Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Titanic Dataset Exploration and Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=pd.read_csv(\"train.csv\")\n",
    "test_df=pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 12), (418, 11))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape,test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Missing Values Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Filling missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Embarked'].fillna(train_df['Embarked'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['Age'].fillna(test_df['Age'].median(), inplace = True)\n",
    "train_df['Age'].fillna(train_df['Age'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "####Cabin Featueres has more than 75% of missing data in both Test and train data so we are remove the Cabin\n",
    "drop_column = ['Cabin']\n",
    "train_df.drop(drop_column, axis=1, inplace = True)\n",
    "test_df.drop(drop_column,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId    0\n",
      "Survived       0\n",
      "Pclass         0\n",
      "Name           0\n",
      "Sex            0\n",
      "Age            0\n",
      "SibSp          0\n",
      "Parch          0\n",
      "Ticket         0\n",
      "Fare           0\n",
      "Embarked       0\n",
      "dtype: int64\n",
      "__________________________________________________________________________________________\n",
      "PassengerId    0\n",
      "Pclass         0\n",
      "Name           0\n",
      "Sex            0\n",
      "Age            0\n",
      "SibSp          0\n",
      "Parch          0\n",
      "Ticket         0\n",
      "Fare           0\n",
      "Embarked       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_df.isnull().sum())\n",
    "print('___'*30)\n",
    "print(test_df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "## combine test and train as single to apply some function\n",
    "all_data=[train_df,test_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new feature FamilySize as a combination of SibSp and Parch\n",
    "for dataset in all_data:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "# Define function to extract titles from passenger names\n",
    "def get_title(name):\n",
    "    title_search = re.search(' ([A-Za-z]+)\\.', name)\n",
    "    # If the title exists, extract and return it.\n",
    "    if title_search:\n",
    "        return title_search.group(1)\n",
    "    return \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new feature Title, containing the titles of passenger names\n",
    "for dataset in all_data:\n",
    "    dataset['Title'] = dataset['Name'].apply(get_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group all non-common titles into one single grouping \"Rare\"\n",
    "for dataset in all_data:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', \n",
    "                                                 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "## create bin for age features\n",
    "for dataset in all_data:\n",
    "    dataset['Age_bin'] = pd.cut(dataset['Age'], bins=[0,12,20,40,120], labels=['Children','Teenage','Adult','Elder'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "## create bin for fare features\n",
    "for dataset in all_data:\n",
    "    dataset['Fare_bin'] = pd.cut(dataset['Fare'], bins=[0,7.91,14.45,31,120], labels=['Low_fare','median_fare',\n",
    "                                                                                      'Average_fare','high_fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 15), (891, 15))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Making a copy of both DataSet start working for copy of dataset\n",
    "traindf=train_df\n",
    "testdf=test_df\n",
    "\n",
    "traindf.shape,train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_dat=[traindf,testdf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the unwanted columns after Feature Engineering\n",
    "for dataset in all_dat:\n",
    "    drop_column = ['Age','Fare','Name','Ticket']\n",
    "    dataset.drop(drop_column, axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_column = ['PassengerId']\n",
    "traindf.drop(drop_column, axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Converting the catergical features in numerical by using dummy variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Title</th>\n",
       "      <th>Age_bin</th>\n",
       "      <th>Fare_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Low_fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Adult</td>\n",
       "      <td>high_fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "      <td>Adult</td>\n",
       "      <td>median_fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Adult</td>\n",
       "      <td>high_fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Adult</td>\n",
       "      <td>median_fare</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex  SibSp  Parch Embarked  FamilySize Title Age_bin  \\\n",
       "0         0       3    male      1      0        S           2    Mr   Adult   \n",
       "1         1       1  female      1      0        C           2   Mrs   Adult   \n",
       "2         1       3  female      0      0        S           1  Miss   Adult   \n",
       "3         1       1  female      1      0        S           2   Mrs   Adult   \n",
       "4         0       3    male      0      0        S           1    Mr   Adult   \n",
       "\n",
       "      Fare_bin  \n",
       "0     Low_fare  \n",
       "1    high_fare  \n",
       "2  median_fare  \n",
       "3    high_fare  \n",
       "4  median_fare  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Title</th>\n",
       "      <th>Age_bin</th>\n",
       "      <th>Fare_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Low_fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Elder</td>\n",
       "      <td>Low_fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Elder</td>\n",
       "      <td>median_fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Adult</td>\n",
       "      <td>median_fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Adult</td>\n",
       "      <td>median_fare</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex  SibSp  Parch Embarked  FamilySize Title  \\\n",
       "0          892       3    male      0      0        Q           1    Mr   \n",
       "1          893       3  female      1      0        S           2   Mrs   \n",
       "2          894       2    male      0      0        Q           1    Mr   \n",
       "3          895       3    male      0      0        S           1    Mr   \n",
       "4          896       3  female      1      1        S           3   Mrs   \n",
       "\n",
       "  Age_bin     Fare_bin  \n",
       "0   Adult     Low_fare  \n",
       "1   Elder     Low_fare  \n",
       "2   Elder  median_fare  \n",
       "3   Adult  median_fare  \n",
       "4   Adult  median_fare  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "traindf = pd.get_dummies(traindf, columns = [\"Sex\",\"Title\",\"Age_bin\",\"Embarked\",\"Fare_bin\"],\n",
    "                             prefix=[\"Sex\",\"Title\",\"Age_type\",\"Em_type\",\"Fare_type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "testdf = pd.get_dummies(testdf, columns = [\"Sex\",\"Title\",\"Age_bin\",\"Embarked\",\"Fare_bin\"],\n",
    "                             prefix=[\"Sex\",\"Title\",\"Age_type\",\"Em_type\",\"Fare_type\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 23), (418, 23))"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindf.shape,testdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "      <th>Parch</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  SibSp  Parch  FamilySize  Sex_female  Sex_male  \\\n",
       "0         0       3      1      0           2           0         1   \n",
       "1         1       1      1      0           2           1         0   \n",
       "2         1       3      0      0           1           1         0   \n",
       "3         1       1      1      0           2           1         0   \n",
       "4         0       3      0      0           1           0         1   \n",
       "\n",
       "   Title_Master  Title_Miss  Title_Mr         ...           Age_type_Teenage  \\\n",
       "0             0           0         1         ...                          0   \n",
       "1             0           0         0         ...                          0   \n",
       "2             0           1         0         ...                          0   \n",
       "3             0           0         0         ...                          0   \n",
       "4             0           0         1         ...                          0   \n",
       "\n",
       "   Age_type_Adult  Age_type_Elder  Em_type_C  Em_type_Q  Em_type_S  \\\n",
       "0               1               0          0          0          1   \n",
       "1               1               0          1          0          0   \n",
       "2               1               0          0          0          1   \n",
       "3               1               0          0          0          1   \n",
       "4               1               0          0          0          1   \n",
       "\n",
       "   Fare_type_Low_fare  Fare_type_median_fare  Fare_type_Average_fare  \\\n",
       "0                   1                      0                       0   \n",
       "1                   0                      0                       0   \n",
       "2                   0                      1                       0   \n",
       "3                   0                      0                       0   \n",
       "4                   0                      1                       0   \n",
       "\n",
       "   Fare_type_high_fare  \n",
       "0                    0  \n",
       "1                    1  \n",
       "2                    0  \n",
       "3                    1  \n",
       "4                    0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
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       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  SibSp  Parch  FamilySize  Sex_female  Sex_male  \\\n",
       "0          892       3      0      0           1           0         1   \n",
       "1          893       3      1      0           2           1         0   \n",
       "2          894       2      0      0           1           0         1   \n",
       "3          895       3      0      0           1           0         1   \n",
       "4          896       3      1      1           3           1         0   \n",
       "\n",
       "   Title_Master  Title_Miss  Title_Mr         ...           Age_type_Teenage  \\\n",
       "0             0           0         1         ...                          0   \n",
       "1             0           0         0         ...                          0   \n",
       "2             0           0         1         ...                          0   \n",
       "3             0           0         1         ...                          0   \n",
       "4             0           0         0         ...                          0   \n",
       "\n",
       "   Age_type_Adult  Age_type_Elder  Em_type_C  Em_type_Q  Em_type_S  \\\n",
       "0               1               0          0          1          0   \n",
       "1               0               1          0          0          1   \n",
       "2               0               1          0          1          0   \n",
       "3               1               0          0          0          1   \n",
       "4               1               0          0          0          1   \n",
       "\n",
       "   Fare_type_Low_fare  Fare_type_median_fare  Fare_type_Average_fare  \\\n",
       "0                   1                      0                       0   \n",
       "1                   1                      0                       0   \n",
       "2                   0                      1                       0   \n",
       "3                   0                      1                       0   \n",
       "4                   0                      1                       0   \n",
       "\n",
       "   Fare_type_high_fare  \n",
       "0                    0  \n",
       "1                    0  \n",
       "2                    0  \n",
       "3                    0  \n",
       "4                    0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Correlation Between The Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(traindf.corr(),annot=True,cmap='RdYlGn',linewidths=0.2) #data.corr()-->correlation matrix\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(20,12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Pairplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x182d4ae0e80>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 483.875x432 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(data=train_df, hue='Survived', palette = 'seismic',\n",
    "                 size=1.2,diag_kind = 'kde',diag_kws=dict(shade=True),plot_kws=dict(s=10) )\n",
    "g.set(xticklabels=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((623, 22), (268, 22), (623,), (268,))"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split #for split the data\n",
    "from sklearn.metrics import accuracy_score  #for accuracy_score\n",
    "from sklearn.model_selection import KFold #for K-fold cross validation\n",
    "from sklearn.model_selection import cross_val_score #score evaluation\n",
    "from sklearn.model_selection import cross_val_predict #prediction\n",
    "from sklearn.metrics import confusion_matrix #for confusion matrix\n",
    "\n",
    "\n",
    "all_features = traindf.drop(\"Survived\",axis=1)\n",
    "Targeted_feature = traindf[\"Survived\"]\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(all_features,Targeted_feature,test_size=0.3,random_state=42)\n",
    "X_train.shape,X_test.shape,y_train.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "### LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression # Logistic Regression\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "prediction_lr=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = round(accuracy_score(prediction_lr,y_test)*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Logistic Regression is 82.46\n"
     ]
    }
   ],
   "source": [
    "print('The accuracy of the Logistic Regression is',accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score for Logistic REgression is: 82.39\n"
     ]
    }
   ],
   "source": [
    "### Perform K-Cross Validation\n",
    "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "result_lr=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n",
    "\n",
    "print('The cross validated score for Logistic REgression is:',round(result_lr.mean()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Confusion_matrix')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion Matrix Plot\n",
    "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n",
    "plt.title('Confusion_matrix', y=1.05, size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model = RandomForestClassifier(criterion='gini', n_estimators=700,\n",
    "                             min_samples_split=10,min_samples_leaf=1,\n",
    "                             max_features='auto',oob_score=True,\n",
    "                             random_state=1,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Random Forest Classifier is 82.46\n"
     ]
    }
   ],
   "source": [
    "model.fit(X_train,y_train)\n",
    "prediction_rm=model.predict(X_test)\n",
    "accuracy = round(accuracy_score(prediction_rm,y_test)*100,2)\n",
    "print('The accuracy of the Random Forest Classifier is',accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score for Random Forest Classifier is: 83.95\n"
     ]
    }
   ],
   "source": [
    "# K-Fold Cross Validaion\n",
    "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "result_rm=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for Random Forest Classifier is:',round(result_rm.mean()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Confusion_matrix')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix Plot\n",
    "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n",
    "plt.title('Confusion_matrix', y=1.05, size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC, LinearSVC\n",
    "\n",
    "model = SVC()\n",
    "model.fit(X_train,y_train)\n",
    "prediction_svm=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Support Vector Machines Classifier is 83.58\n"
     ]
    }
   ],
   "source": [
    "print('The accuracy of the Support Vector Machines Classifier is',round(accuracy_score(prediction_svm,y_test)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score for Support Vector Machines Classifier is: 83.16\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "result_svm=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for Support Vector Machines Classifier is:',round(result_svm.mean()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Confusion_matrix')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n",
    "plt.title('Confusion_matrix', y=1.05, size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "### KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "\n",
    "model = KNeighborsClassifier(n_neighbors = 4)\n",
    "model.fit(X_train,y_train)\n",
    "prediction_knn=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the K Nearst Neighbors Classifier is 81.34\n"
     ]
    }
   ],
   "source": [
    "print('The accuracy of the K Nearst Neighbors Classifier is',round(accuracy_score(prediction_knn,y_test)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score for K Nearest Neighbors Classifier is: 81.48\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "result_knn=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for K Nearest Neighbors Classifier is:',round(result_knn.mean()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Confusion_matrix')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n",
    "plt.title('Confusion_matrix', y=1.05, size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "model= GaussianNB()\n",
    "model.fit(X_train,y_train)\n",
    "prediction_gnb=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Gaussian Naive Bayes Classifier is 79.48\n"
     ]
    }
   ],
   "source": [
    "print('The accuracy of the Gaussian Naive Bayes Classifier is',round(accuracy_score(prediction_gnb,y_test)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score for Gaussian Naive Bayes classifier is: 79.91\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "result_gnb=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for Gaussian Naive Bayes classifier is:',round(result_gnb.mean()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Confusion_matrix')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n",
    "plt.title('Confusion_matrix', y=1.05, size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model= DecisionTreeClassifier(criterion='gini', \n",
    "                             min_samples_split=10,min_samples_leaf=1,\n",
    "                             max_features='auto')\n",
    "model.fit(X_train,y_train)\n",
    "prediction_tree=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the DecisionTree Classifier is 78.36\n"
     ]
    }
   ],
   "source": [
    "print('The accuracy of the DecisionTree Classifier is',round(accuracy_score(prediction_tree,y_test)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score for Decision Tree classifier is: 81.26\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "result_tree=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for Decision Tree classifier is:',round(result_tree.mean()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Confusion_matrix')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n",
    "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n",
    "plt.title('Confusion_matrix', y=1.05, size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "model= AdaBoostClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "prediction_adb=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the AdaBoostClassifier is 80.22\n"
     ]
    }
   ],
   "source": [
    "print('The accuracy of the AdaBoostClassifier is',round(accuracy_score(prediction_adb,y_test)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score for AdaBoostClassifier is: 80.92\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "result_adb=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for AdaBoostClassifier is:',round(result_adb.mean()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Confusion_matrix')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n",
    "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n",
    "plt.title('Confusion_matrix', y=1.05, size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "### LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "model= LinearDiscriminantAnalysis()\n",
    "model.fit(X_train,y_train)\n",
    "prediction_lda=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the LinearDiscriminantAnalysis is 82.84\n"
     ]
    }
   ],
   "source": [
    "print('The accuracy of the LinearDiscriminantAnalysis is',round(accuracy_score(prediction_lda,y_test)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score for AdaBoostClassifier is: 82.61\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "result_lda=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for AdaBoostClassifier is:',round(result_lda.mean()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Confusion_matrix')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n",
    "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n",
    "plt.title('Confusion_matrix', y=1.05, size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "model= GradientBoostingClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "prediction_gbc=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Gradient Boosting Classifier is 82.46\n"
     ]
    }
   ],
   "source": [
    "print('The accuracy of the Gradient Boosting Classifier is',round(accuracy_score(prediction_gbc,y_test)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validated score for AdaBoostClassifier is: 82.39\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts\n",
    "result_gbc=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy')\n",
    "print('The cross validated score for AdaBoostClassifier is:',round(result_gbc.mean()*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Confusion_matrix')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)\n",
    "sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=\"summer\")\n",
    "plt.title('Confusion_matrix', y=1.05, size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', 'AdaBoostClassifier', \n",
    "              'Gradient Decent', 'Linear Discriminant Analysis', \n",
    "              'Decision Tree'],\n",
    "    'Score': [result_svm.mean(), result_knn.mean(), result_lr.mean(), \n",
    "              result_rm.mean(), result_gnb.mean(), result_adb.mean(), \n",
    "              result_gbc.mean(), result_lda.mean(), result_tree.mean()]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.839535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>0.831644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Linear Discriminant Analysis</td>\n",
       "      <td>0.826064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.823880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gradient Decent</td>\n",
       "      <td>0.823867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.814840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.812630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.809209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.799096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Model     Score\n",
       "3                 Random Forest  0.839535\n",
       "0       Support Vector Machines  0.831644\n",
       "7  Linear Discriminant Analysis  0.826064\n",
       "2           Logistic Regression  0.823880\n",
       "6               Gradient Decent  0.823867\n",
       "1                           KNN  0.814840\n",
       "8                 Decision Tree  0.812630\n",
       "5            AdaBoostClassifier  0.809209\n",
       "4                   Naive Bayes  0.799096"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models.sort_values(by='Score',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest is performing good here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hyper-Parameters Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 22), (891,), (418, 22))"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X = traindf.drop(\"Survived\", axis=1)\n",
    "train_Y=traindf[\"Survived\"]\n",
    "test_X  = testdf.drop(\"PassengerId\", axis=1).copy()\n",
    "train_X.shape, train_Y.shape, test_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>...</th>\n",
       "      <th>Age_type_Teenage</th>\n",
       "      <th>Age_type_Adult</th>\n",
       "      <th>Age_type_Elder</th>\n",
       "      <th>Em_type_C</th>\n",
       "      <th>Em_type_Q</th>\n",
       "      <th>Em_type_S</th>\n",
       "      <th>Fare_type_Low_fare</th>\n",
       "      <th>Fare_type_median_fare</th>\n",
       "      <th>Fare_type_Average_fare</th>\n",
       "      <th>Fare_type_high_fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  SibSp  Parch  FamilySize  Sex_female  Sex_male  Title_Master  \\\n",
       "0       3      1      0           2           0         1             0   \n",
       "1       1      1      0           2           1         0             0   \n",
       "2       3      0      0           1           1         0             0   \n",
       "3       1      1      0           2           1         0             0   \n",
       "4       3      0      0           1           0         1             0   \n",
       "\n",
       "   Title_Miss  Title_Mr  Title_Mrs         ...           Age_type_Teenage  \\\n",
       "0           0         1          0         ...                          0   \n",
       "1           0         0          1         ...                          0   \n",
       "2           1         0          0         ...                          0   \n",
       "3           0         0          1         ...                          0   \n",
       "4           0         1          0         ...                          0   \n",
       "\n",
       "   Age_type_Adult  Age_type_Elder  Em_type_C  Em_type_Q  Em_type_S  \\\n",
       "0               1               0          0          0          1   \n",
       "1               1               0          1          0          0   \n",
       "2               1               0          0          0          1   \n",
       "3               1               0          0          0          1   \n",
       "4               1               0          0          0          1   \n",
       "\n",
       "   Fare_type_Low_fare  Fare_type_median_fare  Fare_type_Average_fare  \\\n",
       "0                   1                      0                       0   \n",
       "1                   0                      0                       0   \n",
       "2                   0                      1                       0   \n",
       "3                   0                      0                       0   \n",
       "4                   0                      1                       0   \n",
       "\n",
       "   Fare_type_high_fare  \n",
       "0                    0  \n",
       "1                    1  \n",
       "2                    0  \n",
       "3                    1  \n",
       "4                    0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gradient boosting tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "model = GradientBoostingClassifier()\n",
    "param_grid = {'loss' : [\"deviance\"],\n",
    "              'n_estimators' : [100,200,300,400],\n",
    "              'learning_rate': [0.1, 0.05, 0.01,0.001],\n",
    "              'max_depth': [4, 8],\n",
    "              'min_samples_leaf': [100,150],\n",
    "              'max_features': [0.3, 0.2,0.1] \n",
    "              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelf = GridSearchCV(model,param_grid = param_grid, cv=kfold, scoring=\"accuracy\", n_jobs= 4, verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 192 candidates, totalling 1920 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:    3.3s\n",
      "[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed:   11.5s\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed:   25.3s\n",
      "[Parallel(n_jobs=4)]: Done 792 tasks      | elapsed:   44.6s\n",
      "[Parallel(n_jobs=4)]: Done 1242 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=4)]: Done 1792 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=4)]: Done 1920 out of 1920 | elapsed:  1.8min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=10, random_state=22, shuffle=False),\n",
       "       error_score='raise-deprecating',\n",
       "       estimator=GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_sampl...      subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=4,\n",
       "       param_grid={'loss': ['deviance'], 'n_estimators': [100, 200, 300, 400], 'learning_rate': [0.1, 0.05, 0.01, 0.001], 'max_depth': [4, 8], 'min_samples_leaf': [100, 150], 'max_features': [0.3, 0.2, 0.1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelf.fit(train_X,train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8204264870931538"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best score\n",
    "modelf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=4,\n",
       "              max_features=0.3, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=100, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=400,\n",
       "              n_iter_no_change=None, presort='auto', random_state=None,\n",
       "              subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
       "              verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best Estimator\n",
    "modelf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest Classifier Parameters tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier()\n",
    "n_estim=range(100,1000,100)\n",
    "\n",
    "## Search grid for optimal parameters\n",
    "param_grid = {\"n_estimators\" :n_estim}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  45 out of  45 | elapsed:   13.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=4,\n",
       "       param_grid={'n_estimators': range(100, 1000, 100)},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf = GridSearchCV(model,param_grid = param_grid, cv=5, scoring=\"accuracy\", n_jobs= 4, verbose = 1)\n",
    "\n",
    "model_rf.fit(train_X,train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8215488215488216\n"
     ]
    }
   ],
   "source": [
    "# Best score\n",
    "print(model_rf.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#best estimator\n",
    "model_rf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8215488215488216\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  20 out of  20 | elapsed:    0.1s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,\n",
       "              solver='svd', store_covariance=False, tol=0.001)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "model =LinearDiscriminantAnalysis()\n",
    "param_grid = {'tol':[0.001,0.01,.1,.2]}\n",
    "\n",
    "modell = GridSearchCV(model,param_grid = param_grid, cv=5, scoring=\"accuracy\", n_jobs= 4, verbose = 1)\n",
    "\n",
    "modell.fit(train_X,train_Y)\n",
    "\n",
    "# Best score\n",
    "print(modell.best_score_)\n",
    "\n",
    "# Best Estimator\n",
    "modell.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 56 candidates, totalling 280 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 128 tasks      | elapsed:    4.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=50, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.01, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "0.8338945005611672\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 280 out of 280 | elapsed:   56.1s finished\n"
     ]
    }
   ],
   "source": [
    "model= SVC()\n",
    "param_grid = {'kernel': ['rbf','linear'], \n",
    "                  'gamma': [ 0.001, 0.01, 0.1, 1],\n",
    "                  'C': [1, 10, 50, 100,200,300, 1000]}\n",
    "\n",
    "modelsvm = GridSearchCV(model,param_grid = param_grid, cv=5, scoring=\"accuracy\", n_jobs= 4, verbose = 1)\n",
    "\n",
    "modelsvm.fit(train_X,train_Y)\n",
    "\n",
    "print(modelsvm.best_estimator_)\n",
    "\n",
    "# Best score\n",
    "print(modelsvm.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Apply the Estimator which got from parameter tuning of Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "random_forest = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=1, min_samples_split=2,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=400, n_jobs=1,\n",
    "            oob_score=False, random_state=None, verbose=0,\n",
    "            warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest.fit(train_X, train_Y)\n",
    "Y_pred_rf = random_forest.predict(test_X)\n",
    "random_forest.score(train_X,train_Y)\n",
    "acc_random_forest = round(random_forest.score(train_X, train_Y) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Important features\n",
      "____________________________________________________________\n",
      "89.11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Important features\")\n",
    "pd.Series(random_forest.feature_importances_,train_X.columns).sort_values(ascending=True).plot.barh(width=0.8)\n",
    "print('__'*30)\n",
    "print(acc_random_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_df[\"PassengerId\"],\n",
    "        \"Survived\": Y_pred_rf})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"submission1.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}