Logistic Regression - Titanic Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Necessary Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 12)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading Data\n",
    "data = pd.read_csv('train.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of passengers survived : 891\n"
     ]
    }
   ],
   "source": [
    "print(\"No. of passengers survived : \" + str(len(data.index)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24b3a91b648>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Survived' , data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24b3c18fb08>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Survived', hue = 'Sex' , data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24b39174e48>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Survived', hue = 'Pclass', data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24b3c28b788>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Age'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('Cabin', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \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",
       "\n",
       "   Parch     Ticket     Fare Embarked  \n",
       "0      0  A/5 21171   7.2500        S  \n",
       "1      0   PC 17599  71.2833        C  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   male\n",
       "0     1\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Converting String Data into Caegorical Data\n",
    "sex = pd.get_dummies(data['Sex'], drop_first = True)\n",
    "sex.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Q  S\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  1\n",
       "4  0  1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Embark = pd.get_dummies(data['Embarked'], drop_first = True)\n",
    "Embark.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "   2  3\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  0\n",
       "4  0  1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pcl = pd.get_dummies(data['Pclass'], drop_first = True)\n",
    "Pcl.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "      <th>SibSp</th>\n",
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       "    <tr>\n",
       "      <td>1</td>\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>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>S</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\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>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\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>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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 Embarked  male  Q  S  2  3  \n",
       "0      0         A/5 21171   7.2500        S     1  0  1  0  1  \n",
       "1      0          PC 17599  71.2833        C     0  0  0  0  0  \n",
       "2      0  STON/O2. 3101282   7.9250        S     0  0  1  0  1  \n",
       "3      0            113803  53.1000        S     0  0  1  0  0  \n",
       "4      0            373450   8.0500        S     1  0  1  0  1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat([data,sex,Embark,Pcl], axis = 1)\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age  SibSp  Parch     Fare  male  Q  S  2  3\n",
       "0         0  22.0      1      0   7.2500     1  0  1  0  1\n",
       "1         1  38.0      1      0  71.2833     0  0  0  0  0\n",
       "2         1  26.0      0      0   7.9250     0  0  1  0  1\n",
       "3         1  35.0      1      0  53.1000     0  0  1  0  0\n",
       "4         0  35.0      0      0   8.0500     1  0  1  0  1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['Sex', 'Embarked', 'PassengerId', 'Pclass', 'Name', 'Ticket'], axis = 1, inplace = True)\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop('Survived', axis = 1)\n",
    "y = data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logmodel = LogisticRegression(solver='liblinear')\n",
    "logmodel.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.81      0.83      0.82       126\\n           1       0.75      0.72      0.73        88\\n\\n    accuracy                           0.79       214\\n   macro avg       0.78      0.77      0.78       214\\nweighted avg       0.78      0.79      0.78       214\\n'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "classification_report(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[105,  21],\n",
       "       [ 25,  63]], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7850467289719626"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, predictions)"
   ]
  }
 ],
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