Titanic Survival Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f0bf63f9",
   "metadata": {},
   "source": [
    "# Task 1 - Titanic Survival Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5e0555f",
   "metadata": {},
   "source": [
    "## Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cfd07747",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b352f736",
   "metadata": {},
   "source": [
    "## Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1eeecbbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>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>892</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan Cervin</td>\n",
       "      <td>male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7538</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330972</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Mr. Albert Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Davies, Mr. John Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4 48871</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>902</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ilieff, Mr. Ylio</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349220</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "0           892         0       3   \n",
       "1           893         1       3   \n",
       "2           894         0       2   \n",
       "3           895         0       3   \n",
       "4           896         1       3   \n",
       "5           897         0       3   \n",
       "6           898         1       3   \n",
       "7           899         0       2   \n",
       "8           900         1       3   \n",
       "9           901         0       3   \n",
       "10          902         0       3   \n",
       "\n",
       "                                            Name     Sex   Age  SibSp  Parch  \\\n",
       "0                               Kelly, Mr. James    male  34.5      0      0   \n",
       "1               Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                      Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                               Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4   Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "5                     Svensson, Mr. Johan Cervin    male  14.0      0      0   \n",
       "6                           Connolly, Miss. Kate  female  30.0      0      0   \n",
       "7                   Caldwell, Mr. Albert Francis    male  26.0      1      1   \n",
       "8      Abrahim, Mrs. Joseph (Sophie Halaut Easu)  female  18.0      0      0   \n",
       "9                        Davies, Mr. John Samuel    male  21.0      2      0   \n",
       "10                              Ilieff, Mr. Ylio    male   NaN      0      0   \n",
       "\n",
       "       Ticket     Fare Cabin Embarked  \n",
       "0      330911   7.8292   NaN        Q  \n",
       "1      363272   7.0000   NaN        S  \n",
       "2      240276   9.6875   NaN        Q  \n",
       "3      315154   8.6625   NaN        S  \n",
       "4     3101298  12.2875   NaN        S  \n",
       "5        7538   9.2250   NaN        S  \n",
       "6      330972   7.6292   NaN        Q  \n",
       "7      248738  29.0000   NaN        S  \n",
       "8        2657   7.2292   NaN        C  \n",
       "9   A/4 48871  24.1500   NaN        S  \n",
       "10     349220   7.8958   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic=pd.read_csv('tested.csv')\n",
    "titanic.head(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74193a13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca47cd78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>417.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.627188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.481622</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.907576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>996.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1204.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   418.000000  418.000000  418.000000  332.000000  418.000000   \n",
       "mean   1100.500000    0.363636    2.265550   30.272590    0.447368   \n",
       "std     120.810458    0.481622    0.841838   14.181209    0.896760   \n",
       "min     892.000000    0.000000    1.000000    0.170000    0.000000   \n",
       "25%     996.250000    0.000000    1.000000   21.000000    0.000000   \n",
       "50%    1100.500000    0.000000    3.000000   27.000000    0.000000   \n",
       "75%    1204.750000    1.000000    3.000000   39.000000    1.000000   \n",
       "max    1309.000000    1.000000    3.000000   76.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  418.000000  417.000000  \n",
       "mean     0.392344   35.627188  \n",
       "std      0.981429   55.907576  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.895800  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.500000  \n",
       "max      9.000000  512.329200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf7e651",
   "metadata": {},
   "source": [
    "# Analysing data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d0cdb035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5888e7fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    266\n",
       "1    152\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d3191254",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analysis: 0 represents not survived and 1 represents survived\n",
    "# Above graph shows not survived ratio  are greter than the survival ratio "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d12f78e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', 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='Survived',hue='Pclass',data=titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e929849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    218\n",
       "1    107\n",
       "2     93\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Pclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fde6392c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#   Analysis: 0 represents not survived and 1 represents survived\n",
    "#  The passenger who did  not survived are belong to 3rd class and passengers are survived are also belong  to 3rd class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "86ed1850",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "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": [
    "titanic['Age'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4c3fe970",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analysis: we notice that highest age group amoung the ypung age between 20-40 very few passengers in  age-group 70-80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0624a9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "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 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1790a299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    266\n",
       "1    152\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "64e1c1c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#   Analysis: 0 represents not survived and 1 represents survived\n",
    "#  Womens are more likely to survive than males"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c8bad9a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 20,
     "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": [
    "titanic['Fare'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9860329d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.7500     21\n",
       "26.0000    19\n",
       "13.0000    17\n",
       "8.0500     17\n",
       "7.8958     11\n",
       "           ..\n",
       "7.8208      1\n",
       "8.5167      1\n",
       "78.8500     1\n",
       "52.0000     1\n",
       "22.3583     1\n",
       "Name: Fare, Length: 169, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Fare'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0662e647",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We observe that most of the tickets bought are under fare 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02dbc62d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 23,
     "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=titanic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f6e4c9a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#observe that most of passengers do not have their siblings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b2eb40f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 25,
     "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": [
    "titanic['Parch'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "659004b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Sex'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic['Sex'].value_counts().plot(kind='pie',autopct='%.1f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01527a30",
   "metadata": {},
   "source": [
    "### Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b966cc8b",
   "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>892</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0          892         0       3   \n",
       "1          893         1       3   \n",
       "2          894         0       2   \n",
       "3          895         0       3   \n",
       "4          896         1       3   \n",
       "\n",
       "                                           Name  Sex   Age  SibSp  Parch  \\\n",
       "0                              Kelly, Mr. James    1  34.5      0      0   \n",
       "1              Wilkes, Mrs. James (Ellen Needs)    0  47.0      1      0   \n",
       "2                     Myles, Mr. Thomas Francis    1  62.0      0      0   \n",
       "3                              Wirz, Mr. Albert    1  27.0      0      0   \n",
       "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0  22.0      1      1   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "0   330911   7.8292   NaN        Q  \n",
       "1   363272   7.0000   NaN        S  \n",
       "2   240276   9.6875   NaN        Q  \n",
       "3   315154   8.6625   NaN        S  \n",
       "4  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "titanic['Sex']= label_encoder.fit_transform(titanic['Sex'])\n",
    "titanic.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "35f9ccac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0      1\n",
       " 1      0\n",
       " 2      1\n",
       " 3      1\n",
       " 4      0\n",
       "       ..\n",
       " 413    1\n",
       " 414    0\n",
       " 415    1\n",
       " 416    1\n",
       " 417    1\n",
       " Name: Sex, Length: 418, dtype: int32,\n",
       " 0      0\n",
       " 1      1\n",
       " 2      0\n",
       " 3      0\n",
       " 4      1\n",
       "       ..\n",
       " 413    0\n",
       " 414    1\n",
       " 415    0\n",
       " 416    0\n",
       " 417    0\n",
       " Name: Survived, Length: 418, dtype: int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Sex'],titanic['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0865f621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "922226ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic=titanic.drop(['Age'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8390d26e",
   "metadata": {
    "scrolled": true
   },
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan Cervin</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7538</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330972</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Mr. Albert Francis</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Davies, Mr. John Samuel</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4 48871</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>902</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ilieff, Mr. Ylio</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349220</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PassengerId  Survived  Pclass  \\\n",
       "0           892         0       3   \n",
       "1           893         1       3   \n",
       "2           894         0       2   \n",
       "3           895         0       3   \n",
       "4           896         1       3   \n",
       "5           897         0       3   \n",
       "6           898         1       3   \n",
       "7           899         0       2   \n",
       "8           900         1       3   \n",
       "9           901         0       3   \n",
       "10          902         0       3   \n",
       "\n",
       "                                            Name  Sex  SibSp  Parch  \\\n",
       "0                               Kelly, Mr. James    1      0      0   \n",
       "1               Wilkes, Mrs. James (Ellen Needs)    0      1      0   \n",
       "2                      Myles, Mr. Thomas Francis    1      0      0   \n",
       "3                               Wirz, Mr. Albert    1      0      0   \n",
       "4   Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0      1      1   \n",
       "5                     Svensson, Mr. Johan Cervin    1      0      0   \n",
       "6                           Connolly, Miss. Kate    0      0      0   \n",
       "7                   Caldwell, Mr. Albert Francis    1      1      1   \n",
       "8      Abrahim, Mrs. Joseph (Sophie Halaut Easu)    0      0      0   \n",
       "9                        Davies, Mr. John Samuel    1      2      0   \n",
       "10                              Ilieff, Mr. Ylio    1      0      0   \n",
       "\n",
       "       Ticket     Fare Cabin Embarked  \n",
       "0      330911   7.8292   NaN        Q  \n",
       "1      363272   7.0000   NaN        S  \n",
       "2      240276   9.6875   NaN        Q  \n",
       "3      315154   8.6625   NaN        S  \n",
       "4     3101298  12.2875   NaN        S  \n",
       "5        7538   9.2250   NaN        S  \n",
       "6      330972   7.6292   NaN        Q  \n",
       "7      248738  29.0000   NaN        S  \n",
       "8        2657   7.2292   NaN        C  \n",
       "9   A/4 48871  24.1500   NaN        S  \n",
       "10     349220   7.8958   NaN        S  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_final=titanic\n",
    "titanic_final.head(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e89f45",
   "metadata": {},
   "source": [
    "## Train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b53ca634",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=titanic[['Pclass','Sex']]\n",
    "Y=titanic['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "acb026a0",
   "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=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "679e95b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log=LogisticRegression(random_state=0)\n",
    "log.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a56a7b6e",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "087f2a97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 1 0 1 0 0 0 1 1 0 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 1 1 1 1 0 0\n",
      " 1 1 1 1 0 1 1 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 1 1 0 0 1 1 1 1 0 0 1 1 1\n",
      " 1 0 0 1 0 1 0 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "pred=print(log.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a034aae6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "360    0\n",
      "170    0\n",
      "224    1\n",
      "358    0\n",
      "309    1\n",
      "      ..\n",
      "100    1\n",
      "7      0\n",
      "22     1\n",
      "68     0\n",
      "328    0\n",
      "Name: Survived, Length: 84, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5d289dd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "res=log.predict([[2,0]])\n",
    "if (res==0):\n",
    "    print(\" Sorry!Not Survived\")\n",
    "    \n",
    "else:\n",
    "    print(\"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5d7cb38",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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