Titanic 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Titanic Survivor Prediction\n",
    "\n",
    "**Import Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import Train and Test Data Sets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')\n",
    "\n",
    "test = pd.read_csv('titanic_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Features PassengerId and Name are of No use for Prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['PassengerId','Name'], axis = 'columns', inplace = True)\n",
    "\n",
    "test.drop(['PassengerId','Name'], axis = 'columns', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <td>22.0</td>\n",
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       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>3</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": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0         0       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1         1       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2         1       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3         1       1  female  35.0      1      0            113803  53.1000   \n",
       "4         0       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "  Cabin Embarked  \n",
       "0   NaN        S  \n",
       "1   C85        C  \n",
       "2   NaN        S  \n",
       "3  C123        S  \n",
       "4   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <td>34.5</td>\n",
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       "      <td>0</td>\n",
       "      <td>330911</td>\n",
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       "      <td>Q</td>\n",
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       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
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       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</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>3</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": [
       "   Pclass     Sex   Age  SibSp  Parch   Ticket     Fare Cabin Embarked\n",
       "0       3    male  34.5      0      0   330911   7.8292   NaN        Q\n",
       "1       3  female  47.0      1      0   363272   7.0000   NaN        S\n",
       "2       2    male  62.0      0      0   240276   9.6875   NaN        Q\n",
       "3       3    male  27.0      0      0   315154   8.6625   NaN        S\n",
       "4       3  female  22.0      1      1  3101298  12.2875   NaN        S"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Information of Data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 10 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Sex       891 non-null    object \n",
      " 3   Age       714 non-null    float64\n",
      " 4   SibSp     891 non-null    int64  \n",
      " 5   Parch     891 non-null    int64  \n",
      " 6   Ticket    891 non-null    object \n",
      " 7   Fare      891 non-null    float64\n",
      " 8   Cabin     204 non-null    object \n",
      " 9   Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(4)\n",
      "memory usage: 69.7+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(train.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Survived is a Target Feature ( 0 - Not Survived, 1 - Survived )**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    549\n",
      "1    342\n",
      "Name: Survived, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train['Survived'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis\n",
    "\n",
    "**Visualizing Data for Better Understanding**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Sex', hue = 'Survived', data = train)\n",
    "plt.ylabel('Frequency Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gender plays an Important Role here, Womens and Kids were provided with the First Priority at the Time of Rescue Operation.**\n",
    "\n",
    "**Survival Rate on the basis of Passengers Class.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass  Survived\n",
      "1       0            80\n",
      "        1           136\n",
      "2       0            97\n",
      "        1            87\n",
      "3       0           372\n",
      "        1           119\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train.groupby(['Pclass', 'Survived']).size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived    0    1\n",
      "Pclass            \n",
      "1          80  136\n",
      "2          97   87\n",
      "3         372  119\n"
     ]
    }
   ],
   "source": [
    "print(train.groupby(['Pclass', 'Survived']).size().unstack())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pclass_survived = train.groupby(['Pclass', 'Survived']).size().unstack()\n",
    "\n",
    "sns.heatmap(pclass_survived, annot = True, fmt = 'd');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Survival Rate of Passengers of Class 1 is More as Compared to Second and Third Class.**\n",
    "\n",
    "**First Class Passengers where on the Top Floor of the Ship they got the Life Boats easily.**\n",
    "\n",
    "***\n",
    "\n",
    "### Factor Plot\n",
    "\n",
    "**Adding New Feature for Passengers with Family and Single Passenger.**\n",
    "\n",
    "**Family Feature consist of Total Number of Members in the Family of Passengers.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Family'] = train['Parch'] + train['SibSp']  # Parents - Children + Siblings - Spouses\n",
    "\n",
    "train['Single'] = np.where(train['Family'] == 0, 1,0)"
   ]
  },
  {
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</th>\n",
       "      <th>Single</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>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0         0       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1         1       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2         1       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3         1       1  female  35.0      1      0            113803  53.1000   \n",
       "4         0       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "  Cabin Embarked  Family  Single  \n",
       "0   NaN        S       1       0  \n",
       "1   C85        C       1       0  \n",
       "2   NaN        S       0       1  \n",
       "3  C123        S       1       0  \n",
       "4   NaN        S       0       1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Categorical Plot for Passengers with Family**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Family', y = 'Survived', data = train, kind = 'point');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Categorical Plot for Single Passenger**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Single', y = 'Survived', data = train, kind = 'point');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Survival Rate of Single Passengers was less.**\n",
    "\n",
    "***\n",
    "\n",
    "**Survival Rate based on Fare Category**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Fare Range'] = pd.qcut(train['Fare'], 4) # Quantile Cut in 4 Bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Barplot Representing the Survival Rate on the Basis of Fare Range.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = 'Fare Range', y = 'Survived', data = train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passengers who Paid High Fares where on the Top of the Ship, so there Survival Rate is More than others.**\n",
    "\n",
    "***\n",
    "\n",
    "**Age Distribution of Passengers across Ship**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.figure(figsize = (8,6))\n",
    "sns.distplot(train['Age'].dropna(), kde = False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Passenger were more between 15 and 40, Babies were also More.**\n",
    "\n",
    "***\n",
    "\n",
    "**Survival Rate based on Embarkation Ports and Passengers Class.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AbD8lDgAAAEABKHEAAAAACqDLrokDAADQk7mmCvBOWYkDAAAAUABKHAAAAIACUOIAAAAAFIASBwAAAKAAlDgAAAAABaDEAQAAACgAJQ4AAABAAShxAAAAAApAiQMAAABQAEocAAAAgAJQ4gAAAAAUgBIHAAAAoACUOAAAAAAFUFftAAAAAEDHe+7yD1Y7wnYZOvXxakfYYSlx2KGMOn9etSO02+K+1U4AAABAT6DEAQAA4C0VcSWHVRx0Z66JAwAAAFAAShwAAACAAlDiAAAAABSAEgcAAACgAJQ4AAAAAAWgxAEAAAAoACUOAAAAQAEocQAAAAAKQIkDAAAAUABKHAAAAIACUOIAAAAAFIASBwAAAKAAlDgAAAAABaDEAQAAACgAJQ4AAABAAdRVOwAAQHs8d/kHqx2h3YZOfbzaEQCAbkCJAwA91Kjz51U7wnZZ3LfaCQAAqsPpVAAAAAAFoMQBAAAAKAAlDgAAAEABKHEAAAAACkCJAwAAAFAAVb87VblczrRp0/LEE0+kd+/eufLKK7PnnntWOxYAAJ3ALeIBYPtVvcT5wQ9+kM2bN+e73/1uli9fnhkzZmTu3LnVjgVspyLesnhx35nVjtBu/kIBJEWdudVOAADFVapUKpVqBpg+fXpGjBiRo48+Okly6KGH5ic/+Unr15ubm6sVDaBbGTVq1F/dxswF6BhmLkDXacvM7S6qvhJn48aNaWhoaH1cW1ubLVu2pK7uj9F60ocBUG1mLkDXMXMBaK+qX9i4oaEhLS0trY/L5XJrgQMAAADAH1W9xBk5cmTuv//+JMny5cuz3377VTkRAAAAwI6n6tfE+dPdqX7+85+nUqnk6quvzj777FPNSAAAAAA7nKqXOD3ZTTfdlAcffDA1NTUplUo599xzc8ABB1Q7Fm305JNPZubMmXn11Vfzyiuv5KMf/WgmTZqUUqlU7Wi00YoVKzJr1qy8+uqrqVQqGTNmTM4888z07t272tHoBGZusZm5xWfm9ixmbvGZu8Vm5nZjFariySefrDQ2NlbK5XKlUqlUVq1aVTnmmGOqnIq2+sMf/lD51Kc+VXn66acrlUqlsmXLlsqZZ55Zue2226objDZbu3Zt5eGxNeYAAAXxSURBVKijjqo89dRTlUqlUimXy5U5c+ZUpk2bVuVkdAYzt9jM3OIzc3sWM7f4zN1iM3O7NytxqmTdunX5zGc+k0mTJuWwww7LHnvskc2bN2tGC2Lx4sVZuXJlLrnkktbnWlpa0qtXL59hQdx4443p3bt3Jk6c2PpcpVLJxz/+8fzXf/1Xdtpppyqmo6OZucVm5hafmduzmLnFZ+4Wm5nbvVX9wsY91YABAzJ37tz89Kc/TWNjY4466qj86Ec/qnYs2ui3v/1thgwZss1z9fX1fqgVyJo1a970GZZKpQwcODAvvvhilVLRWczcYjNzi8/M7VnM3OIzd4vNzO3e3Mu7Sp599tk0NDRk+vTpSZLHH388p59+esaMGZP+/ftXOR1/zaBBg7Jq1aptnnv++efz61//Oh/60IeqlIr2GDRoUJ5//vltniuXy1mzZk122223KqWis5i5xWbmFp+Z27OYucVn7habmdu9WYlTJU888USmTZuWTZs2JUmGDRuWvn37pra2tsrJaIvDDz88P/nJT/Lcc88lSV5//fXMmDEjP//5z6ucjLY69thjs3DhwjzzzDNZv359Jk6cmIsvvjiHH354dtlll2rHo4OZucVm5hafmduzmLnFZ+4Wm5nbvbkmThXNnTs399xzT3bZZZdUKpWcdtppOeKII6odizZasWJFvva1r6VSqaSlpSWHH354zjrrLFfsL5AVK1Zk9uzZaWlpyWuvvZaBAwdm4MCBufDCC/1LYTdk5habmVt8Zm7PYuYWn7lbbGZu96XEAfgzP/vZzzJkyJDU19dXOwpAt2fmAnQdM7d7UOIAAAAAFIBr4gAAAAAUgBIHAAAAoACUOAAAAAAFoMQBAAAAKAAlDj3G0qVLc8ghh6Spqan1v7PPPvuvvu7OO+/MNddcs13H/NjHPpZNmza16zWbNm3Kxz72se06HsCOwswF6FrmLvQMddUOAF3pwx/+cGbPnl3tGAA9gpkL0LXMXej+lDj0eE1NTXn/+9+fJ598MrvssktGjx6dJUuWZP369fn2t7+dJFm+fHm+8IUvZOPGjZk0aVL+3//7f/ne976XW2+9tXU/1157bZ588slcc8016dWrV4477rjWr91+++154IEHMmvWrCxfvjyzZ89ObW1thgwZkssvvzybN2/Oeeedl/Xr12fo0KFd/j0A6CpmLkDXMnehe1Hi0KM8/PDDaWpqan380Y9+NEkyYsSIXHLJJTn11FOz00475V/+5V9ywQUX5JFHHkmS7Lzzzrnpppuybt26TJgwIYcddlieeeaZ3HTTTdl5550zderULFmyJHvssUc2bdqUhQsXJkmuu+66zJ8/P6tXr861116bmpqaTJkyJbfddlt22223fOMb38jixYuzefPm7Lfffjn33HPz6KOPZunSpV3/zQHoYGYuQNcyd6H7U+LQo7zVEtMf//jH2X///ZMk/fr1y7777tv66z+d4ztq1KiUSqXstttu6du3b15++eXstttuueCCC1JfX5+nnnoqBx10UJJk2LBh2+z/oYceSm1tbWpra/P73/8+v/3tb3POOeckSV577bV85CMfyUsvvZRDDz00SXLggQemrs5vTaD4zFyArmXuQvfnwsbQBo8//niS5MUXX8wrr7ySXr165brrrsvs2bNz5ZVXpk+fPqlUKkmSmpptf1vdcMMN6devX26//fa8613vynve857ccMMNmT9/fs4444yMGTMme++9d5YvX54kWbVqVbZs2dK1bxBgB2LmAnQtcxeKQwVKj/J/l5gmf/wXgr/mtddey0knnZRXXnkll19+eRoaGjJy5Mh89rOfzS677JJ+/frlt7/9bQYPHvyWr7/kkksyYcKEHHLIIbn44otz+umnp1KppL6+Pl/72tfyoQ99KBdddFFOOOGE7L333unVq1eHvF+AajJzAbqWuQvdX6nyp0oVAAAAgB2W06kAAAAACkCJAwAAAFAAShwAAACAAlDiAAAAABSAEgcAAACgAJQ4AAAAAAWgxAEAAAAogP8P4jnnKUb2gMAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1127x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = 'Embarked', hue = 'Survived', kind = 'count', col = 'Pclass', data = train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Majority of Passengers boarded Ship from Southampton Port.**\n",
    "\n",
    "**Majority of Passengers boarded Ship at Southampton Port were of Class 3.**\n",
    "\n",
    "***\n",
    "\n",
    "### Data Preprocessing\n",
    "\n",
    "**Dropping the Columns Created for Exploratory Data Analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</th>\n",
       "      <th>Single</th>\n",
       "      <th>Fare Range</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>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(-0.001, 7.91]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0         0       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1         1       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2         1       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3         1       1  female  35.0      1      0            113803  53.1000   \n",
       "4         0       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "  Cabin Embarked  Family  Single       Fare Range  \n",
       "0   NaN        S       1       0   (-0.001, 7.91]  \n",
       "1   C85        C       1       0  (31.0, 512.329]  \n",
       "2   NaN        S       0       1   (7.91, 14.454]  \n",
       "3  C123        S       1       0  (31.0, 512.329]  \n",
       "4   NaN        S       0       1   (7.91, 14.454]  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</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>0</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>3</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": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch            Ticket     Fare  \\\n",
       "0         0       3    male  22.0      1      0         A/5 21171   7.2500   \n",
       "1         1       1  female  38.0      1      0          PC 17599  71.2833   \n",
       "2         1       3  female  26.0      0      0  STON/O2. 3101282   7.9250   \n",
       "3         1       1  female  35.0      1      0            113803  53.1000   \n",
       "4         0       3    male  35.0      0      0            373450   8.0500   \n",
       "\n",
       "  Cabin Embarked  \n",
       "0   NaN        S  \n",
       "1   C85        C  \n",
       "2   NaN        S  \n",
       "3  C123        S  \n",
       "4   NaN        S  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(['Family', 'Single', 'Fare Range'], axis = 'columns', inplace = True)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Target Feature : Survived**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = train['Survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Independent Features (Train) : Dropping Target Feature.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Survived', axis = 'columns', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Adding a Columns for Later Splitting the Train and Test Data Sets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Data'] = 'train'\n",
    "\n",
    "test['Data'] = 'test'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking Columns of Train and Test Data Set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Set : Index(['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin',\n",
      "       'Embarked', 'Data'],\n",
      "      dtype='object')\n",
      "\n",
      "Test Data Set : Index(['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin',\n",
      "       'Embarked', 'Data'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(f'Train Data Set : {train.columns}')\n",
    "\n",
    "print()\n",
    "\n",
    "print(f'Test Data Set : {test.columns}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Concatenating Train and Test Data Sets for Data Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data Set : (891, 10) \n",
      "Test Data Set : (418, 10) \n",
      "Concatenated Data Set : (1309, 10)\n"
     ]
    }
   ],
   "source": [
    "titanic = pd.concat([train, test], axis = 'rows')\n",
    "\n",
    "print(f'Train Data Set : {train.shape} \\nTest Data Set : {test.shape} \\nConcatenated Data Set : {titanic.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dropping the Unnecessary Features.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pclass     Sex   Age  SibSp  Parch Embarked   Data\n",
      "0       3    male  22.0      1      0        S  train\n",
      "1       1  female  38.0      1      0        C  train\n",
      "2       3  female  26.0      0      0        S  train\n",
      "3       1  female  35.0      1      0        S  train\n",
      "4       3    male  35.0      0      0        S  train\n"
     ]
    }
   ],
   "source": [
    "titanic.drop(['Ticket','Fare', 'Cabin'], axis = 'columns', inplace = True)\n",
    "\n",
    "print(titanic.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Pclass    1309 non-null   int64  \n",
      " 1   Sex       1309 non-null   object \n",
      " 2   Age       1046 non-null   float64\n",
      " 3   SibSp     1309 non-null   int64  \n",
      " 4   Parch     1309 non-null   int64  \n",
      " 5   Embarked  1307 non-null   object \n",
      " 6   Data      1309 non-null   object \n",
      "dtypes: float64(1), int64(3), object(3)\n",
      "memory usage: 81.8+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(titanic.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Converting Sex Feature from Categorical to Numerical.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pclass  Sex   Age  SibSp  Parch Embarked   Data\n",
      "0       3    1  22.0      1      0        S  train\n",
      "1       1    0  38.0      1      0        C  train\n",
      "2       3    0  26.0      0      0        S  train\n",
      "3       1    0  35.0      1      0        S  train\n",
      "4       3    1  35.0      0      0        S  train\n"
     ]
    }
   ],
   "source": [
    "titanic['Sex'] = np.where(titanic['Sex'] == 'male', 1, 0)\n",
    "\n",
    "print(titanic.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking Missing Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass        0\n",
      "Sex           0\n",
      "Age         263\n",
      "SibSp         0\n",
      "Parch         0\n",
      "Embarked      2\n",
      "Data          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(titanic.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filling / Imputing Missing Values using Scikit Learn Simple Imputer**\n",
    "\n",
    "**Import Library**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Imputing Missing Data in Age with the Mean of Age Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean')\n",
    "\n",
    "imputer.fit(titanic[['Age']])\n",
    "\n",
    "titanic[['Age']] = imputer.transform(titanic[['Age']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Imputing Missing Categorical Data with the Most Frequent Data in Embarked**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = SimpleImputer(missing_values = np.nan, strategy = 'most_frequent')\n",
    "\n",
    "imputer.fit(titanic[['Embarked']])\n",
    "\n",
    "titanic[['Embarked']] = imputer.transform(titanic[['Embarked']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Converting Age to Integer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic['Age'] = titanic['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Confirming for Any Missing Data remaining.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pclass      0\n",
      "Sex         0\n",
      "Age         0\n",
      "SibSp       0\n",
      "Parch       0\n",
      "Embarked    0\n",
      "Data        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(titanic.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**No Missing Data is Remaining.**\n",
    "\n",
    "***\n",
    "\n",
    "**Confirming for Numeric Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Pclass    1309 non-null   int64 \n",
      " 1   Sex       1309 non-null   int32 \n",
      " 2   Age       1309 non-null   int32 \n",
      " 3   SibSp     1309 non-null   int64 \n",
      " 4   Parch     1309 non-null   int64 \n",
      " 5   Embarked  1309 non-null   object\n",
      " 6   Data      1309 non-null   object\n",
      "dtypes: int32(2), int64(3), object(2)\n",
      "memory usage: 71.6+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(titanic.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Embarked Feature is Categorical.**\n",
    "\n",
    "**Label Encoding the Embarked Feature to convert Categorical Data into Numeric Data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()"
   ]
  },
  {
   "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",
       "    .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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  SibSp  Parch  Embarked   Data\n",
       "0       3    1   22      1      0         2  train\n",
       "1       1    0   38      1      0         0  train\n",
       "2       3    0   26      0      0         2  train\n",
       "3       1    0   35      1      0         2  train\n",
       "4       3    1   35      0      0         2  train"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Embarked'] = label_encoder.fit_transform(titanic['Embarked'])\n",
    "\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Pclass    1309 non-null   int64 \n",
      " 1   Sex       1309 non-null   int32 \n",
      " 2   Age       1309 non-null   int32 \n",
      " 3   SibSp     1309 non-null   int64 \n",
      " 4   Parch     1309 non-null   int64 \n",
      " 5   Embarked  1309 non-null   int32 \n",
      " 6   Data      1309 non-null   object\n",
      "dtypes: int32(3), int64(3), object(1)\n",
      "memory usage: 66.5+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(titanic.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pclass  Sex  Age  SibSp  Parch  Embarked   Data\n",
      "0       3    1   22      1      0         2  train\n",
      "1       1    0   38      1      0         0  train\n",
      "2       3    0   26      0      0         2  train\n",
      "3       1    0   35      1      0         2  train\n",
      "4       3    1   35      0      0         2  train\n",
      "\n",
      "     Pclass  Sex  Age  SibSp  Parch  Embarked  Data\n",
      "413       3    1   29      0      0         2  test\n",
      "414       1    0   39      0      0         0  test\n",
      "415       3    1   38      0      0         2  test\n",
      "416       3    1   29      0      0         2  test\n",
      "417       3    1   29      1      1         0  test\n"
     ]
    }
   ],
   "source": [
    "print(titanic.head())\n",
    "print()\n",
    "print(titanic.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There is no Categorical Feature left and No more Missing Data Left, Now the Data is Comfortable for Machine Learning.**\n",
    "\n",
    "**Splitting the Data into Independent Features and Target Feature (Labels)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = titanic[titanic['Data'] == 'train']\n",
    "\n",
    "test = titanic[titanic['Data'] == 'test']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dropping the Data Features from Train and Test Data Sets.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop('Data', axis = 'columns')\n",
    "test = test.drop('Data', axis = 'columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking Dimensions of Train and Test Data Sets.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Set : (891, 6) \n",
      "Testing Data Set : (418, 6)\n"
     ]
    }
   ],
   "source": [
    "print(f'Training Data Set : {train.shape} \\nTesting Data Set : {test.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applying Feature Scaling on Data Set**\n",
    "\n",
    "**Import Library**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(train)\n",
    "X_test = scaler.fit_transform(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Set after Scaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.82737724  0.73769513 -0.58004441  0.43279337 -0.47367361  0.58595414]\n",
      " [-1.56610693 -1.35557354  0.65011226  0.43279337 -0.47367361 -1.9423032 ]\n",
      " [ 0.82737724 -1.35557354 -0.27250525 -0.4745452  -0.47367361  0.58595414]\n",
      " ...\n",
      " [ 0.82737724 -1.35557354 -0.04185087  0.43279337  2.00893337  0.58595414]\n",
      " [-1.56610693  0.73769513 -0.27250525 -0.4745452  -0.47367361 -1.9423032 ]\n",
      " [ 0.82737724  0.73769513  0.18880351 -0.4745452  -0.47367361 -0.67817453]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Classification Models\n",
    "\n",
    "**Creating Classification Models.**\n",
    "\n",
    "**Model with Best Accuracy will be the Final Machine Learning Model for our Data Set.**\n",
    "\n",
    "### 1. Logistic Regression\n",
    "\n",
    "***\n",
    "\n",
    "**Import Linear Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr_classifier = LogisticRegression(random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fitting the Data to the Logistic Regression Classifier for Training and Finding Patterns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
      "                   random_state=1, solver='lbfgs', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(lr_classifier.fit(X_train, Y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prediction made by Logistic Regression Classifier on Test Data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_prediction = lr_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluation of the Logistic Regression Classifier.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluating Estimator Performance**\n",
    "\n",
    "**Avoids Overfitting**\n",
    "\n",
    "<table><tr><td><img src = \"Grid Search Cross Validation.png\"></td></tr></table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score of Logistic Regression Classifier : 79.57%\n"
     ]
    }
   ],
   "source": [
    "lr_score = cross_val_score(lr_classifier, X_train, Y_train, cv = 10, scoring = 'accuracy').mean()\n",
    "\n",
    "print(f'Cross Validation Score of Logistic Regression Classifier : {lr_score*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. K Nearest Neighbors \n",
    "\n",
    "**Import K Nearest Neighbors Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "KNN_classifier = KNeighborsClassifier(n_neighbors = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fitting the Data to the K Nearest Neighbors Classifier for Training and Finding Patterns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
      "                     weights='uniform')\n"
     ]
    }
   ],
   "source": [
    "print(KNN_classifier.fit(X_train, Y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prediction made by K Nearest Neighbors Classifier on Test Data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_prediction = KNN_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluation of the K Nearest Neighbors Classifier.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score of K Nearest Neighbors Classifier : 79.46%\n"
     ]
    }
   ],
   "source": [
    "KNN_score = cross_val_score(KNN_classifier, X_train, Y_train, cv = 10, scoring = 'accuracy').mean()\n",
    "\n",
    "print(f'Cross Validation Score of K Nearest Neighbors Classifier : {KNN_score*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Decision Tree Classifier\n",
    "\n",
    "**Import Tree Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "DT_classifier = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fitting the Data to the Decision Tree Classifier for Training and Finding Patterns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
      "                       max_depth=None, max_features=None, 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, presort='deprecated',\n",
      "                       random_state=None, splitter='best')\n"
     ]
    }
   ],
   "source": [
    "print(DT_classifier.fit(X_train, Y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predictions made by Decision Tree Classifier on Test Data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_prediction = DT_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluation of the Decision Tree Classifier.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score of Decision Tree Classifier : 78.68%\n"
     ]
    }
   ],
   "source": [
    "DT_score = cross_val_score(DT_classifier, X_train, Y_train, cv = 10, scoring = 'accuracy').mean()\n",
    "\n",
    "print(f'Cross Validation Score of Decision Tree Classifier : {DT_score*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Random Forest Classifier\n",
    "\n",
    "**Import Ensemble Classifier Technique.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "RF_classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fitting the Data to the Model for Training and Finding Patterns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
      "                       criterion='entropy', max_depth=None, max_features='auto',\n",
      "                       max_leaf_nodes=None, max_samples=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
      "                       n_jobs=None, oob_score=False, random_state=1, verbose=0,\n",
      "                       warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(RF_classifier.fit(X_train, Y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prediction made by Random Forest Classifier on Test Data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_prediction = RF_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluation of Random Forest Classifier.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score of Random Forest Classifier : 81.49%\n"
     ]
    }
   ],
   "source": [
    "RF_score = cross_val_score(RF_classifier, X_train, Y_train, cv = 10, scoring = 'accuracy').mean()\n",
    "\n",
    "print(f'Cross Validation Score of Random Forest Classifier : {RF_score*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Support Vector Classifier :\n",
    "\n",
    "**Import SVM Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "SV_classifier = SVC()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fitting the Data to the Support Vector Classifier for Training and Finding Patterns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
      "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "    tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "print(SV_classifier.fit(X_train, Y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Prediction made by Support Vector Classifier on Test Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_prediction = SV_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluation of Support Vector Classifier.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Validation Score of Suppport Vector Classifier : 82.38%\n"
     ]
    }
   ],
   "source": [
    "SVC_score = cross_val_score(SV_classifier, X_train, Y_train, cv = 10, scoring = 'accuracy').mean()\n",
    "\n",
    "print(f'Cross Validation Score of Suppport Vector Classifier : {SVC_score*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a Data Frame Representing Accuracy Score of All Classifiers.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Classifier     Score\n",
      "0  Logistic Regression  0.795743\n",
      "1  K Nearest Neighbors  0.794644\n",
      "2        Decision Tree  0.786841\n",
      "3        Random Forest  0.814869\n",
      "4       Support Vector  0.823820\n"
     ]
    }
   ],
   "source": [
    "Accuracy = pd.DataFrame({'Classifier' : ['Logistic Regression', 'K Nearest Neighbors', 'Decision Tree', 'Random Forest', 'Support Vector'],\n",
    "                         'Score' : [lr_score, KNN_score, DT_score, RF_score, SVC_score]},\n",
    "                        columns = ['Classifier', 'Score'])\n",
    "\n",
    "print(Accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing test Data Set for Selecting PassengerId Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('titanic_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating Data Frame for Submission of Predictions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({'PassengerId' : test['PassengerId'],\n",
    "                           'Survived' : Y_prediction})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    286\n",
      "1    132\n",
      "Name: Survived, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(submission['Survived'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Submission to CSV**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('Titanic_Prediction.csv', index = False)"
   ]
  }
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