01.LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UNDERSTANDING LOGISTIC REGRESSION WITH TITANIC DATASET\n",
    "\n",
    "<b>PROBLEM STATEMENT</b>\n",
    "\n",
    "*The sinking of the Titanic is one of the most infamous shipwrecks in history.*\n",
    "\n",
    "*On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.*\n",
    "\n",
    "*While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.*\n",
    "\n",
    "*In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).*\n",
    "\n",
    "**Data Source** - https://www.kaggle.com/c/titanic/overview\n",
    "\n",
    "\n",
    "\n",
    "### DOWNLOAD THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading titanic.zip to /Users/nikhilkashyap/Downloads/Learning/Github/11-Projects-to-DataScience\r\n",
      "\r",
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      "\r",
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     ]
    }
   ],
   "source": [
    "!kaggle competitions download -c titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression.ipynb README.md                titanic.zip\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  titanic.zip\r\n",
      "  inflating: gender_submission.csv   \r\n",
      "  inflating: test.csv                \r\n",
      "  inflating: train.csv               \r\n"
     ]
    }
   ],
   "source": [
    "#Unzip the file\n",
    "!unzip titanic.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression.ipynb gender_submission.csv    titanic.zip\r\n",
      "README.md                test.csv                 train.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### READ THE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
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   "cell_type": "code",
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      "  Stored in directory: /Users/nikhilkashyap/Library/Caches/pip/wheels/5c/4b/ec/41f4e116c8053c3654e2c2a47c62b4fca34cc67ef7b55deb7f\n",
      "  Building wheel for dash-editor-components (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for dash-editor-components: filename=dash_editor_components-0.0.2-py3-none-any.whl size=41160 sha256=0d8ce6f57e2bd152cf797858ff36eab9cb786c98162e565055a0675c8d1b9361\n",
      "  Stored in directory: /Users/nikhilkashyap/Library/Caches/pip/wheels/fb/eb/3f/0279d7cdb00f785818be5a9c8f98d8d0a8ab719bee32bdc504\n",
      "  Building wheel for shap (setup.py) ... \u001b[?25l\\"
     ]
    }
   ],
   "source": [
    "!pip install explainx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('1.train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Data Dictionary</b>\n",
    "\n",
    "| Variable | Definition | Definition |\n",
    "| --- | --- | --- |\n",
    "| survival\t| Survival | 0 = No, 1 = Yes|\n",
    "|pclass\t| Ticket class |\t1 = 1st, 2 = 2nd, 3 = 3rd |\n",
    "|sex\t| Sex |\t\n",
    "|Age\t| Age in years |\t\n",
    "|sibsp\t| # of siblings / spouses aboard the Titanic |\n",
    "|parch\t| # of parents / children aboard the Titanic |\n",
    "|ticket\t| Ticket number\t|\n",
    "|fare\t| Passenger fare |\t\n",
    "|cabin\t| Cabin number\t|\n",
    "|embarked | Port of Embarkation | C = Cherbourg, Q = Queenstown, S = Southampton |\n",
    "\n",
    "<b>Variable Notes</b>\n",
    "    \n",
    "pclass: A proxy for socio-economic status (SES)\n",
    "1st = Upper\n",
    "2nd = Middle\n",
    "3rd = Lower\n",
    "\n",
    "age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "\n",
    "sibsp: The dataset defines family relations in this way...\n",
    "Sibling = brother, sister, stepbrother, stepsister\n",
    "Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "parch: The dataset defines family relations in this way...\n",
    "Parent = mother, father\n",
    "Child = daughter, son, stepdaughter, stepson\n",
    "Some children travelled only with a nanny, therefore parch=0 for them.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DATA CLEANING\n",
    "Check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of data missing\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fix Age, Cabin, Embarked.\n",
    "\n",
    "1. Age - Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.865319865319865"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of data missing from \"Age\" column\n",
    "(df['Age'].isnull().sum()/df.shape[0])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill missing values with median\n",
    "df[\"Age\"].fillna(df[\"Age\"].median(skipna=True), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Cabin - Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.10437710437711"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of data missing from \"Cabin\" column\n",
    "(df['Cabin'].isnull().sum()/df.shape[0])*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's lot of data missing in Cabin, filling the missing value is not wise.\n",
    "\n",
    "3. Embarked - Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22446689113355783"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of data missing from \"Embarked\" column\n",
    "(df['Embarked'].isnull().sum()/df.shape[0])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill missing values with mode\n",
    "df[\"Embarked\"].fillna(df['Embarked'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA\n",
    "\n",
    "<u><b>Hypothesis</b></u>\n",
    "\n",
    "1. Women were likely to survive than men.\n",
    "2. Children were more likely to survive.\n",
    "3. Upper class passengers were more likely to survive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the count of passengers who survived and who didn't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d0b15ca90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "577"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Male count on Titanic\n",
    "df[\"Sex\"].loc[df[\"Sex\"] == \"male\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the count of how many men survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d0b46f050>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAOMklEQVR4nO3df6zddX3H8ecLCrLJBLE3DNtuJUo0bCrOBlH/mIEtA+aEOGAYmcU16ZYwo3GbY1syf8QtmjkZ00kkAy1k0SJuozNmzvBjbkbQW0UQiFvnYLSivfwWnWxl7/1xPvfDtdzaA/R7zm3v85Hc9Pv9fL/n9F3S9Mn33HO+N1WFJEkAB017AEnS0mEUJEmdUZAkdUZBktQZBUlSt2LaAzwdK1eurLVr1057DEnar2zduvXeqppZ7Nh+HYW1a9cyOzs77TEkab+S5K49HfPlI0lSZxQkSZ1RkCR1RkGS1BkFSVJnFCRJnVGQJHVGQZLUGQVJUrdff6J5X3jZ710x7RG0BG39szdOewRpKrxSkCR1RkGS1BkFSVJnFCRJnVGQJHVGQZLUGQVJUmcUJEmdUZAkdUZBktQZBUlSZxQkSZ1RkCR1RkGS1BkFSVJnFCRJnVGQJHVGQZLUGQVJUmcUJEmdUZAkdUZBktQZBUlSZxQkSZ1RkCR1RkGS1A0ehSQHJ/lqkk+3/WOT3JRkW5LNSQ5t689o+9va8bVDzyZJ+mGTuFJ4C3DHgv33ARdV1fOBB4ANbX0D8EBbv6idJ0maoEGjkGQ18MvAX7f9ACcDV7dTNgFntu0z2j7t+CntfEnShAx9pfAXwNuB/2v7zwEerKpdbX87sKptrwLuBmjHH2rn/5AkG5PMJpmdm5sbcnZJWnYGi0KS1wA7q2rrvnzeqrq0qtZV1bqZmZl9+dSStOytGPC5XwW8NsnpwGHAs4CLgSOTrGhXA6uBHe38HcAaYHuSFcARwH0DzidJ2s1gVwpV9QdVtbqq1gLnAtdV1RuA64Gz2mnrgWva9pa2Tzt+XVXVUPNJkp5oGp9T+H3gbUm2MfqewWVt/TLgOW39bcCFU5hNkpa1IV8+6qrqBuCGtv1N4MRFzvkBcPYk5pEkLc5PNEuSOqMgSeqMgiSpMwqSpM4oSJI6oyBJ6oyCJKkzCpKkzihIkjqjIEnqjIIkqTMKkqTOKEiSOqMgSeqMgiSpMwqSpM4oSJI6oyBJ6oyCJKkzCpKkzihIkjqjIEnqjIIkqTMKkqTOKEiSOqMgSeqMgiSpMwqSpM4oSJI6oyBJ6oyCJKkzCpKkzihIkjqjIEnqjIIkqRssCkkOS/KlJF9LcluSd7X1Y5PclGRbks1JDm3rz2j729rxtUPNJkla3JBXCo8CJ1fVS4ATgFOTnAS8D7ioqp4PPABsaOdvAB5o6xe18yRJEzRYFGrkkbZ7SPsq4GTg6ra+CTizbZ/R9mnHT0mSoeaTJD3RoN9TSHJwkpuBncDngP8AHqyqXe2U7cCqtr0KuBugHX8IeM4iz7kxyWyS2bm5uSHHl6RlZ9AoVNVjVXUCsBo4EXjhPnjOS6tqXVWtm5mZedozSpIeN5F3H1XVg8D1wCuAI5OsaIdWAzva9g5gDUA7fgRw3yTmkySNDPnuo5kkR7btHwN+EbiDURzOaqetB65p21vaPu34dVVVQ80nSXqiFXs/5Sk7BtiU5GBG8bmqqj6d5HbgE0neA3wVuKydfxlwZZJtwP3AuQPOJklaxGBRqKpbgJcusv5NRt9f2H39B8DZQ80jSdo7P9EsSeqMgiSpMwqSpM4oSJI6oyBJ6oyCJKkzCpKkbqwoJLl2nDVJ0v7tR354LclhwI8DK5M8G5i/lfWzePzuppKkA8TePtH8m8BbgecCW3k8Cg8DHxpwLknSFPzIKFTVxcDFSd5cVR+c0EySpCkZ695HVfXBJK8E1i58TFVdMdBckqQpGCsKSa4EngfcDDzWlgswCpJ0ABn3LqnrgOP9+QaSdGAb93MKXwd+cshBJEnTN+6Vwkrg9iRfAh6dX6yq1w4ylSRpKsaNwjuHHEKStDSM++6jfx56EEnS9I377qPvMnq3EcChwCHA96rqWUMNJkmavHGvFH5ifjtJgDOAk4YaSpI0HU/6Lqk18vfALw0wjyRpisZ9+eh1C3YPYvS5hR8MMpEkaWrGfffRryzY3gXcyeglJEnSAWTc7ym8aehBJEnTN+4P2Vmd5O+S7Gxfn0qyeujhJEmTNe43mj8KbGH0cxWeC/xDW5MkHUDGjcJMVX20qna1r48BMwPOJUmagnGjcF+S85Ic3L7OA+4bcjBJ0uSNG4XfAM4Bvg3cA5wFnD/QTJKkKRn3LanvBtZX1QMASY4C3s8oFpKkA8S4Vwovng8CQFXdD7x0mJEkSdMybhQOSvLs+Z12pTDuVYYkaT8x7j/sfw58Mckn2/7ZwJ8MM5IkaVrG/UTzFUlmgZPb0uuq6vbhxpIkTcPYLwG1CBgCSTqAPelbZ48ryZok1ye5PcltSd7S1o9K8rkk/95+fXZbT5K/TLItyS1Jfm6o2SRJixssCozupvo7VXU8ox/Ic0GS44ELgWur6jjg2rYPcBpwXPvaCFwy4GySpEUMFoWquqeqvtK2vwvcAaxidMvtTe20TcCZbfsM4Ir2Q3xuBI5McsxQ80mSnmjIK4UuyVpGn2u4CTi6qu5ph74NHN22VwF3L3jY9ra2+3NtTDKbZHZubm6wmSVpORo8CkkOBz4FvLWqHl54rKoKqCfzfFV1aVWtq6p1MzPek0+S9qVBo5DkEEZB+Juq+tu2/J35l4Xarzvb+g5gzYKHr25rkqQJGfLdRwEuA+6oqg8sOLQFWN+21wPXLFh/Y3sX0knAQwteZpIkTcCQt6p4FfDrwK1Jbm5rfwi8F7gqyQbgLkZ3XwX4DHA6sA34PuCPAJWkCRssClX1r0D2cPiURc4v4IKh5pEk7d1E3n0kSdo/GAVJUmcUJEmdUZAkdUZBktQZBUlSZxQkSZ1RkCR1RkGS1BkFSVJnFCRJnVGQJHVGQZLUGQVJUmcUJEmdUZAkdUZBktQZBUlSZxQkSZ1RkCR1RkGS1BkFSVJnFCRJnVGQJHVGQZLUGQVJUmcUJEmdUZAkdUZBktQZBUlSZxQkSZ1RkCR1RkGS1BkFSVJnFCRJ3YqhnjjJ5cBrgJ1V9bNt7ShgM7AWuBM4p6oeSBLgYuB04PvA+VX1laFmk/YH//XuF017BC1BP/XHtw76/ENeKXwMOHW3tQuBa6vqOODatg9wGnBc+9oIXDLgXJKkPRgsClX1eeD+3ZbPADa17U3AmQvWr6iRG4Ejkxwz1GySpMVN+nsKR1fVPW3728DRbXsVcPeC87a3NUnSBE3tG81VVUA92ccl2ZhkNsns3NzcAJNJ0vI16Sh8Z/5lofbrzra+A1iz4LzVbe0JqurSqlpXVetmZmYGHVaSlptJR2ELsL5trweuWbD+xoycBDy04GUmSdKEDPmW1I8DrwZWJtkOvAN4L3BVkg3AXcA57fTPMHo76jZGb0l901BzSZL2bLAoVNXr93DolEXOLeCCoWaRJI3HTzRLkjqjIEnqjIIkqTMKkqTOKEiSOqMgSeqMgiSpMwqSpM4oSJI6oyBJ6oyCJKkzCpKkzihIkjqjIEnqjIIkqTMKkqTOKEiSOqMgSeqMgiSpMwqSpM4oSJI6oyBJ6oyCJKkzCpKkzihIkjqjIEnqjIIkqTMKkqTOKEiSOqMgSeqMgiSpMwqSpM4oSJI6oyBJ6oyCJKkzCpKkbklFIcmpSb6RZFuSC6c9jyQtN0smCkkOBv4KOA04Hnh9kuOnO5UkLS9LJgrAicC2qvpmVf0P8AngjCnPJEnLyoppD7DAKuDuBfvbgZfvflKSjcDGtvtIkm9MYLblYiVw77SHWAry/vXTHkE/zL+b896RffEsP72nA0spCmOpqkuBS6c9x4EoyWxVrZv2HNLu/Ls5OUvp5aMdwJoF+6vbmiRpQpZSFL4MHJfk2CSHAucCW6Y8kyQtK0vm5aOq2pXkt4HPAgcDl1fVbVMea7nxZTktVf7dnJBU1bRnkCQtEUvp5SNJ0pQZBUlSZxTk7UW0ZCW5PMnOJF+f9izLhVFY5ry9iJa4jwGnTnuI5cQoyNuLaMmqqs8D9097juXEKGix24usmtIskqbMKEiSOqMgby8iqTMK8vYikjqjsMxV1S5g/vYidwBXeXsRLRVJPg58EXhBku1JNkx7pgOdt7mQJHVeKUiSOqMgSeqMgiSpMwqSpM4oSJI6oyABSf4oyW1Jbklyc5KX74PnfO2+uutskkf2xfNIe+NbUrXsJXkF8AHg1VX1aJKVwKFV9a0xHruifdZj6BkfqarDh/59JK8UJDgGuLeqHgWoqnur6ltJ7myBIMm6JDe07XcmuTLJF4Ark9yY5GfmnyzJDe3885N8KMkRSe5KclA7/swkdyc5JMnzkvxjkq1J/iXJC9s5xyb5YpJbk7xnwv89tIwZBQn+CViT5N+SfDjJz4/xmOOBX6iq1wObgXMAkhwDHFNVs/MnVtVDwM3A/PO+BvhsVf0vox9I/+aqehnwu8CH2zkXA5dU1YuAe572n1Aak1HQsldVjwAvAzYCc8DmJOfv5WFbquq/2/ZVwFlt+xzg6kXO3wz8Wts+t/0ehwOvBD6Z5GbgI4yuWgBeBXy8bV/5pP5A0tOwYtoDSEtBVT0G3ADckORWYD2wi8f/x+mw3R7yvQWP3ZHkviQvZvQP/28t8ltsAf40yVGMAnQd8Ezgwao6YU9jPcU/jvSUeaWgZS/JC5Ict2DpBOAu4E5G/4AD/OpenmYz8HbgiKq6ZfeD7Wrky4xeFvp0VT1WVQ8D/5nk7DZHkrykPeQLjK4oAN7w5P9U0lNjFCQ4HNiU5PYktzD6fsE7gXcBFyeZBR7by3Nczegf8at+xDmbgfPar/PeAGxI8jXgNh7/UahvAS5oVy3+JDxNjG9JlSR1XilIkjqjIEnqjIIkqTMKkqTOKEiSOqMgSeqMgiSp+3/dF042apLQqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',data=df.loc[df[\"Sex\"] == \"male\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "314"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Female count on Titanic\n",
    "df[\"Survived\"].loc[df[\"Sex\"] == \"female\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the count of how many women survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d0b646b10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',data=df.loc[df[\"Sex\"] == \"female\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that our <b><i>Hypothesis 1</i></b> is true.\n",
    "\n",
    "Let's visualize the <i><u>age</u></i> of passengers. \n",
    "\n",
    "Before that we shall create a column <i>\"Minor\"</i>, which will have 1 if the age < 16, else 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Minor'] = np.where(df['Age']<=16, 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's visualize the count of the Minor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d0b1263d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Minor',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "791"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count of Adults\n",
    "df[\"Minor\"].loc[df[\"Minor\"] == 0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d0b700c50>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',data=df.loc[df[\"Minor\"] == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count of Minors\n",
    "df[\"Minor\"].loc[df[\"Minor\"] == 1].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d0b7d27d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived',data=df.loc[df[\"Minor\"] == 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that our <b><i>Hypothesis 2</i></b> is true.\n",
    "\n",
    "Let's visualize the travel class of the passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d0b7b9f10>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Pclass',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d0b964390>"
      ]
     },
     "execution_count": 29,
     "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.barplot('Pclass', 'Survived', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that our <b><i>Hypothesis 3</i></b> is true."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create Categorical variables for Sex\n",
    "df['Sex'] = pd.get_dummies(df['Sex'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create Categorical variables for Embark\n",
    "df['Embarked'] = pd.get_dummies(df['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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",
       "      <th>Minor</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>1</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>0</td>\n",
       "      <td>0</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>0</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>0</td>\n",
       "      <td>0</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>0</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>0</td>\n",
       "      <td>0</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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</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  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin  Embarked  Minor  \n",
       "0         A/5 21171   7.2500   NaN         0      0  \n",
       "1          PC 17599  71.2833   C85         0      0  \n",
       "2  STON/O2. 3101282   7.9250   NaN         0      0  \n",
       "3            113803  53.1000  C123         0      0  \n",
       "4            373450   8.0500   NaN         0      0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know that PassengerID, Name, Ticket, Cabin wouldn't help with model building, so let's drop those columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['PassengerId','Name','Ticket','Cabin'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODEL BUILDING\n",
    "\n",
    "Let's build a logistic regression model"
   ]
  },
  {
   "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>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>Embarked</th>\n",
       "      <th>Minor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked  Minor\n",
       "0         0       3    1  22.0      1      0   7.2500         0      0\n",
       "1         1       1    0  38.0      1      0  71.2833         0      0\n",
       "2         1       3    0  26.0      0      0   7.9250         0      0\n",
       "3         1       1    0  35.0      1      0  53.1000         0      0\n",
       "4         0       3    1  35.0      0      0   8.0500         0      0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Survived',axis=1)\n",
    "y = df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel = LogisticRegression(max_iter=1000)\n",
    "logmodel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8138041733547352"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7835820895522388"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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