Logistic Regression -Survival prediction on Titanic Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python Odyssey Logisti regression playlist : https://www.youtube.com/watch?v=K1lGWWkuvxc&list=PLUgYBRHnEddPVGoFJT7EcIYjmtWEIgN-A\n",
    "\n",
    "Python Odyssey repo: https://github.com/AjitAntony\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/K1lGWWkuvxc\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1edb9953588>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('K1lGWWkuvxc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression\n",
    "This is a typical dataset used to teach how to perform logistic regression to predict whether a passenger survived or not during the ill-fated voyage of the Titanic\n",
    "\n",
    "Feature\n",
    "\n",
    "    -Pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
    "    -name Name\n",
    "    -sex Sex\n",
    "    -age Age\n",
    "    -sibsp Number of Siblings/Spouses Aboard\n",
    "    -parch Number of Parents/Children Aboard\n",
    "    -ticket Ticket Number\n",
    "    -fare Passenger Fare (British pound)\n",
    "    -cabin Cabin\n",
    "    -embarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
    "\n",
    "\n",
    "Target\n",
    "\n",
    "    -survival Survival (0 = No; 1 = Yes)\n",
    "    \n",
    "    \n",
    "## Not Going to solve Any real word problem\n",
    "## Apply the concepts learned here to built a classification model (EDA->Feature Engineering->Feature Selection->Model->Evaluate->Predict)for a similar usecase \n",
    "\n",
    "    - In Titanic Dataset based on passengers features we are predict if passender will survive/notsurvive \n",
    "    - Uses built to solve RealWorld problems \n",
    "         - Based on person features(Age,Smoke,Family History) we can predict if person  has cancer/not cancer\n",
    "         - Based on person features (Age,Smoke,Family History) we can  predict if preson  has obestiy/ Not Obesity "
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"titanic_dataset.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",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 12 columns</p>\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",
       "1304         1305         0       3   \n",
       "1305         1306         1       1   \n",
       "1306         1307         0       3   \n",
       "1307         1308         0       3   \n",
       "1308         1309         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",
       "1304                                 Spector, Mr. Woolf    male   NaN      0   \n",
       "1305                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "1306                       Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
       "1307                                Ware, Mr. Frederick    male   NaN      0   \n",
       "1308                           Peter, Master. Michael J    male   NaN      1   \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",
       "...     ...                 ...       ...   ...      ...  \n",
       "1304      0           A.5. 3236    8.0500   NaN        S  \n",
       "1305      0            PC 17758  108.9000  C105        C  \n",
       "1306      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "1307      0              359309    8.0500   NaN        S  \n",
       "1308      1                2668   22.3583   NaN        C  \n",
       "\n",
       "[1309 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    1309 non-null int64\n",
      "Survived       1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Name           1309 non-null object\n",
      "Sex            1309 non-null object\n",
      "Age            1046 non-null float64\n",
      "SibSp          1309 non-null int64\n",
      "Parch          1309 non-null int64\n",
      "Ticket         1309 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass                          Name     Sex  \\\n",
       "1304         1305         0       3            Spector, Mr. Woolf    male   \n",
       "1305         1306         1       1  Oliva y Ocana, Dona. Fermina  female   \n",
       "1306         1307         0       3  Saether, Mr. Simon Sivertsen    male   \n",
       "1307         1308         0       3           Ware, Mr. Frederick    male   \n",
       "1308         1309         0       3      Peter, Master. Michael J    male   \n",
       "\n",
       "       Age  SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
       "1304   NaN      0      0           A.5. 3236    8.0500   NaN        S  \n",
       "1305  39.0      0      0            PC 17758  108.9000  C105        C  \n",
       "1306  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "1307   NaN      0      0              359309    8.0500   NaN        S  \n",
       "1308   NaN      1      1                2668   22.3583   NaN        C  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capture interesting facts present in  Survived column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       0\n",
       "       ..\n",
       "1304    0\n",
       "1305    1\n",
       "1306    0\n",
       "1307    0\n",
       "1308    0\n",
       "Name: Survived, Length: 1309, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Survived\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survived vs Not Survived count "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    815\n",
       "1    494\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survived vs Not Survived count at gender level "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>81</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>734</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Survived    0    1\n",
       "Sex               \n",
       "female     81  385\n",
       "male      734  109"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"Sex\"],df[\"Survived\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Survived vs Not Survived count at gender level and Age level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "woman=df[df[\"Sex\"]==\"female\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>1300</th>\n",
       "      <td>1301</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Peacock, Miss. Treasteall</td>\n",
       "      <td>female</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SOTON/O.Q. 3101315</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>1302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365237</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347086</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>466 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass  \\\n",
       "1               2         1       1   \n",
       "2               3         1       3   \n",
       "3               4         1       1   \n",
       "8               9         1       3   \n",
       "9              10         1       2   \n",
       "...           ...       ...     ...   \n",
       "1300         1301         1       3   \n",
       "1301         1302         1       3   \n",
       "1302         1303         1       1   \n",
       "1303         1304         1       3   \n",
       "1305         1306         1       1   \n",
       "\n",
       "                                                   Name     Sex   Age  SibSp  \\\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",
       "8     Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                   Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "...                                                 ...     ...   ...    ...   \n",
       "1300                          Peacock, Miss. Treasteall  female   3.0      1   \n",
       "1301                             Naughton, Miss. Hannah  female   NaN      0   \n",
       "1302    Minahan, Mrs. William Edward (Lillian E Thorpe)  female  37.0      1   \n",
       "1303                     Henriksson, Miss. Jenny Lovisa  female  28.0      0   \n",
       "1305                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "\n",
       "      Parch              Ticket      Fare Cabin Embarked  \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",
       "8         2              347742   11.1333   NaN        S  \n",
       "9         0              237736   30.0708   NaN        C  \n",
       "...     ...                 ...       ...   ...      ...  \n",
       "1300      1  SOTON/O.Q. 3101315   13.7750   NaN        S  \n",
       "1301      0              365237    7.7500   NaN        Q  \n",
       "1302      0               19928   90.0000   C78        Q  \n",
       "1303      0              347086    7.7750   NaN        S  \n",
       "1305      0            PC 17758  108.9000  C105        C  \n",
       "\n",
       "[466 rows x 12 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "woman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "woman_survived=woman[woman[\"Survived\"]==1]\n",
    "woman_not_survived=woman[woman[\"Survived\"]==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "man=df[df[\"Sex\"]==\"male\"]\n",
    "man_survived=man[man[\"Survived\"]==1]\n",
    "man_not_survived=man[man[\"Survived\"]==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "    <tr>\n",
       "      <th>5</th>\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",
       "      <th>6</th>\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",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>1298</th>\n",
       "      <td>1299</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Widener, Mr. George Dunton</td>\n",
       "      <td>male</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>113503</td>\n",
       "      <td>211.5000</td>\n",
       "      <td>C80</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>734 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PassengerId  Survived  Pclass                            Name   Sex  \\\n",
       "0               1         0       3         Braund, Mr. Owen Harris  male   \n",
       "4               5         0       3        Allen, Mr. William Henry  male   \n",
       "5               6         0       3                Moran, Mr. James  male   \n",
       "6               7         0       1         McCarthy, Mr. Timothy J  male   \n",
       "7               8         0       3  Palsson, Master. Gosta Leonard  male   \n",
       "...           ...       ...     ...                             ...   ...   \n",
       "1298         1299         0       1      Widener, Mr. George Dunton  male   \n",
       "1304         1305         0       3              Spector, Mr. Woolf  male   \n",
       "1306         1307         0       3    Saether, Mr. Simon Sivertsen  male   \n",
       "1307         1308         0       3             Ware, Mr. Frederick  male   \n",
       "1308         1309         0       3        Peter, Master. Michael J  male   \n",
       "\n",
       "       Age  SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
       "0     22.0      1      0           A/5 21171    7.2500   NaN        S  \n",
       "4     35.0      0      0              373450    8.0500   NaN        S  \n",
       "5      NaN      0      0              330877    8.4583   NaN        Q  \n",
       "6     54.0      0      0               17463   51.8625   E46        S  \n",
       "7      2.0      3      1              349909   21.0750   NaN        S  \n",
       "...    ...    ...    ...                 ...       ...   ...      ...  \n",
       "1298  50.0      1      1              113503  211.5000   C80        C  \n",
       "1304   NaN      0      0           A.5. 3236    8.0500   NaN        S  \n",
       "1306  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "1307   NaN      0      0              359309    8.0500   NaN        S  \n",
       "1308   NaN      1      1                2668   22.3583   NaN        C  \n",
       "\n",
       "[734 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "man_not_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c360d16848>"
      ]
     },
     "execution_count": 16,
     "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": [
    "# Age- 1-100 --> 1,20 - bin1 ,20-40bin2 ,40-60-bin3,60-80 bin4,80-100bin5\n",
    "#Histogram\n",
    "sns.distplot(woman_survived.Age,bins=5,label=\"woman_survived\",kde=False)\n",
    "sns.distplot(woman_not_survived.Age,bins=5,label=\"woman_not_survived\",kde=False)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c360e06b48>"
      ]
     },
     "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.distplot(man_survived.Age,bins=5,label=\"man_survived\",kde=True)\n",
    "sns.distplot(man_not_survived.Age,bins=5,label=\"man_not_survived\",kde=True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Histogram -> affect by bin value   - Y count\n",
    "#KDE -> dose not get affect by bin values -Y probability "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature engineering \n",
    "    -filling missing values\n",
    "    -variable transformation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list(df.columns.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 12 columns</p>\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",
       "1304         1305         0       3   \n",
       "1305         1306         1       1   \n",
       "1306         1307         0       3   \n",
       "1307         1308         0       3   \n",
       "1308         1309         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",
       "1304                                 Spector, Mr. Woolf    male   NaN      0   \n",
       "1305                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "1306                       Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
       "1307                                Ware, Mr. Frederick    male   NaN      0   \n",
       "1308                           Peter, Master. Michael J    male   NaN      1   \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",
       "...     ...                 ...       ...   ...      ...  \n",
       "1304      0           A.5. 3236    8.0500   NaN        S  \n",
       "1305      0            PC 17758  108.9000  C105        C  \n",
       "1306      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "1307      0              359309    8.0500   NaN        S  \n",
       "1308      1                2668   22.3583   NaN        C  \n",
       "\n",
       "[1309 rows x 12 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       0\n",
       "Survived          0\n",
       "Pclass            0\n",
       "Name              0\n",
       "Sex               0\n",
       "Age             263\n",
       "SibSp             0\n",
       "Parch             0\n",
       "Ticket            0\n",
       "Fare              1\n",
       "Cabin          1014\n",
       "Embarked          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n",
    "\n",
    "# cabin -> \n",
    "#[1,2,NAN,NAN,5]\n",
    "#[False,False,True,True,False]\n",
    "#[0,0,1,1,0] -> 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c361005588>"
      ]
     },
     "execution_count": 28,
     "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())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CABIN "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 12)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1  df=df.drop([\"Cabin\"],axis=1)\n",
    "#2 inplace=True\n",
    "\n",
    "df.drop([\"Cabin\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 11 columns</p>\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",
       "1304         1305         0       3   \n",
       "1305         1306         1       1   \n",
       "1306         1307         0       3   \n",
       "1307         1308         0       3   \n",
       "1308         1309         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",
       "1304                                 Spector, Mr. Woolf    male   NaN      0   \n",
       "1305                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "1306                       Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
       "1307                                Ware, Mr. Frederick    male   NaN      0   \n",
       "1308                           Peter, Master. Michael J    male   NaN      1   \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",
       "...     ...                 ...       ...      ...  \n",
       "1304      0           A.5. 3236    8.0500        S  \n",
       "1305      0            PC 17758  108.9000        C  \n",
       "1306      0  SOTON/O.Q. 3101262    7.2500        S  \n",
       "1307      0              359309    8.0500        S  \n",
       "1308      1                2668   22.3583        C  \n",
       "\n",
       "[1309 rows x 11 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            263\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q', nan], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Embarked\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    914\n",
       "C    270\n",
       "Q    123\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Embarked\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Embarked\"]=df[\"Embarked\"].fillna(\"S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            263\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Age "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df[\"Age\"].mean()\n",
    "#df[\"Age\"].median()\n",
    "#df[\"Age\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3., nan, nan,  6.,  7.,  8., nan, 10.])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_tmp=np.array([1,2,3,np.NAN,np.NAN,6,7,8,np.NAN,10])\n",
    "age_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  3, 44])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_age_tmp=np.random.randint(1,100,3)\n",
    "random_age_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False,  True,  True, False, False, False,  True,\n",
       "       False])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(age_tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_tmp[np.isnan(age_tmp)]=random_age_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  6.,  3.,  6.,  7.,  8., 44., 10.])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling missing values on AGE column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.881137667304014"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_age=df[\"Age\"].mean()\n",
    "mean_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.413493211271321"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_age=df[\"Age\"].std()\n",
    "std_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.467644456032692"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_age=mean_age-std_age\n",
    "low_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44.29463087857533"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_age=mean_age+std_age\n",
    "high_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 41, 30, 22, 37, 20, 36, 42, 34, 23, 43, 20, 28, 33, 39, 42, 42,\n",
       "       23, 40, 27, 34, 27, 23, 33, 33, 25, 21, 17, 23, 32, 38, 28, 40, 21,\n",
       "       29, 35, 33, 42, 35, 37, 20, 26, 41, 29, 32, 33, 17, 29, 31, 26, 21,\n",
       "       39, 37, 43, 17, 24, 25, 27, 43, 24, 19, 17, 38, 23, 43, 22, 43, 37,\n",
       "       42, 15, 17, 29, 28, 36, 33, 27, 19, 17, 34, 27, 39, 20, 26, 29, 34,\n",
       "       25, 28, 18, 16, 36, 21, 39, 23, 16, 31, 30, 36, 25, 31, 34, 17, 24,\n",
       "       38, 39, 23, 40, 15, 36, 37, 17, 30, 25, 33, 19, 35, 16, 21, 18, 41,\n",
       "       34, 22, 16, 36, 22, 28, 41, 33, 19, 17, 34, 25, 31, 36, 29, 37, 43,\n",
       "       41, 26, 27, 42, 25, 29, 31, 25, 42, 25, 22, 29, 18, 15, 31, 33, 43,\n",
       "       32, 33, 32, 28, 30, 22, 17, 23, 22, 34, 29, 30, 26, 35, 42, 32, 39,\n",
       "       17, 20, 18, 21, 25, 38, 30, 34, 37, 34, 29, 37, 42, 22, 33, 36, 26,\n",
       "       34, 37, 21, 40, 42, 34, 37, 22, 37, 35, 33, 23, 28, 15, 30, 23, 25,\n",
       "       28, 16, 33, 20, 24, 22, 30, 42, 17, 27, 38, 30, 20, 24, 42, 22, 16,\n",
       "       39, 41, 39, 37, 15, 29, 22, 22, 34, 34, 42, 42, 28, 18, 25, 17, 22,\n",
       "       33, 32, 22, 20, 18, 16, 27, 21, 15, 39, 20, 16, 38, 26, 26, 31, 25,\n",
       "       21, 29, 15, 21, 26, 31, 40, 35])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_ages=np.random.randint(15,44,263)\n",
    "random_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       22.0\n",
       "1       38.0\n",
       "2       26.0\n",
       "3       35.0\n",
       "4       35.0\n",
       "        ... \n",
       "1304     NaN\n",
       "1305    39.0\n",
       "1306    38.5\n",
       "1307     NaN\n",
       "1308     NaN\n",
       "Name: Age, Length: 1309, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_copy=df[\"Age\"].copy()\n",
    "age_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       22.0\n",
       "1       38.0\n",
       "2       26.0\n",
       "3       35.0\n",
       "4       35.0\n",
       "        ... \n",
       "1304    31.0\n",
       "1305    39.0\n",
       "1306    38.5\n",
       "1307    40.0\n",
       "1308    35.0\n",
       "Name: Age, Length: 1309, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_copy[np.isnan(age_copy)]=random_ages\n",
    "age_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Age\"]=age_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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           1\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # FARE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# variable transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1308 rows × 11 columns</p>\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",
       "1304         1305         0       3   \n",
       "1305         1306         1       1   \n",
       "1306         1307         0       3   \n",
       "1307         1308         0       3   \n",
       "1308         1309         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",
       "1304                                 Spector, Mr. Woolf    male  31.0      0   \n",
       "1305                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "1306                       Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
       "1307                                Ware, Mr. Frederick    male  40.0      0   \n",
       "1308                           Peter, Master. Michael J    male  35.0      1   \n",
       "\n",
       "      Parch              Ticket      Fare Embarked  \n",
       "0         0           A/5 21171    7.2500        S  \n",
       "1         0            PC 17599   71.2833        C  \n",
       "2         0    STON/O2. 3101282    7.9250        S  \n",
       "3         0              113803   53.1000        S  \n",
       "4         0              373450    8.0500        S  \n",
       "...     ...                 ...       ...      ...  \n",
       "1304      0           A.5. 3236    8.0500        S  \n",
       "1305      0            PC 17758  108.9000        C  \n",
       "1306      0  SOTON/O.Q. 3101262    7.2500        S  \n",
       "1307      0              359309    8.0500        S  \n",
       "1308      1                2668   22.3583        C  \n",
       "\n",
       "[1308 rows x 11 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      842\n",
       "female    466\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Sex\"]=df[\"Sex\"].map({\"male\":\"0\",\"female\":\"1\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>0</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>1</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>1</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>1</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>0</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",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>1304</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1308 rows × 11 columns</p>\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",
       "1304         1305         0       3   \n",
       "1305         1306         1       1   \n",
       "1306         1307         0       3   \n",
       "1307         1308         0       3   \n",
       "1308         1309         0       3   \n",
       "\n",
       "                                                   Name Sex   Age  SibSp  \\\n",
       "0                               Braund, Mr. Owen Harris   0  22.0      1   \n",
       "1     Cumings, Mrs. John Bradley (Florence Briggs Th...   1  38.0      1   \n",
       "2                                Heikkinen, Miss. Laina   1  26.0      0   \n",
       "3          Futrelle, Mrs. Jacques Heath (Lily May Peel)   1  35.0      1   \n",
       "4                              Allen, Mr. William Henry   0  35.0      0   \n",
       "...                                                 ...  ..   ...    ...   \n",
       "1304                                 Spector, Mr. Woolf   0  31.0      0   \n",
       "1305                       Oliva y Ocana, Dona. Fermina   1  39.0      0   \n",
       "1306                       Saether, Mr. Simon Sivertsen   0  38.5      0   \n",
       "1307                                Ware, Mr. Frederick   0  40.0      0   \n",
       "1308                           Peter, Master. Michael J   0  35.0      1   \n",
       "\n",
       "      Parch              Ticket      Fare Embarked  \n",
       "0         0           A/5 21171    7.2500        S  \n",
       "1         0            PC 17599   71.2833        C  \n",
       "2         0    STON/O2. 3101282    7.9250        S  \n",
       "3         0              113803   53.1000        S  \n",
       "4         0              373450    8.0500        S  \n",
       "...     ...                 ...       ...      ...  \n",
       "1304      0           A.5. 3236    8.0500        S  \n",
       "1305      0            PC 17758  108.9000        C  \n",
       "1306      0  SOTON/O.Q. 3101262    7.2500        S  \n",
       "1307      0              359309    8.0500        S  \n",
       "1308      1                2668   22.3583        C  \n",
       "\n",
       "[1308 rows x 11 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    915\n",
       "C    270\n",
       "Q    123\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Embarked\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1308 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Q  S\n",
       "0     0  1\n",
       "1     0  0\n",
       "2     0  1\n",
       "3     0  1\n",
       "4     0  1\n",
       "...  .. ..\n",
       "1304  0  1\n",
       "1305  0  0\n",
       "1306  0  1\n",
       "1307  0  1\n",
       "1308  0  0\n",
       "\n",
       "[1308 rows x 2 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boarding_point=pd.get_dummies(df[\"Embarked\"],drop_first=True)\n",
    "boarding_point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Name\",\"Ticket\",\"PassengerId\",\"Embarked\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.concat([df,boarding_point],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>1304</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1308 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Survived  Pclass Sex   Age  SibSp  Parch      Fare  Q  S\n",
       "0            0       3   0  22.0      1      0    7.2500  0  1\n",
       "1            1       1   1  38.0      1      0   71.2833  0  0\n",
       "2            1       3   1  26.0      0      0    7.9250  0  1\n",
       "3            1       1   1  35.0      1      0   53.1000  0  1\n",
       "4            0       3   0  35.0      0      0    8.0500  0  1\n",
       "...        ...     ...  ..   ...    ...    ...       ... .. ..\n",
       "1304         0       3   0  31.0      0      0    8.0500  0  1\n",
       "1305         1       1   1  39.0      0      0  108.9000  0  0\n",
       "1306         0       3   0  38.5      0      0    7.2500  0  1\n",
       "1307         0       3   0  40.0      0      0    8.0500  0  1\n",
       "1308         0       3   0  35.0      1      1   22.3583  0  0\n",
       "\n",
       "[1308 rows x 9 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>1304</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1308 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Pclass Sex   Age  SibSp  Parch      Fare  Q  S\n",
       "0          3   0  22.0      1      0    7.2500  0  1\n",
       "1          1   1  38.0      1      0   71.2833  0  0\n",
       "2          3   1  26.0      0      0    7.9250  0  1\n",
       "3          1   1  35.0      1      0   53.1000  0  1\n",
       "4          3   0  35.0      0      0    8.0500  0  1\n",
       "...      ...  ..   ...    ...    ...       ... .. ..\n",
       "1304       3   0  31.0      0      0    8.0500  0  1\n",
       "1305       1   1  39.0      0      0  108.9000  0  0\n",
       "1306       3   0  38.5      0      0    7.2500  0  1\n",
       "1307       3   0  40.0      0      0    8.0500  0  1\n",
       "1308       3   0  35.0      1      1   22.3583  0  0\n",
       "\n",
       "[1308 rows x 8 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df.drop(\"Survived\",axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       0\n",
       "       ..\n",
       "1304    0\n",
       "1305    1\n",
       "1306    0\n",
       "1307    0\n",
       "1308    0\n",
       "Name: Survived, Length: 1308, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df[\"Survived\"]\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.30, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LogisticRegression(max_iter=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=500)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
       "       1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_res=model.predict(X_test)\n",
    "predicted_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1013    1\n",
       "1049    0\n",
       "695     0\n",
       "736     0\n",
       "723     0\n",
       "       ..\n",
       "325     1\n",
       "916     0\n",
       "532     0\n",
       "679     1\n",
       "513     1\n",
       "Name: Survived, Length: 393, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8422391857506362"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,predicted_res)  # tp+tn /tp+tn+fp+fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[214,  26],\n",
       "       [ 36, 117]], dtype=int64)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predicted_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "214-> 1 survived : model->survived\n",
    "26 -> 0 Not survived : model->survived\n",
    "\n",
    "117-> 0 Not survived : model->Not survived \n",
    "36-> 1  survived : model -> Not Survived \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.89      0.87       240\n",
      "           1       0.82      0.76      0.79       153\n",
      "\n",
      "    accuracy                           0.84       393\n",
      "   macro avg       0.84      0.83      0.83       393\n",
      "weighted avg       0.84      0.84      0.84       393\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predicted_res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    240\n",
       "1    153\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 250\n",
      "1 143\n"
     ]
    }
   ],
   "source": [
    "print(\"0\",(predicted_res==0).sum())\n",
    "print(\"1\",(predicted_res==1).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# P 1\n",
    "# N 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# precision - 82%   \n",
    "# 143 ppl survived - out of 143 ppl that our model predicted as survived , how many had actual value as survived "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recall - 0.76 %\n",
    "\n",
    "# out of 153 survived ppl how many the model was able to identify as survived 143"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "macro avg\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.84"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"macro avg\")\n",
    "(0.86+0.82)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8444274809160306"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((240*0.86)+ (153*0.82))/(240+153)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction for JACK "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass        1\n",
       "Sex           1\n",
       "Age          35\n",
       "SibSp         1\n",
       "Parch         0\n",
       "Fare      57.75\n",
       "Q             0\n",
       "S             0\n",
       "Name: 1013, dtype: object"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    512.3292\n",
       "2     73.5000\n",
       "3     69.5500\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Pclass\")[\"Fare\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jack=[[2,0,22,0,0,73.5000,0,1]]\n",
    "\n",
    "model.predict(jack)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction for ROSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rose=[[1,1,22,1,1,512.3292,0,1]]\n",
    "model.predict(rose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-8.39889103e-01,  3.62460455e+00, -2.56359702e-02,\n",
       "        -3.54050804e-01,  5.36304009e-02,  1.99656417e-03,\n",
       "         1.36114345e-01, -2.44181280e-01]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_coeff=pd.DataFrame(X_test.columns.values,model.coef_[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-0.839889</th>\n",
       "      <td>Pclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.624605</th>\n",
       "      <td>Sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.025636</th>\n",
       "      <td>Age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.354051</th>\n",
       "      <td>SibSp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.053630</th>\n",
       "      <td>Parch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001997</th>\n",
       "      <td>Fare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.136114</th>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.244181</th>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0\n",
       "-0.839889  Pclass\n",
       " 3.624605     Sex\n",
       "-0.025636     Age\n",
       "-0.354051   SibSp\n",
       " 0.053630   Parch\n",
       " 0.001997    Fare\n",
       " 0.136114       Q\n",
       "-0.244181       S"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Q', 'S'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}