Logistic Regression- Practical.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.el2c6ple4zyw3eceviv3oxxgrn2nrfm2.gfortran-win_amd64.dll\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dll\n",
      "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
     ]
    }
   ],
   "source": [
    "### Import the required packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('titanic-training-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "      <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": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyse the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "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=df)"
   ]
  },
  {
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>233</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Survived              \n",
       "0             81   468\n",
       "1            233   109"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"Survived\"],df[\"Sex\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "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=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "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=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"SibSp\",data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Wrangling"
   ]
  },
  {
   "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            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),yticklabels=False,cmap=\"Greens\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 15,
     "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.boxplot(x=\"Pclass\",y=\"Age\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"Cabin\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
       "      <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>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>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>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>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>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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "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.heatmap(df.isnull(),yticklabels=False,cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>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>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>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>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>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>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": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(712, 11)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   male\n",
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       "4     1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
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   "source": [
    "Sex=pd.get_dummies(df[\"Sex\"],drop_first=True)\n",
    "Sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   C  Q  S\n",
       "0  0  0  1\n",
       "1  1  0  0\n",
       "2  0  0  1\n",
       "3  0  0  1\n",
       "4  0  0  1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark=pd.get_dummies(df[\"Embarked\"])\n",
    "embark.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark=pd.get_dummies(df[\"Embarked\"],drop_first=True)\n",
    "embark.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   2  3\n",
       "0  0  1\n",
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       "2  0  1\n",
       "3  0  0\n",
       "4  0  1"
      ]
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcl=pd.get_dummies(df[\"Pclass\"],drop_first=True)\n",
    "pcl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <td>S</td>\n",
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       "      <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",
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       "      <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>S</td>\n",
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       "      <td>0</td>\n",
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       "      <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>S</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",
       "      <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>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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.concat([df,Sex,embark,pcl],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>male</th>\n",
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       "      <th>0</th>\n",
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       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age  SibSp  Parch  male  Q  S  2  3\n",
       "0         0  22.0      1      0     1  0  1  0  1\n",
       "1         1  38.0      1      0     0  0  0  0  0\n",
       "2         1  26.0      0      0     0  0  1  0  1\n",
       "3         1  35.0      1      0     0  0  1  0  0\n",
       "4         0  35.0      0      0     1  0  1  0  1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([\"Sex\",\"Embarked\",\"PassengerId\",\"Name\",\"Ticket\",\"Pclass\",\"Fare\"],axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(\"Survived\",axis=1)\n",
    "y=df[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.794392523364486"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.87      0.83       122\n",
      "           1       0.80      0.70      0.74        92\n",
      "\n",
      "    accuracy                           0.79       214\n",
      "   macro avg       0.80      0.78      0.79       214\n",
      "weighted avg       0.79      0.79      0.79       214\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test, predictions))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[106,  16],\n",
       "       [ 28,  64]], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cm=metrics.confusion_matrix(y_test, predictions, labels=[1, 0])\n",
    "\n",
    "df_cm = pd.DataFrame(cm, index = [i for i in [\"1\",\"0\"]],\n",
    "                  columns = [i for i in [\"Predict 1\",\"Predict 0\"]])\n",
    "plt.figure(figsize = (7,5))\n",
    "sns.heatmap(df_cm, annot=True,fmt='g')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}