Titanic_Try.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data=pd.read_csv('Titanic.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",
       "      <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",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</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",
       "5            6         0       3   \n",
       "6            7         0       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",
       "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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2899141fd48>"
      ]
     },
     "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=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x289917362c8>"
      ]
     },
     "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=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x289917ac708>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fGfErlz0FtQo55vXBMa8Po4w5VYdM20uS1qljafpIkjRjhoIkqVvzobDUrTOSPCrJm9v29yXZMv0qV9aAMV+c5KNJbkpybZLDnrO8Wgy9RUqSFyapJKv+9MUhY07yA+3P+iNJ3jjtGlfagN/tJye5LsmN7ff77FnUuVKSvCHJgSQ3H2Z7klza/nvclOSMZX9pVa3ZHyYHrP8NeCrwSOBfgdMf0ucngde25fOBN8+67imM+dnAY9ryT6yHMbd+jwXeC1wPbJt13VP4c94K3Ag8oa2fPOu6pzDmXcBPtOXTgdtnXfcyx/ztwBnAzYfZfjbwN0yu8zoTeN9yv3Ot7ykMuXXGdmB3W34rcFaSxS6kWy2WHHNVXVdV97bV65lcE7KaDb1Fym8CvwN8fprFjWTImF8C/HFVfRqgqg5MucaVNmTMBTyuLT+eVX6tU1W9F/jUEbpsBy6vieuBE5OcspzvXOuhsNitMzYfrk9V3Q/cDTxxKtWNY8iYF7qQyb80VrMlx5zkGcBpVfWOaRY2oiF/zl8NfHWSf05yfZLnTa26cQwZ88uBH06yD7ga+OnplDYzR/v/+5KOmesURjLk1hmDbq+xigweT5IfBrYB3zFqReM74piTPAJ4NfCj0ypoCob8OW9gMoX0nUz2Bv8xydOr6jMj1zaWIWN+EfCnVfWqJN8CXNHG/MXxy5uJFf/7a63vKQy5dUbvk2QDk13OI+2uHesG3S4kyXOBXwXOqaovTKm2sSw15scCTwfek+R2JnOve1b5weahv9tXVdX/VtW/Ax9nEhKr1ZAxXwhcCVBV/wKcwORmeWvVit8eaK2HwpBbZ+wBdrTlFwLvrnYEZ5VacsxtKuV1TAJhtc8zwxJjrqq7q2pjVW2pqi1MjqOcU1Vzsyl3RQz53X47k5MKSLKRyXTSbVOtcmUNGfMdwFkASZ7GJBQOTrXK6doDvLidhXQmcHdV3bWcD1zT00d1mFtnJPkNYK6q9gCXMdnF3MtkD+H82VW8fAPH/LvAlwJvacfU76iqc2ZW9DINHPOaMnDM7wK+O8lHgQeAX6yqT86u6uUZOOZfAF6f5OeZTKP86Gr+R16Sv2Ay/bexHSd5GXA8QFW9lslxk7OBvcC9wAXL/s5V/N9LkrTC1vr0kSTpKBgKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlS93+QQ+aPV9LPnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_data[\"Survived\"].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>886</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>888</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull()"
   ]
  },
  {
   "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": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      "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",
       "5            6         0       3   \n",
       "6            7         0       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",
       "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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      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  \n",
       "5      0            330877   8.4583        Q  \n",
       "6      0             17463  51.8625        S  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcl=pd.get_dummies(titanic_data['Pclass'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "   2  3\n",
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "pcl.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sex=pd.get_dummies(titanic_data['Sex'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   male\n",
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       "6     1"
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "sex.head(6)"
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  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "embarked=pd.get_dummies(titanic_data['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "   Q  S\n",
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       "4  0  1"
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     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "embarked.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data=pd.concat([titanic_data,sex,pcl,embarked],axis=1) # axis 1 means its column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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      "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  2  3  Q  S  \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  0  0  1  \n",
       "4      0            373450   8.0500        S     1  0  1  0  1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop(['PassengerId','Pclass','Name','Sex','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop('Embarked',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>2</th>\n",
       "      <th>3</th>\n",
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       "      <th>S</th>\n",
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       "      <td>1</td>\n",
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       "      <td>38.0</td>\n",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
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       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</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  2  3  Q  S\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  0  0  1\n",
       "4         0  35.0      0      0   8.0500     1  0  1  0  1\n",
       "6         0  54.0      0      0  51.8625     1  0  0  0  1\n",
       "7         0   2.0      3      1  21.0750     1  0  1  0  1"
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "titanic_data.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.linear_model  import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=titanic_data.drop('Survived',axis=1)\n",
    "y=titanic_data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>53.1000</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <td>4</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",
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      "text/plain": [
       "    Age  SibSp  Parch     Fare  male  2  3  Q  S\n",
       "0  22.0      1      0   7.2500     1  0  1  0  1\n",
       "1  38.0      1      0  71.2833     0  0  0  0  0\n",
       "2  26.0      0      0   7.9250     0  0  1  0  1\n",
       "3  35.0      1      0  53.1000     0  0  0  0  1\n",
       "4  35.0      0      0   8.0500     1  0  1  0  1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:939: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html.\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "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='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0,\n",
       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import  classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.80      0.81      0.81       126\\n           1       0.72      0.72      0.72        88\\n\\n    accuracy                           0.77       214\\n   macro avg       0.76      0.76      0.76       214\\nweighted avg       0.77      0.77      0.77       214\\n'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[102,  24],\n",
       "       [ 25,  63]], dtype=int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.10280373831776"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predictions)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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