\n",
       "      <td>46.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>Master</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sex  Pclass Salutation   Age\n",
       "12  male       2     Master   1.0\n",
       "13  male       2         Mr  31.0\n",
       "14  male       2    Officer  46.5\n",
       "15  male       3     Master   4.0\n",
       "16  male       3         Mr  26.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Imputing Age - Separately for test and train\n",
    "grouped_train = data2.iloc[:891].groupby(['Sex','Pclass','Salutation'])\n",
    "grouped_median_train = grouped_train.median()\n",
    "grouped_median_train = grouped_median_train.reset_index()[['Sex', 'Pclass', 'Salutation', 'Age']]\n",
    "grouped_median_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_age(row):\n",
    "    condition = (\n",
    "        (grouped_median_train['Sex'] == row['Sex']) & \n",
    "        (grouped_median_train['Salutation'] == row['Salutation']) & \n",
    "        (grouped_median_train['Pclass'] == row['Pclass'])\n",
    "    ) \n",
    "    return grouped_median_train[condition]['Age'].values[0]\n",
    "\n",
    "data2['Age'] = data2.apply(lambda row: fill_age(row) if np.isnan(row['Age']) else row['Age'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x164f46f10b8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x164f4619320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2['Age'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adult        570\n",
      "Youth        378\n",
      "MiddleAge    228\n",
      "Children     123\n",
      "Seniors       10\n",
      "Name: Age_grp, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Binning Age\n",
    "bins = [0,15,25,40,65,100]\n",
    "groups = ['Children', 'Youth', 'Adult', 'MiddleAge','Seniors']\n",
    "data2['Age_grp'] = pd.cut(data2['Age'], bins, labels= groups)\n",
    "print(data2['Age_grp'].value_counts())\n",
    "data2 = data2.drop(columns= 'Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x164f46f8470>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x164f47b34e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2['Fare'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7.854, 10.5]        184\n",
      "(21.679, 39.688]     180\n",
      "(-0.001, 7.854]      179\n",
      "(39.688, 512.329]    176\n",
      "(10.5, 21.679]       172\n",
      "Name: Fare, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Bins for Fare\n",
    "tr = data2.iloc[:891,]\n",
    "\n",
    "Fare_range = pd.qcut(tr['Fare'], 5)\n",
    "print(Fare_range.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [-0.1, 7.854,10.5,21.679,39.688,512.330]\n",
    "group = [0,1,2,3,4]\n",
    "data2['Fare_grp'] = pd.cut(data2['Fare'], bins, labels= group)\n",
    "data2['Fare_grp'].value_counts()\n",
    "data2 = data2.drop(columns= 'Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "#enc = OneHotEncoder(sparse = False)\n",
    "\n",
    "\n",
    "one_hot = pd.get_dummies(data2['Embarked'],prefix='Embarked')\n",
    "p_class = pd.get_dummies(data2['Pclass'], prefix='Pclass')\n",
    "sex = pd.get_dummies(data2['Sex'], prefix = 'Sex')\n",
    "Salutation = pd.get_dummies(data2['Salutation'], prefix = 'Sal')\n",
    "#Deck = pd.get_dummies(data2['Deck'], prefix = 'Deck')\n",
    "Tick = pd.get_dummies(data2['Ticket_pref'], prefix = 'Tkt')\n",
    "Age = pd.get_dummies(data2['Age_grp'], prefix = 'Age')\n",
    "Fare = pd.get_dummies(data2['Fare_grp'], prefix = 'Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1309, 19)\n",
      "(1309, 61)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Buffer dtype mismatch, expected 'Python object' but got 'long long'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;31mValueError\u001b[0m: Buffer dtype mismatch, expected 'Python object' but got 'long long'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.lib.is_bool_array'\n",
      "ValueError: Buffer dtype mismatch, expected 'Python object' but got 'long long'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1309, 81)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb = [one_hot, p_class, sex, Salutation,Tick,Age,Fare]\n",
    "data3 = pd.concat(comb, axis = 1)\n",
    "#data3.sample(10)\n",
    "print(data2.shape)\n",
    "print(data3.shape)\n",
    "data2['SNO'] = 1\n",
    "data3['SNO'] = 1\n",
    "data2['SNO'] = data2['SNO'].cumsum()\n",
    "data3['SNO'] = data3['SNO'].cumsum()\n",
    "data4 = data2.merge(data3, left_on='SNO', right_on='SNO', how='inner')\n",
    "data4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Embarked           object\n",
       "Parch               int64\n",
       "PassengerId         int64\n",
       "Pclass              int64\n",
       "Sex                object\n",
       "SibSp               int64\n",
       "Survived          float64\n",
       "Ticket             object\n",
       "Dead              float64\n",
       "Last_Name          object\n",
       "Salutation         object\n",
       "Deck               object\n",
       "Family              int64\n",
       "Single              int64\n",
       "Small_Family        int64\n",
       "Big_Family          int64\n",
       "Ticket_pref        object\n",
       "Age_grp          category\n",
       "Fare_grp         category\n",
       "SNO                 int64\n",
       "Embarked_C          uint8\n",
       "Embarked_Q          uint8\n",
       "Embarked_S          uint8\n",
       "Pclass_1            uint8\n",
       "Pclass_2            uint8\n",
       "Pclass_3            uint8\n",
       "Sex_female          uint8\n",
       "Sex_male            uint8\n",
       "Sal_Master          uint8\n",
       "Sal_Miss            uint8\n",
       "                   ...   \n",
       "Tkt_SC              uint8\n",
       "Tkt_SCA3            uint8\n",
       "Tkt_SCA4            uint8\n",
       "Tkt_SCAH            uint8\n",
       "Tkt_SCOW            uint8\n",
       "Tkt_SCPARIS         uint8\n",
       "Tkt_SCParis         uint8\n",
       "Tkt_SOC             uint8\n",
       "Tkt_SOP             uint8\n",
       "Tkt_SOPP            uint8\n",
       "Tkt_SOTONO2         uint8\n",
       "Tkt_SOTONOQ         uint8\n",
       "Tkt_SP              uint8\n",
       "Tkt_STONO           uint8\n",
       "Tkt_STONO2          uint8\n",
       "Tkt_STONOQ          uint8\n",
       "Tkt_SWPP            uint8\n",
       "Tkt_WC              uint8\n",
       "Tkt_WEP             uint8\n",
       "Tkt_XXX             uint8\n",
       "Age_Children        uint8\n",
       "Age_Youth           uint8\n",
       "Age_Adult           uint8\n",
       "Age_MiddleAge       uint8\n",
       "Age_Seniors         uint8\n",
       "Fare_0              uint8\n",
       "Fare_1              uint8\n",
       "Fare_2              uint8\n",
       "Fare_3              uint8\n",
       "Fare_4              uint8\n",
       "Length: 81, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "prep = data4.drop(columns=['Fare_grp', 'Tkt_A', 'Sal_Miss', 'Sal_Mr', 'Sal_Master', 'Fare_4', \n",
    "'Tkt_A4',\n",
    "'Tkt_AQ3',\n",
    "'Tkt_AQ4',\n",
    "'Tkt_AS',\n",
    "'Tkt_C',\n",
    "'Tkt_CASOTON',\n",
    "'Tkt_FC',\n",
    "'Tkt_FCC',\n",
    "'Tkt_Fa',\n",
    "'Tkt_LINE',\n",
    "'Tkt_LP',\n",
    "'Tkt_PP',\n",
    "'Tkt_PPP',\n",
    "'Tkt_SC',\n",
    "'Tkt_SCA3',\n",
    "'Tkt_SCA4',\n",
    "'Tkt_SCAH',\n",
    "'Tkt_SCOW',\n",
    "'Tkt_SCPARIS',\n",
    "'Tkt_SCParis',\n",
    "'Tkt_SOC',\n",
    "'Tkt_SOP',\n",
    "'Tkt_SOPP',\n",
    "'Tkt_SOTONO2',\n",
    "'Tkt_SOTONOQ',\n",
    "'Tkt_SP',\n",
    "'Tkt_STONO',\n",
    "'Tkt_STONO2',\n",
    "'Tkt_STONOQ',\n",
    "'Tkt_SWPP',\n",
    "'Tkt_WC',\n",
    "'Tkt_WEP','Age_grp','Age_Seniors','Fare_0','Embarked_S','Sex_male','Pclass_3','Sal_Officer','Tkt_XXX','Pclass','Dead','Parch','SibSp','Ticket_pref','Sex','Ticket','Embarked','Last_Name','Salutation','Deck','SNO','Family'], axis=1)\n",
    "#,'Deck_U'\n",
    "#,'Embarked_C','Pclass_3','Sex_female','Deck_A'\n",
    "#'PassengerId'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId        int64\n",
       "Survived         float64\n",
       "Single             int64\n",
       "Small_Family       int64\n",
       "Big_Family         int64\n",
       "Embarked_C         uint8\n",
       "Embarked_Q         uint8\n",
       "Pclass_1           uint8\n",
       "Pclass_2           uint8\n",
       "Sex_female         uint8\n",
       "Sal_Mrs            uint8\n",
       "Sal_Royalty        uint8\n",
       "Tkt_A5             uint8\n",
       "Tkt_CA             uint8\n",
       "Tkt_PC             uint8\n",
       "Age_Children       uint8\n",
       "Age_Youth          uint8\n",
       "Age_Adult          uint8\n",
       "Age_MiddleAge      uint8\n",
       "Fare_1             uint8\n",
       "Fare_2             uint8\n",
       "Fare_3             uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prep.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      857395.0\n",
       "Survived            342.0\n",
       "Single              790.0\n",
       "Small_Family        437.0\n",
       "Big_Family           82.0\n",
       "Embarked_C          270.0\n",
       "Embarked_Q          123.0\n",
       "Pclass_1            323.0\n",
       "Pclass_2            277.0\n",
       "Sex_female          466.0\n",
       "Sal_Mrs             197.0\n",
       "Sal_Royalty           5.0\n",
       "Tkt_A5               28.0\n",
       "Tkt_CA               68.0\n",
       "Tkt_PC               92.0\n",
       "Age_Children        123.0\n",
       "Age_Youth           378.0\n",
       "Age_Adult           570.0\n",
       "Age_MiddleAge       228.0\n",
       "Fare_1              275.0\n",
       "Fare_2              256.0\n",
       "Fare_3              260.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prep.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(418, 21)\n",
      "(891, 21)\n"
     ]
    }
   ],
   "source": [
    "#splitting new and train data\n",
    "#chk = prep.loc[isnull(prep['Survived']) == True,]\n",
    "chk = prep[pd.isnull(prep['Survived'])]\n",
    "chk = chk.drop(columns=['Survived'], axis=1)\n",
    "print(chk.shape)\n",
    "\n",
    "#datis = prep.dropna(subset = ['Survived'], how = 'all', inplace= True)\n",
    "prep = prep[prep['Survived'] >= 0]\n",
    "prep = prep.drop(columns=['PassengerId'], axis=1)\n",
    "print(prep.shape)\n",
    "#df.dropna(subset=[1])\n",
    "#df.dropna(subset=['EPS'], how='all', inplace = True)\n",
    "#df = df[df.EPS >= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Survived</th>\n",
       "      <th>Single</th>\n",
       "      <th>Small_Family</th>\n",
       "      <th>Big_Family</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sal_Mrs</th>\n",
       "      <th>Sal_Royalty</th>\n",
       "      <th>Tkt_A5</th>\n",
       "      <th>Tkt_CA</th>\n",
       "      <th>Tkt_PC</th>\n",
       "      <th>Age_Children</th>\n",
       "      <th>Age_Youth</th>\n",
       "      <th>Age_Adult</th>\n",
       "      <th>Age_MiddleAge</th>\n",
       "      <th>Fare_1</th>\n",
       "      <th>Fare_2</th>\n",
       "      <th>Fare_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.203367</td>\n",
       "      <td>0.279855</td>\n",
       "      <td>-0.125147</td>\n",
       "      <td>0.168240</td>\n",
       "      <td>0.003650</td>\n",
       "      <td>0.285904</td>\n",
       "      <td>0.093349</td>\n",
       "      <td>0.543351</td>\n",
       "      <td>0.339040</td>\n",
       "      <td>0.033391</td>\n",
       "      <td>-0.092199</td>\n",
       "      <td>-0.019137</td>\n",
       "      <td>0.147062</td>\n",
       "      <td>0.136884</td>\n",
       "      <td>-0.005687</td>\n",
       "      <td>-0.085847</td>\n",
       "      <td>0.024854</td>\n",
       "      <td>-0.198067</td>\n",
       "      <td>0.043153</td>\n",
       "      <td>0.060373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single</th>\n",
       "      <td>-0.203367</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.859931</td>\n",
       "      <td>-0.336825</td>\n",
       "      <td>-0.095298</td>\n",
       "      <td>0.086464</td>\n",
       "      <td>-0.113364</td>\n",
       "      <td>-0.039070</td>\n",
       "      <td>-0.303646</td>\n",
       "      <td>-0.365454</td>\n",
       "      <td>-0.000414</td>\n",
       "      <td>0.050549</td>\n",
       "      <td>-0.117245</td>\n",
       "      <td>-0.019783</td>\n",
       "      <td>-0.358790</td>\n",
       "      <td>0.053502</td>\n",
       "      <td>0.188261</td>\n",
       "      <td>-0.042835</td>\n",
       "      <td>0.338625</td>\n",
       "      <td>-0.145954</td>\n",
       "      <td>-0.217122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Small_Family</th>\n",
       "      <td>0.279855</td>\n",
       "      <td>-0.859931</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.190940</td>\n",
       "      <td>0.158586</td>\n",
       "      <td>-0.087093</td>\n",
       "      <td>0.168568</td>\n",
       "      <td>0.104546</td>\n",
       "      <td>0.260747</td>\n",
       "      <td>0.372029</td>\n",
       "      <td>0.011568</td>\n",
       "      <td>-0.029666</td>\n",
       "      <td>0.006430</td>\n",
       "      <td>0.041377</td>\n",
       "      <td>0.181151</td>\n",
       "      <td>-0.052569</td>\n",
       "      <td>-0.108224</td>\n",
       "      <td>0.073383</td>\n",
       "      <td>-0.279801</td>\n",
       "      <td>0.194048</td>\n",
       "      <td>0.081314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Big_Family</th>\n",
       "      <td>-0.125147</td>\n",
       "      <td>-0.336825</td>\n",
       "      <td>-0.190940</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109274</td>\n",
       "      <td>-0.005620</td>\n",
       "      <td>-0.092945</td>\n",
       "      <td>-0.117721</td>\n",
       "      <td>0.102954</td>\n",
       "      <td>0.016535</td>\n",
       "      <td>-0.020544</td>\n",
       "      <td>-0.042488</td>\n",
       "      <td>0.213620</td>\n",
       "      <td>-0.038283</td>\n",
       "      <td>0.355838</td>\n",
       "      <td>-0.005918</td>\n",
       "      <td>-0.162413</td>\n",
       "      <td>-0.052993</td>\n",
       "      <td>-0.135075</td>\n",
       "      <td>-0.077273</td>\n",
       "      <td>0.267558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_C</th>\n",
       "      <td>0.168240</td>\n",
       "      <td>-0.095298</td>\n",
       "      <td>0.158586</td>\n",
       "      <td>-0.109274</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.148258</td>\n",
       "      <td>0.296423</td>\n",
       "      <td>-0.125416</td>\n",
       "      <td>0.082853</td>\n",
       "      <td>0.061395</td>\n",
       "      <td>0.079020</td>\n",
       "      <td>-0.074892</td>\n",
       "      <td>-0.105869</td>\n",
       "      <td>0.397139</td>\n",
       "      <td>0.034760</td>\n",
       "      <td>-0.061748</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.042283</td>\n",
       "      <td>-0.201512</td>\n",
       "      <td>0.005520</td>\n",
       "      <td>-0.029439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked_Q</th>\n",
       "      <td>0.003650</td>\n",
       "      <td>0.086464</td>\n",
       "      <td>-0.087093</td>\n",
       "      <td>-0.005620</td>\n",
       "      <td>-0.148258</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155342</td>\n",
       "      <td>-0.127301</td>\n",
       "      <td>0.074115</td>\n",
       "      <td>-0.089739</td>\n",
       "      <td>-0.023105</td>\n",
       "      <td>-0.021454</td>\n",
       "      <td>-0.067548</td>\n",
       "      <td>-0.082643</td>\n",
       "      <td>-0.033890</td>\n",
       "      <td>0.089786</td>\n",
       "      <td>0.009040</td>\n",
       "      <td>-0.096346</td>\n",
       "      <td>-0.096489</td>\n",
       "      <td>-0.038317</td>\n",
       "      <td>-0.056012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_1</th>\n",
       "      <td>0.285904</td>\n",
       "      <td>-0.113364</td>\n",
       "      <td>0.168568</td>\n",
       "      <td>-0.092945</td>\n",
       "      <td>0.296423</td>\n",
       "      <td>-0.155342</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.288585</td>\n",
       "      <td>0.098013</td>\n",
       "      <td>0.088207</td>\n",
       "      <td>0.132798</td>\n",
       "      <td>-0.087887</td>\n",
       "      <td>-0.124239</td>\n",
       "      <td>0.475007</td>\n",
       "      <td>-0.133146</td>\n",
       "      <td>-0.147137</td>\n",
       "      <td>-0.031816</td>\n",
       "      <td>0.309898</td>\n",
       "      <td>-0.301390</td>\n",
       "      <td>-0.275681</td>\n",
       "      <td>0.130974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass_2</th>\n",
       "      <td>0.093349</td>\n",
       "      <td>-0.039070</td>\n",
       "      <td>0.104546</td>\n",
       "      <td>-0.117721</td>\n",
       "      <td>-0.125416</td>\n",
       "      <td>-0.127301</td>\n",
       "      <td>-0.288585</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.064746</td>\n",
       "      <td>0.121239</td>\n",
       "      <td>-0.038324</td>\n",
       "      <td>-0.079259</td>\n",
       "      <td>0.139384</td>\n",
       "      <td>-0.137080</td>\n",
       "      <td>0.009655</td>\n",
       "      <td>-0.026113</td>\n",
       "      <td>0.011441</td>\n",
       "      <td>0.006038</td>\n",
       "      <td>-0.111456</td>\n",
       "      <td>0.314612</td>\n",
       "      <td>0.183454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_female</th>\n",
       "      <td>0.543351</td>\n",
       "      <td>-0.303646</td>\n",
       "      <td>0.260747</td>\n",
       "      <td>0.102954</td>\n",
       "      <td>0.082853</td>\n",
       "      <td>0.074115</td>\n",
       "      <td>0.098013</td>\n",
       "      <td>0.064746</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.547600</td>\n",
       "      <td>0.007483</td>\n",
       "      <td>-0.083638</td>\n",
       "      <td>0.006179</td>\n",
       "      <td>0.073639</td>\n",
       "      <td>0.097669</td>\n",
       "      <td>0.129419</td>\n",
       "      <td>-0.175810</td>\n",
       "      <td>0.017444</td>\n",
       "      <td>-0.166584</td>\n",
       "      <td>0.099850</td>\n",
       "      <td>0.036281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sal_Mrs</th>\n",
       "      <td>0.339040</td>\n",
       "      <td>-0.365454</td>\n",
       "      <td>0.372029</td>\n",
       "      <td>0.016535</td>\n",
       "      <td>0.061395</td>\n",
       "      <td>-0.089739</td>\n",
       "      <td>0.088207</td>\n",
       "      <td>0.121239</td>\n",
       "      <td>0.547600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.030347</td>\n",
       "      <td>-0.020155</td>\n",
       "      <td>0.019250</td>\n",
       "      <td>0.046197</td>\n",
       "      <td>-0.111109</td>\n",
       "      <td>-0.111044</td>\n",
       "      <td>0.026079</td>\n",
       "      <td>0.196536</td>\n",
       "      <td>-0.145139</td>\n",
       "      <td>0.114970</td>\n",
       "      <td>0.061103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sal_Royalty</th>\n",
       "      <td>0.033391</td>\n",
       "      <td>-0.000414</td>\n",
       "      <td>0.011568</td>\n",
       "      <td>-0.020544</td>\n",
       "      <td>0.079020</td>\n",
       "      <td>-0.023105</td>\n",
       "      <td>0.132798</td>\n",
       "      <td>-0.038324</td>\n",
       "      <td>0.007483</td>\n",
       "      <td>-0.030347</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011671</td>\n",
       "      <td>-0.016499</td>\n",
       "      <td>0.099717</td>\n",
       "      <td>-0.024712</td>\n",
       "      <td>-0.047306</td>\n",
       "      <td>0.024221</td>\n",
       "      <td>0.046160</td>\n",
       "      <td>-0.040024</td>\n",
       "      <td>-0.036610</td>\n",
       "      <td>0.036756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tkt_A5</th>\n",
       "      <td>-0.092199</td>\n",
       "      <td>0.050549</td>\n",
       "      <td>-0.029666</td>\n",
       "      <td>-0.042488</td>\n",
       "      <td>-0.074892</td>\n",
       "      <td>-0.021454</td>\n",
       "      <td>-0.087887</td>\n",
       "      <td>-0.079259</td>\n",
       "      <td>-0.083638</td>\n",
       "      <td>-0.020155</td>\n",
       "      <td>-0.011671</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.034122</td>\n",
       "      <td>-0.041747</td>\n",
       "      <td>-0.051107</td>\n",
       "      <td>0.049830</td>\n",
       "      <td>-0.048276</td>\n",
       "      <td>0.048138</td>\n",
       "      <td>0.113329</td>\n",
       "      <td>-0.000569</td>\n",
       "      <td>-0.078444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tkt_CA</th>\n",
       "      <td>-0.019137</td>\n",
       "      <td>-0.117245</td>\n",
       "      <td>0.006430</td>\n",
       "      <td>0.213620</td>\n",
       "      <td>-0.105869</td>\n",
       "      <td>-0.067548</td>\n",
       "      <td>-0.124239</td>\n",
       "      <td>0.139384</td>\n",
       "      <td>0.006179</td>\n",
       "      <td>0.019250</td>\n",
       "      <td>-0.016499</td>\n",
       "      <td>-0.034122</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059014</td>\n",
       "      <td>0.126263</td>\n",
       "      <td>-0.019506</td>\n",
       "      <td>-0.021995</td>\n",
       "      <td>-0.070654</td>\n",
       "      <td>-0.000840</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>-0.004378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tkt_PC</th>\n",
       "      <td>0.147062</td>\n",
       "      <td>-0.019783</td>\n",
       "      <td>0.041377</td>\n",
       "      <td>-0.038283</td>\n",
       "      <td>0.397139</td>\n",
       "      <td>-0.082643</td>\n",
       "      <td>0.475007</td>\n",
       "      <td>-0.137080</td>\n",
       "      <td>0.073639</td>\n",
       "      <td>0.046197</td>\n",
       "      <td>0.099717</td>\n",
       "      <td>-0.041747</td>\n",
       "      <td>-0.059014</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.088391</td>\n",
       "      <td>-0.040095</td>\n",
       "      <td>-0.003585</td>\n",
       "      <td>0.105424</td>\n",
       "      <td>-0.143162</td>\n",
       "      <td>-0.130950</td>\n",
       "      <td>0.020163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_Children</th>\n",
       "      <td>0.136884</td>\n",
       "      <td>-0.358790</td>\n",
       "      <td>0.181151</td>\n",
       "      <td>0.355838</td>\n",
       "      <td>0.034760</td>\n",
       "      <td>-0.033890</td>\n",
       "      <td>-0.133146</td>\n",
       "      <td>0.009655</td>\n",
       "      <td>0.097669</td>\n",
       "      <td>-0.111109</td>\n",
       "      <td>-0.024712</td>\n",
       "      <td>-0.051107</td>\n",
       "      <td>0.126263</td>\n",
       "      <td>-0.088391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.207148</td>\n",
       "      <td>-0.291558</td>\n",
       "      <td>-0.148595</td>\n",
       "      <td>-0.138816</td>\n",
       "      <td>0.127725</td>\n",
       "      <td>0.191023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_Youth</th>\n",
       "      <td>-0.005687</td>\n",
       "      <td>0.053502</td>\n",
       "      <td>-0.052569</td>\n",
       "      <td>-0.005918</td>\n",
       "      <td>-0.061748</td>\n",
       "      <td>0.089786</td>\n",
       "      <td>-0.147137</td>\n",
       "      <td>-0.026113</td>\n",
       "      <td>0.129419</td>\n",
       "      <td>-0.111044</td>\n",
       "      <td>-0.047306</td>\n",
       "      <td>0.049830</td>\n",
       "      <td>-0.019506</td>\n",
       "      <td>-0.040095</td>\n",
       "      <td>-0.207148</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.558139</td>\n",
       "      <td>-0.284461</td>\n",
       "      <td>0.090338</td>\n",
       "      <td>-0.047754</td>\n",
       "      <td>-0.157104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_Adult</th>\n",
       "      <td>-0.085847</td>\n",
       "      <td>0.188261</td>\n",
       "      <td>-0.108224</td>\n",
       "      <td>-0.162413</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.009040</td>\n",
       "      <td>-0.031816</td>\n",
       "      <td>0.011441</td>\n",
       "      <td>-0.175810</td>\n",
       "      <td>0.026079</td>\n",
       "      <td>0.024221</td>\n",
       "      <td>-0.048276</td>\n",
       "      <td>-0.021995</td>\n",
       "      <td>-0.003585</td>\n",
       "      <td>-0.291558</td>\n",
       "      <td>-0.558139</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.400374</td>\n",
       "      <td>0.088967</td>\n",
       "      <td>0.021632</td>\n",
       "      <td>-0.054129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_MiddleAge</th>\n",
       "      <td>0.024854</td>\n",
       "      <td>-0.042835</td>\n",
       "      <td>0.073383</td>\n",
       "      <td>-0.052993</td>\n",
       "      <td>0.042283</td>\n",
       "      <td>-0.096346</td>\n",
       "      <td>0.309898</td>\n",
       "      <td>0.006038</td>\n",
       "      <td>0.017444</td>\n",
       "      <td>0.196536</td>\n",
       "      <td>0.046160</td>\n",
       "      <td>0.048138</td>\n",
       "      <td>-0.070654</td>\n",
       "      <td>0.105424</td>\n",
       "      <td>-0.148595</td>\n",
       "      <td>-0.284461</td>\n",
       "      <td>-0.400374</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.118123</td>\n",
       "      <td>-0.060618</td>\n",
       "      <td>0.106516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_1</th>\n",
       "      <td>-0.198067</td>\n",
       "      <td>0.338625</td>\n",
       "      <td>-0.279801</td>\n",
       "      <td>-0.135075</td>\n",
       "      <td>-0.201512</td>\n",
       "      <td>-0.096489</td>\n",
       "      <td>-0.301390</td>\n",
       "      <td>-0.111456</td>\n",
       "      <td>-0.166584</td>\n",
       "      <td>-0.145139</td>\n",
       "      <td>-0.040024</td>\n",
       "      <td>0.113329</td>\n",
       "      <td>-0.000840</td>\n",
       "      <td>-0.143162</td>\n",
       "      <td>-0.138816</td>\n",
       "      <td>0.090338</td>\n",
       "      <td>0.088967</td>\n",
       "      <td>-0.118123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.259648</td>\n",
       "      <td>-0.269007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_2</th>\n",
       "      <td>0.043153</td>\n",
       "      <td>-0.145954</td>\n",
       "      <td>0.194048</td>\n",
       "      <td>-0.077273</td>\n",
       "      <td>0.005520</td>\n",
       "      <td>-0.038317</td>\n",
       "      <td>-0.275681</td>\n",
       "      <td>0.314612</td>\n",
       "      <td>0.099850</td>\n",
       "      <td>0.114970</td>\n",
       "      <td>-0.036610</td>\n",
       "      <td>-0.000569</td>\n",
       "      <td>0.015388</td>\n",
       "      <td>-0.130950</td>\n",
       "      <td>0.127725</td>\n",
       "      <td>-0.047754</td>\n",
       "      <td>0.021632</td>\n",
       "      <td>-0.060618</td>\n",
       "      <td>-0.259648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.246061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_3</th>\n",
       "      <td>0.060373</td>\n",
       "      <td>-0.217122</td>\n",
       "      <td>0.081314</td>\n",
       "      <td>0.267558</td>\n",
       "      <td>-0.029439</td>\n",
       "      <td>-0.056012</td>\n",
       "      <td>0.130974</td>\n",
       "      <td>0.183454</td>\n",
       "      <td>0.036281</td>\n",
       "      <td>0.061103</td>\n",
       "      <td>0.036756</td>\n",
       "      <td>-0.078444</td>\n",
       "      <td>-0.004378</td>\n",
       "      <td>0.020163</td>\n",
       "      <td>0.191023</td>\n",
       "      <td>-0.157104</td>\n",
       "      <td>-0.054129</td>\n",
       "      <td>0.106516</td>\n",
       "      <td>-0.269007</td>\n",
       "      <td>-0.246061</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Survived    Single  Small_Family  Big_Family  Embarked_C  \\\n",
       "Survived       1.000000 -0.203367      0.279855   -0.125147    0.168240   \n",
       "Single        -0.203367  1.000000     -0.859931   -0.336825   -0.095298   \n",
       "Small_Family   0.279855 -0.859931      1.000000   -0.190940    0.158586   \n",
       "Big_Family    -0.125147 -0.336825     -0.190940    1.000000   -0.109274   \n",
       "Embarked_C     0.168240 -0.095298      0.158586   -0.109274    1.000000   \n",
       "Embarked_Q     0.003650  0.086464     -0.087093   -0.005620   -0.148258   \n",
       "Pclass_1       0.285904 -0.113364      0.168568   -0.092945    0.296423   \n",
       "Pclass_2       0.093349 -0.039070      0.104546   -0.117721   -0.125416   \n",
       "Sex_female     0.543351 -0.303646      0.260747    0.102954    0.082853   \n",
       "Sal_Mrs        0.339040 -0.365454      0.372029    0.016535    0.061395   \n",
       "Sal_Royalty    0.033391 -0.000414      0.011568   -0.020544    0.079020   \n",
       "Tkt_A5        -0.092199  0.050549     -0.029666   -0.042488   -0.074892   \n",
       "Tkt_CA        -0.019137 -0.117245      0.006430    0.213620   -0.105869   \n",
       "Tkt_PC         0.147062 -0.019783      0.041377   -0.038283    0.397139   \n",
       "Age_Children   0.136884 -0.358790      0.181151    0.355838    0.034760   \n",
       "Age_Youth     -0.005687  0.053502     -0.052569   -0.005918   -0.061748   \n",
       "Age_Adult     -0.085847  0.188261     -0.108224   -0.162413    0.000506   \n",
       "Age_MiddleAge  0.024854 -0.042835      0.073383   -0.052993    0.042283   \n",
       "Fare_1        -0.198067  0.338625     -0.279801   -0.135075   -0.201512   \n",
       "Fare_2         0.043153 -0.145954      0.194048   -0.077273    0.005520   \n",
       "Fare_3         0.060373 -0.217122      0.081314    0.267558   -0.029439   \n",
       "\n",
       "               Embarked_Q  Pclass_1  Pclass_2  Sex_female   Sal_Mrs  \\\n",
       "Survived         0.003650  0.285904  0.093349    0.543351  0.339040   \n",
       "Single           0.086464 -0.113364 -0.039070   -0.303646 -0.365454   \n",
       "Small_Family    -0.087093  0.168568  0.104546    0.260747  0.372029   \n",
       "Big_Family      -0.005620 -0.092945 -0.117721    0.102954  0.016535   \n",
       "Embarked_C      -0.148258  0.296423 -0.125416    0.082853  0.061395   \n",
       "Embarked_Q       1.000000 -0.155342 -0.127301    0.074115 -0.089739   \n",
       "Pclass_1        -0.155342  1.000000 -0.288585    0.098013  0.088207   \n",
       "Pclass_2        -0.127301 -0.288585  1.000000    0.064746  0.121239   \n",
       "Sex_female       0.074115  0.098013  0.064746    1.000000  0.547600   \n",
       "Sal_Mrs         -0.089739  0.088207  0.121239    0.547600  1.000000   \n",
       "Sal_Royalty     -0.023105  0.132798 -0.038324    0.007483 -0.030347   \n",
       "Tkt_A5          -0.021454 -0.087887 -0.079259   -0.083638 -0.020155   \n",
       "Tkt_CA          -0.067548 -0.124239  0.139384    0.006179  0.019250   \n",
       "Tkt_PC          -0.082643  0.475007 -0.137080    0.073639  0.046197   \n",
       "Age_Children    -0.033890 -0.133146  0.009655    0.097669 -0.111109   \n",
       "Age_Youth        0.089786 -0.147137 -0.026113    0.129419 -0.111044   \n",
       "Age_Adult        0.009040 -0.031816  0.011441   -0.175810  0.026079   \n",
       "Age_MiddleAge   -0.096346  0.309898  0.006038    0.017444  0.196536   \n",
       "Fare_1          -0.096489 -0.301390 -0.111456   -0.166584 -0.145139   \n",
       "Fare_2          -0.038317 -0.275681  0.314612    0.099850  0.114970   \n",
       "Fare_3          -0.056012  0.130974  0.183454    0.036281  0.061103   \n",
       "\n",
       "               Sal_Royalty    Tkt_A5    Tkt_CA    Tkt_PC  Age_Children  \\\n",
       "Survived          0.033391 -0.092199 -0.019137  0.147062      0.136884   \n",
       "Single           -0.000414  0.050549 -0.117245 -0.019783     -0.358790   \n",
       "Small_Family      0.011568 -0.029666  0.006430  0.041377      0.181151   \n",
       "Big_Family       -0.020544 -0.042488  0.213620 -0.038283      0.355838   \n",
       "Embarked_C        0.079020 -0.074892 -0.105869  0.397139      0.034760   \n",
       "Embarked_Q       -0.023105 -0.021454 -0.067548 -0.082643     -0.033890   \n",
       "Pclass_1          0.132798 -0.087887 -0.124239  0.475007     -0.133146   \n",
       "Pclass_2         -0.038324 -0.079259  0.139384 -0.137080      0.009655   \n",
       "Sex_female        0.007483 -0.083638  0.006179  0.073639      0.097669   \n",
       "Sal_Mrs          -0.030347 -0.020155  0.019250  0.046197     -0.111109   \n",
       "Sal_Royalty       1.000000 -0.011671 -0.016499  0.099717     -0.024712   \n",
       "Tkt_A5           -0.011671  1.000000 -0.034122 -0.041747     -0.051107   \n",
       "Tkt_CA           -0.016499 -0.034122  1.000000 -0.059014      0.126263   \n",
       "Tkt_PC            0.099717 -0.041747 -0.059014  1.000000     -0.088391   \n",
       "Age_Children     -0.024712 -0.051107  0.126263 -0.088391      1.000000   \n",
       "Age_Youth        -0.047306  0.049830 -0.019506 -0.040095     -0.207148   \n",
       "Age_Adult         0.024221 -0.048276 -0.021995 -0.003585     -0.291558   \n",
       "Age_MiddleAge     0.046160  0.048138 -0.070654  0.105424     -0.148595   \n",
       "Fare_1           -0.040024  0.113329 -0.000840 -0.143162     -0.138816   \n",
       "Fare_2           -0.036610 -0.000569  0.015388 -0.130950      0.127725   \n",
       "Fare_3            0.036756 -0.078444 -0.004378  0.020163      0.191023   \n",
       "\n",
       "               Age_Youth  Age_Adult  Age_MiddleAge    Fare_1    Fare_2  \\\n",
       "Survived       -0.005687  -0.085847       0.024854 -0.198067  0.043153   \n",
       "Single          0.053502   0.188261      -0.042835  0.338625 -0.145954   \n",
       "Small_Family   -0.052569  -0.108224       0.073383 -0.279801  0.194048   \n",
       "Big_Family     -0.005918  -0.162413      -0.052993 -0.135075 -0.077273   \n",
       "Embarked_C     -0.061748   0.000506       0.042283 -0.201512  0.005520   \n",
       "Embarked_Q      0.089786   0.009040      -0.096346 -0.096489 -0.038317   \n",
       "Pclass_1       -0.147137  -0.031816       0.309898 -0.301390 -0.275681   \n",
       "Pclass_2       -0.026113   0.011441       0.006038 -0.111456  0.314612   \n",
       "Sex_female      0.129419  -0.175810       0.017444 -0.166584  0.099850   \n",
       "Sal_Mrs        -0.111044   0.026079       0.196536 -0.145139  0.114970   \n",
       "Sal_Royalty    -0.047306   0.024221       0.046160 -0.040024 -0.036610   \n",
       "Tkt_A5          0.049830  -0.048276       0.048138  0.113329 -0.000569   \n",
       "Tkt_CA         -0.019506  -0.021995      -0.070654 -0.000840  0.015388   \n",
       "Tkt_PC         -0.040095  -0.003585       0.105424 -0.143162 -0.130950   \n",
       "Age_Children   -0.207148  -0.291558      -0.148595 -0.138816  0.127725   \n",
       "Age_Youth       1.000000  -0.558139      -0.284461  0.090338 -0.047754   \n",
       "Age_Adult      -0.558139   1.000000      -0.400374  0.088967  0.021632   \n",
       "Age_MiddleAge  -0.284461  -0.400374       1.000000 -0.118123 -0.060618   \n",
       "Fare_1          0.090338   0.088967      -0.118123  1.000000 -0.259648   \n",
       "Fare_2         -0.047754   0.021632      -0.060618 -0.259648  1.000000   \n",
       "Fare_3         -0.157104  -0.054129       0.106516 -0.269007 -0.246061   \n",
       "\n",
       "                 Fare_3  \n",
       "Survived       0.060373  \n",
       "Single        -0.217122  \n",
       "Small_Family   0.081314  \n",
       "Big_Family     0.267558  \n",
       "Embarked_C    -0.029439  \n",
       "Embarked_Q    -0.056012  \n",
       "Pclass_1       0.130974  \n",
       "Pclass_2       0.183454  \n",
       "Sex_female     0.036281  \n",
       "Sal_Mrs        0.061103  \n",
       "Sal_Royalty    0.036756  \n",
       "Tkt_A5        -0.078444  \n",
       "Tkt_CA        -0.004378  \n",
       "Tkt_PC         0.020163  \n",
       "Age_Children   0.191023  \n",
       "Age_Youth     -0.157104  \n",
       "Age_Adult     -0.054129  \n",
       "Age_MiddleAge  0.106516  \n",
       "Fare_1        -0.269007  \n",
       "Fare_2        -0.246061  \n",
       "Fare_3         1.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prep.corr(method='spearman')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PREDICTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sadha\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import KFold   #For K-fold cross validation\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedKFold,train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_comb(dat,modl):\n",
    "\n",
    "    y = dat['Survived']\n",
    "    x = dat\n",
    "    x = x.drop('Survived', axis = 1)\n",
    "    #Model fit\n",
    "    modl.fit(x,y)\n",
    "    \n",
    "    #Model Predictions\n",
    "    predictions = modl.predict(x)\n",
    "    \n",
    "    #Accuracy\n",
    "    acc = 0\n",
    "    acc = metrics.accuracy_score(predictions,y)\n",
    "    print(\"Accuracy score: \", acc)\n",
    "    \n",
    "    #Kfold cross validation\n",
    "    cf = KFold(dat.shape[0], n_folds= 5)\n",
    "    acc_all = []\n",
    "    for train, test in cf:\n",
    "        x_tr = x.iloc[train,:]\n",
    "        y_tr = y.iloc[train]\n",
    "        \n",
    "        modl.fit(x_tr, y_tr)\n",
    "        pred = modl.predict(x_tr)\n",
    "        \n",
    "        x_ts = x.iloc[test, :]\n",
    "        y_ts = y.iloc[test]\n",
    "        \n",
    "        acc_all.append(modl.score(x_ts,y_ts))\n",
    "    \n",
    "    print(\"Cross fold score : \", np.mean(acc_all))\n",
    "    \n",
    "    modl.fit(x,y)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8170594837261503\n",
      "Cross fold score :  0.8080848659845584\n"
     ]
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "modl = LogisticRegression()\n",
    "model_comb(prep,modl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8877665544332211\n",
      "Cross fold score :  0.815956311593748\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "modl = RandomForestClassifier()\n",
    "model_comb(prep,modl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 20)\n"
     ]
    }
   ],
   "source": [
    "model = SelectFromModel(modl, prefit=True)\n",
    "tr = prep.drop(columns= 'Survived')\n",
    "train_reduced = model.transform(tr)\n",
    "train_reduced = pd.DataFrame(train_reduced)\n",
    "print(train_reduced.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(418, 20)\n"
     ]
    }
   ],
   "source": [
    "test_reduced = pd.DataFrame(model.transform(chk))\n",
    "print(test_reduced.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_reduced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8877665544332211\n",
      "Cross fold score :  0.8316364321134895\n"
     ]
    }
   ],
   "source": [
    "\n",
    "train_reduced['Survived'] = prep['Survived']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8922558922558923\n",
      "Cross fold score :  0.8260310087251271\n"
     ]
    }
   ],
   "source": [
    "#Random Forest\n",
    "modl = RandomForestClassifier()\n",
    "model_comb(train_reduced,modl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8305274971941639\n",
      "Cross fold score :  0.8193019898311468\n"
     ]
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "modl = LogisticRegression()\n",
    "model_comb(train_reduced,modl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.9337822671156004\n",
      "Cross fold score :  0.8058753373925052\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree Classifier\n",
    "modl = DecisionTreeClassifier()\n",
    "model_comb(prep,modl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8451178451178452\n",
      "Cross fold score :  0.7755508128805474\n"
     ]
    }
   ],
   "source": [
    "#KNN\n",
    "modl = KNeighborsClassifier(n_neighbors= 5)\n",
    "model_comb(prep,modl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sadha\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.9079685746352413\n",
      "Cross fold score :  0.822666499278137\n"
     ]
    }
   ],
   "source": [
    "#Neural Network\n",
    "modl = MLPClassifier()\n",
    "model_comb(prep,modl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.8799102132435466\n",
      "Cross fold score :  0.8260435628648548\n"
     ]
    }
   ],
   "source": [
    "#Gradient Boosting\n",
    "modl = GradientBoostingClassifier()\n",
    "model_comb(prep,modl)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Single</th>\n",
       "      <th>Small_Family</th>\n",
       "      <th>Big_Family</th>\n",
       "      <th>Embarked_C</th>\n",
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       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
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       "      <th>Deck_B</th>\n",
       "      <th>Deck_C</th>\n",
       "      <th>Deck_D</th>\n",
       "      <th>Deck_E</th>\n",
       "      <th>Deck_F</th>\n",
       "      <th>Deck_G</th>\n",
       "      <th>Deck_T</th>\n",
       "      <th>Deck_U</th>\n",
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       "      <th>Sal_Mrs</th>\n",
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       "      <th>Tkt_A4</th>\n",
       "      <th>Tkt_A5</th>\n",
       "      <th>Tkt_AQ3</th>\n",
       "      <th>Tkt_AQ4</th>\n",
       "      <th>Tkt_AS</th>\n",
       "      <th>Tkt_C</th>\n",
       "      <th>Tkt_CA</th>\n",
       "      <th>Tkt_CASOTON</th>\n",
       "      <th>Tkt_FC</th>\n",
       "      <th>Tkt_FCC</th>\n",
       "      <th>Tkt_Fa</th>\n",
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       "      <th>Tkt_LP</th>\n",
       "      <th>Tkt_PC</th>\n",
       "      <th>Tkt_PP</th>\n",
       "      <th>Tkt_PPP</th>\n",
       "      <th>Tkt_SC</th>\n",
       "      <th>Tkt_SCA3</th>\n",
       "      <th>Tkt_SCA4</th>\n",
       "      <th>Tkt_SCAH</th>\n",
       "      <th>Tkt_SCOW</th>\n",
       "      <th>Tkt_SCPARIS</th>\n",
       "      <th>Tkt_SCParis</th>\n",
       "      <th>Tkt_SOC</th>\n",
       "      <th>Tkt_SOP</th>\n",
       "      <th>Tkt_SOPP</th>\n",
       "      <th>Tkt_SOTONO2</th>\n",
       "      <th>Tkt_SOTONOQ</th>\n",
       "      <th>Tkt_SP</th>\n",
       "      <th>Tkt_STONO</th>\n",
       "      <th>Tkt_STONO2</th>\n",
       "      <th>Tkt_STONOQ</th>\n",
       "      <th>Tkt_SWPP</th>\n",
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       "      <th>Tkt_WEP</th>\n",
       "      <th>Tkt_XXX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.350478</td>\n",
       "      <td>35.576535</td>\n",
       "      <td>1100.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>0.346890</td>\n",
       "      <td>0.047847</td>\n",
       "      <td>0.244019</td>\n",
       "      <td>0.110048</td>\n",
       "      <td>0.645933</td>\n",
       "      <td>0.255981</td>\n",
       "      <td>0.222488</td>\n",
       "      <td>0.521531</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.016746</td>\n",
       "      <td>0.043062</td>\n",
       "      <td>0.083732</td>\n",
       "      <td>0.031100</td>\n",
       "      <td>0.021531</td>\n",
       "      <td>0.019139</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.782297</td>\n",
       "      <td>0.050239</td>\n",
       "      <td>0.191388</td>\n",
       "      <td>0.574163</td>\n",
       "      <td>0.172249</td>\n",
       "      <td>0.011962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.007177</td>\n",
       "      <td>0.016746</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007177</td>\n",
       "      <td>0.064593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004785</td>\n",
       "      <td>0.009569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.076555</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.004785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016746</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.004785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009569</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.021531</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004785</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011962</td>\n",
       "      <td>0.002392</td>\n",
       "      <td>0.708134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.230491</td>\n",
       "      <td>55.850103</td>\n",
       "      <td>120.810458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.489380</td>\n",
       "      <td>0.476551</td>\n",
       "      <td>0.213698</td>\n",
       "      <td>0.430019</td>\n",
       "      <td>0.313324</td>\n",
       "      <td>0.478803</td>\n",
       "      <td>0.436934</td>\n",
       "      <td>0.416416</td>\n",
       "      <td>0.500135</td>\n",
       "      <td>0.481622</td>\n",
       "      <td>0.481622</td>\n",
       "      <td>0.128474</td>\n",
       "      <td>0.203240</td>\n",
       "      <td>0.277317</td>\n",
       "      <td>0.173797</td>\n",
       "      <td>0.145320</td>\n",
       "      <td>0.137177</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.413179</td>\n",
       "      <td>0.218700</td>\n",
       "      <td>0.393865</td>\n",
       "      <td>0.495062</td>\n",
       "      <td>0.378049</td>\n",
       "      <td>0.108844</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.084514</td>\n",
       "      <td>0.128474</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.084514</td>\n",
       "      <td>0.246101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.069088</td>\n",
       "      <td>0.097471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.266203</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.069088</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.128474</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.069088</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.097471</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.145320</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.069088</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.108844</td>\n",
       "      <td>0.048912</td>\n",
       "      <td>0.455166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>892.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.250000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>996.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>1100.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.375000</td>\n",
       "      <td>31.471875</td>\n",
       "      <td>1204.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age        Fare  PassengerId  Dead      Single  Small_Family  \\\n",
       "count  418.000000  418.000000   418.000000   0.0  418.000000    418.000000   \n",
       "mean    29.350478   35.576535  1100.500000   NaN    0.605263      0.346890   \n",
       "std     13.230491   55.850103   120.810458   NaN    0.489380      0.476551   \n",
       "min      0.170000    0.000000   892.000000   NaN    0.000000      0.000000   \n",
       "25%     21.250000    7.895800   996.250000   NaN    0.000000      0.000000   \n",
       "50%     26.000000   14.454200  1100.500000   NaN    1.000000      0.000000   \n",
       "75%     36.375000   31.471875  1204.750000   NaN    1.000000      1.000000   \n",
       "max     76.000000  512.329200  1309.000000   NaN    1.000000      1.000000   \n",
       "\n",
       "       Big_Family  Embarked_C  Embarked_Q  Embarked_S    Pclass_1    Pclass_2  \\\n",
       "count  418.000000  418.000000  418.000000  418.000000  418.000000  418.000000   \n",
       "mean     0.047847    0.244019    0.110048    0.645933    0.255981    0.222488   \n",
       "std      0.213698    0.430019    0.313324    0.478803    0.436934    0.416416   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      0.000000    0.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "75%      0.000000    0.000000    0.000000    1.000000    1.000000    0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         Pclass_3  Sex_female    Sex_male      Deck_A      Deck_B      Deck_C  \\\n",
       "count  418.000000  418.000000  418.000000  418.000000  418.000000  418.000000   \n",
       "mean     0.521531    0.363636    0.636364    0.016746    0.043062    0.083732   \n",
       "std      0.500135    0.481622    0.481622    0.128474    0.203240    0.277317   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      1.000000    0.000000    1.000000    0.000000    0.000000    0.000000   \n",
       "75%      1.000000    1.000000    1.000000    0.000000    0.000000    0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "           Deck_D      Deck_E      Deck_F      Deck_G  Deck_T      Deck_U  \\\n",
       "count  418.000000  418.000000  418.000000  418.000000   418.0  418.000000   \n",
       "mean     0.031100    0.021531    0.019139    0.002392     0.0    0.782297   \n",
       "std      0.173797    0.145320    0.137177    0.048912     0.0    0.413179   \n",
       "min      0.000000    0.000000    0.000000    0.000000     0.0    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000     0.0    1.000000   \n",
       "50%      0.000000    0.000000    0.000000    0.000000     0.0    1.000000   \n",
       "75%      0.000000    0.000000    0.000000    0.000000     0.0    1.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000     0.0    1.000000   \n",
       "\n",
       "       Sal_Master    Sal_Miss      Sal_Mr     Sal_Mrs  Sal_Officer  \\\n",
       "count  418.000000  418.000000  418.000000  418.000000   418.000000   \n",
       "mean     0.050239    0.191388    0.574163    0.172249     0.011962   \n",
       "std      0.218700    0.393865    0.495062    0.378049     0.108844   \n",
       "min      0.000000    0.000000    0.000000    0.000000     0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000     0.000000   \n",
       "50%      0.000000    0.000000    1.000000    0.000000     0.000000   \n",
       "75%      0.000000    0.000000    1.000000    0.000000     0.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000     1.000000   \n",
       "\n",
       "       Sal_Royalty       Tkt_A      Tkt_A4      Tkt_A5     Tkt_AQ3  \\\n",
       "count        418.0  418.000000  418.000000  418.000000  418.000000   \n",
       "mean           0.0    0.002392    0.007177    0.016746    0.002392   \n",
       "std            0.0    0.048912    0.084514    0.128474    0.048912   \n",
       "min            0.0    0.000000    0.000000    0.000000    0.000000   \n",
       "25%            0.0    0.000000    0.000000    0.000000    0.000000   \n",
       "50%            0.0    0.000000    0.000000    0.000000    0.000000   \n",
       "75%            0.0    0.000000    0.000000    0.000000    0.000000   \n",
       "max            0.0    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "          Tkt_AQ4  Tkt_AS       Tkt_C      Tkt_CA  Tkt_CASOTON      Tkt_FC  \\\n",
       "count  418.000000   418.0  418.000000  418.000000        418.0  418.000000   \n",
       "mean     0.002392     0.0    0.007177    0.064593          0.0    0.004785   \n",
       "std      0.048912     0.0    0.084514    0.246101          0.0    0.069088   \n",
       "min      0.000000     0.0    0.000000    0.000000          0.0    0.000000   \n",
       "25%      0.000000     0.0    0.000000    0.000000          0.0    0.000000   \n",
       "50%      0.000000     0.0    0.000000    0.000000          0.0    0.000000   \n",
       "75%      0.000000     0.0    0.000000    0.000000          0.0    0.000000   \n",
       "max      1.000000     0.0    1.000000    1.000000          0.0    1.000000   \n",
       "\n",
       "          Tkt_FCC  Tkt_Fa  Tkt_LINE      Tkt_LP      Tkt_PC      Tkt_PP  \\\n",
       "count  418.000000   418.0     418.0  418.000000  418.000000  418.000000   \n",
       "mean     0.009569     0.0       0.0    0.002392    0.076555    0.002392   \n",
       "std      0.097471     0.0       0.0    0.048912    0.266203    0.048912   \n",
       "min      0.000000     0.0       0.0    0.000000    0.000000    0.000000   \n",
       "25%      0.000000     0.0       0.0    0.000000    0.000000    0.000000   \n",
       "50%      0.000000     0.0       0.0    0.000000    0.000000    0.000000   \n",
       "75%      0.000000     0.0       0.0    0.000000    0.000000    0.000000   \n",
       "max      1.000000     0.0       0.0    1.000000    1.000000    1.000000   \n",
       "\n",
       "       Tkt_PPP      Tkt_SC    Tkt_SCA3    Tkt_SCA4    Tkt_SCAH  Tkt_SCOW  \\\n",
       "count    418.0  418.000000  418.000000  418.000000  418.000000     418.0   \n",
       "mean       0.0    0.002392    0.002392    0.002392    0.004785       0.0   \n",
       "std        0.0    0.048912    0.048912    0.048912    0.069088       0.0   \n",
       "min        0.0    0.000000    0.000000    0.000000    0.000000       0.0   \n",
       "25%        0.0    0.000000    0.000000    0.000000    0.000000       0.0   \n",
       "50%        0.0    0.000000    0.000000    0.000000    0.000000       0.0   \n",
       "75%        0.0    0.000000    0.000000    0.000000    0.000000       0.0   \n",
       "max        0.0    1.000000    1.000000    1.000000    1.000000       0.0   \n",
       "\n",
       "       Tkt_SCPARIS  Tkt_SCParis     Tkt_SOC  Tkt_SOP    Tkt_SOPP  Tkt_SOTONO2  \\\n",
       "count   418.000000   418.000000  418.000000    418.0  418.000000   418.000000   \n",
       "mean      0.016746     0.002392    0.004785      0.0    0.009569     0.002392   \n",
       "std       0.128474     0.048912    0.069088      0.0    0.097471     0.048912   \n",
       "min       0.000000     0.000000    0.000000      0.0    0.000000     0.000000   \n",
       "25%       0.000000     0.000000    0.000000      0.0    0.000000     0.000000   \n",
       "50%       0.000000     0.000000    0.000000      0.0    0.000000     0.000000   \n",
       "75%       0.000000     0.000000    0.000000      0.0    0.000000     0.000000   \n",
       "max       1.000000     1.000000    1.000000      0.0    1.000000     1.000000   \n",
       "\n",
       "       Tkt_SOTONOQ  Tkt_SP   Tkt_STONO  Tkt_STONO2  Tkt_STONOQ  Tkt_SWPP  \\\n",
       "count   418.000000   418.0  418.000000  418.000000  418.000000     418.0   \n",
       "mean      0.021531     0.0    0.004785    0.002392    0.002392       0.0   \n",
       "std       0.145320     0.0    0.069088    0.048912    0.048912       0.0   \n",
       "min       0.000000     0.0    0.000000    0.000000    0.000000       0.0   \n",
       "25%       0.000000     0.0    0.000000    0.000000    0.000000       0.0   \n",
       "50%       0.000000     0.0    0.000000    0.000000    0.000000       0.0   \n",
       "75%       0.000000     0.0    0.000000    0.000000    0.000000       0.0   \n",
       "max       1.000000     0.0    1.000000    1.000000    1.000000       0.0   \n",
       "\n",
       "           Tkt_WC     Tkt_WEP     Tkt_XXX  \n",
       "count  418.000000  418.000000  418.000000  \n",
       "mean     0.011962    0.002392    0.708134  \n",
       "std      0.108844    0.048912    0.455166  \n",
       "min      0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000  \n",
       "50%      0.000000    0.000000    1.000000  \n",
       "75%      0.000000    0.000000    1.000000  \n",
       "max      1.000000    1.000000    1.000000  "
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chk.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "chk_PID = pd.DataFrame(chk['PassengerId'])\n",
    "chk = chk.drop(columns=['PassengerId'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     0\n",
       "0  0.0\n",
       "1  1.0\n",
       "2  0.0\n",
       "3  0.0\n",
       "4  1.0"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chk_pred = pd.DataFrame(modl.predict(chk))\n",
    "chk_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 168,
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   ],
   "source": [
    "chk_pred = pd.DataFrame(modl.predict(test_reduced))\n",
    "chk_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "chk_pred.to_csv(\"Result5\", sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
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       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
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       "      <td>1307</td>\n",
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       "      <td>1308</td>\n",
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       "      <td>1309</td>\n",
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      ],
      "text/plain": [
       "      PassengerId\n",
       "1304         1305\n",
       "1305         1306\n",
       "1306         1307\n",
       "1307         1308\n",
       "1308         1309"
      ]
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     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "chk_PID.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HYPER PARAMETER TUNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = prep['Survived']\n",
    "x = prep\n",
    "x = x.drop('Survived', axis = 1)\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.30, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 864 candidates, totalling 4320 fits\n",
      "Best score: 0.8417508417508418\n",
      "Best parameters: {'bootstrap': False, 'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 10}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 4320 out of 4320 | elapsed:  3.9min finished\n"
     ]
    }
   ],
   "source": [
    "parameter_grid = {\n",
    "             'max_depth' : [6, 8, 10, 12],\n",
    "             'n_estimators': [30, 20, 10],\n",
    "             'max_features': ['sqrt', 'auto', 'log2'],\n",
    "             'min_samples_split': [2, 5 , 7],\n",
    "             'min_samples_leaf': [1,2, 3, 10],\n",
    "             'bootstrap': [True, False],\n",
    "             }\n",
    "forest = RandomForestClassifier()\n",
    "cross_validation = StratifiedKFold(n_splits=5)\n",
    "\n",
    "grid_search = GridSearchCV(forest,\n",
    "                           scoring='accuracy',\n",
    "                           param_grid=parameter_grid,\n",
    "                           cv=cross_validation,\n",
    "                           verbose=1\n",
    "                          )\n",
    "\n",
    "grid_search.fit(x, y)\n",
    "model = grid_search\n",
    "parameters = grid_search.best_params_\n",
    "\n",
    "print('Best score: {}'.format(grid_search.best_score_))\n",
    "print('Best parameters: {}'.format(grid_search.best_params_))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'bootstrap': False, 'min_samples_leaf': 2, 'n_estimators': 10, \n",
    "              'min_samples_split': 5, 'max_features': 'sqrt', 'max_depth': 10}\n",
    "\n",
    "model = RandomForestClassifier(**parameters)\n",
    "model.fit(X_train, Y_train)\n",
    "modl = model.fit(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold :  0.0 Accuracy :  0.8022388059701493\n",
      "Threshold :  0.008702668571865196 Accuracy :  0.7873134328358209\n",
      "Threshold :  0.009256478187315292 Accuracy :  0.7873134328358209\n",
      "Threshold :  0.010903247245301093 Accuracy :  0.7947761194029851\n",
      "Threshold :  0.01204798381038224 Accuracy :  0.7985074626865671\n",
      "Threshold :  0.014827986421486975 Accuracy :  0.8097014925373134\n",
      "Threshold :  0.01567337535456277 Accuracy :  0.7985074626865671\n",
      "Threshold :  0.017233889600412233 Accuracy :  0.7910447761194029\n",
      "Threshold :  0.020392179384125714 Accuracy :  0.7798507462686567\n",
      "Threshold :  0.020790746306963617 Accuracy :  0.7873134328358209\n",
      "Threshold :  0.022045841410378315 Accuracy :  0.7761194029850746\n",
      "Threshold :  0.02635287025989986 Accuracy :  0.7798507462686567\n",
      "Threshold :  0.026623774137416345 Accuracy :  0.7910447761194029\n",
      "Threshold :  0.02792236579593273 Accuracy :  0.7835820895522388\n",
      "Threshold :  0.028193497030069424 Accuracy :  0.7686567164179104\n",
      "Threshold :  0.032345022877182245 Accuracy :  0.7686567164179104\n",
      "Threshold :  0.039862087887490696 Accuracy :  0.75\n",
      "Threshold :  0.04744270432334101 Accuracy :  0.75\n",
      "Threshold :  0.05485519096858758 Accuracy :  0.75\n",
      "Threshold :  0.06914706198837973 Accuracy :  0.746268656716418\n",
      "Threshold :  0.08000947640160597 Accuracy :  0.7425373134328358\n",
      "Threshold :  0.10953635997510999 Accuracy :  0.7425373134328358\n",
      "Threshold :  0.12366460783571827 Accuracy :  0.7425373134328358\n",
      "Threshold :  0.18217058422647275 Accuracy :  0.7425373134328358\n"
     ]
    }
   ],
   "source": [
    "tresholds = np.sort(model.feature_importances_)\n",
    "for tresh in tresholds:\n",
    "    a = SelectFromModel(model, threshold = tresh, prefit=True)\n",
    "    train_reduced = pd.DataFrame(a.transform(X_train))\n",
    "    test_reduced = pd.DataFrame(a.transform(X_test))\n",
    "    mmm =  RandomForestClassifier(**parameters)\n",
    "    mmm.fit(train_reduced,Y_train)\n",
    "    y_pred = mmm.predict(test_reduced)\n",
    "    accuracy = metrics.accuracy_score(Y_test,y_pred)\n",
    "    print(\"Threshold : \", tresh, \"Accuracy : \", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.00870267, 0.00925648, 0.01090325, 0.01204798,\n",
       "       0.01482799, 0.01567338, 0.01723389, 0.02039218, 0.02079075,\n",
       "       0.02204584, 0.02635287, 0.02662377, 0.02792237, 0.0281935 ,\n",
       "       0.03234502, 0.03986209, 0.0474427 , 0.05485519, 0.06914706,\n",
       "       0.08000948, 0.10953636, 0.12366461, 0.18217058])"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold :  0.18217058422647275 Accuracy :  0.7985074626865671\n"
     ]
    }
   ],
   "source": [
    "a = SelectFromModel(model, threshold = 0.014827986421486975 , prefit=True)\n",
    "train_reduced = pd.DataFrame(a.transform(X_train))\n",
    "test_reduced = pd.DataFrame(a.transform(X_test))\n",
    "mmm =  RandomForestClassifier(**parameters)\n",
    "mmm.fit(train_reduced,Y_train)\n",
    "y_pred = mmm.predict(test_reduced)\n",
    "accuracy = metrics.accuracy_score(Y_test,y_pred)\n",
    "print(\"Threshold : \", tresh, \"Accuracy : \", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "chk_PID = pd.DataFrame(chk['PassengerId'])\n",
    "chk = chk.drop(columns=['PassengerId'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "chk_pred = pd.DataFrame(model.predict(chk))\n",
    "chk_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "chk_pred.to_csv(\"Result3\", sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
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