Project using RandomForest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb8fd4ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Introduction\n",
    "# Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks.\n",
    "#It is a type of ensemble learning method, where a group of weak models combine to form a powerful model.\n",
    "#They operates by constructing a multitude of decision trees at training time.\n",
    "#They give output of the class that is the mode of the classes or mean/average prediction of the individual trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "97d5eadc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Problem Statement\n",
    "#RMS Titanic was a British passenger liner operated by the White Star Line that sank in the North Atlantic Ocean on 15 April 1912.\n",
    "#On April 15, 1912 the Titanic sank, costing the lives of 1502 out of 2224 passengers and crew.\n",
    "#Unfortunately, there weren’t enough lifeboats for everyone onboard, causing a disproportionate number of deaths.\n",
    "#The reason behind sinking of this beauty was because of striking to an iceberg while travelling from Southampton to New York City.\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c323be77",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scenario\n",
    "#One of the most popular disasters in the history is the sinking of the RMS Titanic.\n",
    "#Let's say, after the accident, there is a need to implement better regulations for passenger liners.\n",
    "#As a part of a data science team, you have been tasked with analyzing the passenger data of the RMS Titanic.\n",
    "#You have been provided with the data that contains the information about the passengers aboarded the ship.\n",
    "#The purpose is to analyze and explore factors that can influence a person’s likelihood to survive.\n",
    "#So, the primary objectives are to:\n",
    "#Do a statistical analysis of how some group of people were survived more than others.\n",
    "#Perform an Exploratory Data Analysis of titanic dataset with visualizations and storytelling.\n",
    "#Do modelling of data to come up with a model with best fit of accuracy score and which explains the \n",
    "#solution of above issue appropriately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a33e199a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Installing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "45a9f993",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#------------------------------------------------------------------------------------------------------------------------------\n",
    "import pandas as pd                                                 # Importing for panel data analysis\n",
    "                     \n",
    "#-------------------------------------------------------------------------------------------------------------------------------\n",
    "import numpy as np                                                  # Importing package numpys (For Numerical Python)\n",
    "#-------------------------------------------------------------------------------------------------------------------------------\n",
    "import matplotlib.pyplot as plt                                     # Importing pyplot interface using matplotlib\n",
    "import seaborn as sns                                               # Importin seaborm library for interactive visualization\n",
    "%matplotlib inline\n",
    "from scipy.stats import randint as sp_randint                       # For initializing random integer values\n",
    "#-------------------------------------------------------------------------------------------------------------------------------\n",
    "from sklearn.preprocessing import StandardScaler                    # To scaled data with mean 0 and variance 1\n",
    "from sklearn.model_selection import train_test_split                # To split the data in training and testing part\n",
    "from sklearn.model_selection import RandomizedSearchCV              # To find best hyperparamter setting for the algorithm\n",
    "from sklearn.ensemble import RandomForestClassifier                 # To implement random forest classifier\n",
    "from sklearn.tree import DecisionTreeClassifier                     # To implement decision tree classifier\n",
    "from sklearn.metrics import classification_report                   # To generate classification report\n",
    "from sklearn.metrics import plot_confusion_matrix                   # To find best hyperparamter setting for the algorithm\n",
    "#-------------------------------------------------------------------------------------------------------------------------------\n",
    "import warnings                                                     # Importing warning to disable runtime warnings\n",
    "warnings.filterwarnings(\"ignore\")                                   # Warnings will appear only once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c6d7102",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Data Acquisition & Description- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a3d3b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The dataset consists of the information about people boarding the famous RMS Titanic.\n",
    "#Records\tFeatures\tDataset Size\n",
    "#891\t12\t58.9 KB\n",
    "\n",
    "#ID\tFeature Name\tDescription of the feature\n",
    "#01\tPassengerId\tIdentity of the passenger\n",
    "#02\tSurvived\tWhether the passenger survived or not\n",
    "#03\tPclass\tClass of the ticket holder\n",
    "#04\tName\tName of the passenger\n",
    "#05\tSex\tSex of the passenger\n",
    "#06\tAge\tAge of the passenger\n",
    "#07\tSibSp\tSiblings and/or spouse travelling with passenger\n",
    "#08\tParch\tParents and/or children travelling with passenger\n",
    "#09\tTicket\tTicket number\n",
    "#10\tFare\tPrice of the ticket\n",
    "#11\tCabin\tCabin number\n",
    "#12\tEmbarked\tPort of Embarkation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f89c55a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataset: (891, 12)\n"
     ]
    },
    {
     "data": {
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       "</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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data\n",
    "data = pd.read_csv(filepath_or_buffer='https://raw.githubusercontent.com/insaid2018/Term-1/master/Data/Casestudy/titanic_train.csv')\n",
    "\n",
    "# Get the dimesions of data\n",
    "print('Shape of the dataset:', data.shape)\n",
    "\n",
    "# Output first 5 data rows\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79671750",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.1 Data Description\n",
    "#In this section we will get information about the data and see some observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6575a1f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac928d48",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Observations:\n",
    "    \n",
    "#Survived:\n",
    "#More than 50% did not survive the accident.\n",
    "\n",
    "#Pclass:\n",
    "#There are a lot more 3rd class passengers than 1st and 2nd class.\n",
    "#We can also see that there are more 2nd class passengers than 1st class passengers.\n",
    "#SibSp:\n",
    "#More than 50% of passengers are not travelling with their siblings or a spouse.\n",
    "#There are some passengers who are travelling with as maximum as 8 siblings and spouse.\n",
    "#Parch:\n",
    "#More than 75% passengers are not travelling with a parent or children\n",
    "#But there are some passengers who have a maximum number of 6 children and/or parents with them on the ship.\n",
    "#We observe that a vast majority of passengers are not travelling with their family members.\n",
    "#Age:\n",
    "#The average age of passengers is around 29 years while the minimum and maximum ages are 0.4 years and 80 years respectively.\n",
    "#There is some missing data in the Age feature.\n",
    "#Fare:\n",
    "#The average price of ticket seems to be £32.2. Minimum price of the ticket is recorded as £0 and maximum price recorded as high as £512.32.\n",
    "#More than 50% of the passengers have paid atleast £14\n",
    "#More than 75% passengers have paid atleast £7 for their ticket whereas less than 25% have paid for more than £31.\n",
    "#We have to replace the minimum value in the Fare feature with a reasonable value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f979cc24",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.2 Data Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57c3d237",
   "metadata": {},
   "outputs": [],
   "source": [
    "#In this section we will see the information about the types of the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c102eae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59556bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Observations:\n",
    "#The data types of all the features look appropriate.\n",
    "#There are missing values present in the Age, Cabin and Embarked features.\n",
    "#Age and Cabin have a significant amount of missing values which requires further investigation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f67d85fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#5. Data Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a168af65",
   "metadata": {},
   "outputs": [],
   "source": [
    "#5.1 Data Pre-profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf730f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For quick analysis pandas profiling is very handy.\n",
    "#Generates profile reports from a pandas DataFrame.\n",
    "#For each column statistics are presented in an interactive HTML report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d3b15ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# profile = ProfileReport(df=data)\n",
    "# profile.to_file(output_file='Pre Profiling Report.html')\n",
    "# print('Accomplished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb26ffb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "**Observations:**\n",
    "\n",
    "- The report shows that there are a **total** of **12 features** out of which **7** are **numerical** and **5** are **categorical**.\n",
    "\n",
    "- Only **342** passengers out of **891** survived the accident.\n",
    "\n",
    "- **Name**, **Ticket**, and **Cabin** features have high cardinality and are uniformly distributed.\n",
    "\n",
    "- **PassengerId** is having a uniformly distribution in the dataset.\n",
    "\n",
    "- **Fare** feature is highly skewed towards right while **Age** feature is faily symmetrical.\n",
    "\n",
    "- There are **no duplicate** rows in the dataset while a lot of **zeros** are present in **Fare**, **Sibsp** and **Parch** features.\n",
    "\n",
    "- We can observe that **8.1%** of data in cells is **missing**:\n",
    "  - Age (17 missing values) &rarr; Fill with median.\n",
    "  - Cabin (687 missing values) &rarr; Fill with median.\n",
    "  - Embarked (2 missing values) &rarr; Fill with mode.\n",
    "\n",
    "- **Embarked** feature has just **2 missing values**.\n",
    "\n",
    "- For detailed information, check the **Pre Profiling Report.html** file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f8ed6e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5.2 Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79892448",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now we will perform all the necessary operations that are required to clean and rectify the schema of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56253f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling the missing values of Embarked feature with the mode of the feature.\n",
    "data['Embarked'] = data['Embarked'].fillna(value=data['Embarked'].mode()[0])\n",
    "\n",
    "# Filling the missing values of Age feature with the median age.\n",
    "data['Age'].fillna(value=data['Age'].median(), inplace=True)\n",
    "\n",
    "# Dropping the Cabin feature as has many missing values\n",
    "data.drop(labels='Cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7c255fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5.3 Data Post Profilling\n",
    "#Next, we will generate and verify the report to identify any new observation that can help us in further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32171733",
   "metadata": {},
   "outputs": [],
   "source": [
    "# post_profile=ProfileReport(df=data)\n",
    "# post_profile.to_file(output_file='Post Profiling Report.html')\n",
    "# print('Accomplished!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0015436f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Observations:\n",
    "You can compare the two reports, i.e Pre Profiling Report.html and Post Profiling Report.html.\n",
    "Observations in Post Profiling Report.html:\n",
    "In the Dataset info, Total Missing = 0.0%\n",
    "Number of features = 11\n",
    "You can see the difference in the Age feature in both the reports.\n",
    "A lot of zeros are present in Sibsp and Parch features. They won't be removed as they are necessary.\n",
    "We can observe that Pclass and Fare are highly correlated to each other inversely.\n",
    "A lot of inverse correlations are observed among the features.\n",
    "For detailed information, check the Post Profiling Report.html file.\n",
    "We can now begin the Exploratory Data Analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "999c294b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7712ee6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Before diving further, we will create some new features that will be useful for analyzing the data.\n",
    "#These features will be FamilySize \n",
    "#The FamilySize will describe the frequency of family members."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0ed79b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>1</td>\n",
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       "      <td>female</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  Family Size  \n",
       "0      0         A/5 21171   7.2500        S            2  \n",
       "1      0          PC 17599  71.2833        C            2  \n",
       "2      0  STON/O2. 3101282   7.9250        S            1  \n",
       "3      0            113803  53.1000        S            2  \n",
       "4      0            373450   8.0500        S            1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a new feature FamilySize from Sibsp and Parch\n",
    "data['Family Size'] = data['SibSp'] + data['Parch'] + 1\n",
    "\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eb1e3749",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
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       "      <td>female</td>\n",
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       "      <td>3</td>\n",
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       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
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       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>female</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",
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       "      <td>0</td>\n",
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       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  Family Size GenderClass  \n",
       "0      0         A/5 21171   7.2500        S            2        male  \n",
       "1      0          PC 17599  71.2833        C            2      female  \n",
       "2      0  STON/O2. 3101282   7.9250        S            1      female  \n",
       "3      0            113803  53.1000        S            2      female  \n",
       "4      0            373450   8.0500        S            1        male  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating another variable for easy analysis i.e Gender calss in which all passenger less than 15 years will be child other \n",
    "# will be described as per 'sex' variable\n",
    "#lambda argument : expression\n",
    "# e.g z = lambda a : a*a\n",
    "# z(3)\n",
    "\n",
    "data['GenderClass']= data.apply(lambda x: 'child' if x['Age']<15 else x['Sex'], axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb9e3da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating crsoss tabs to assess data further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "e9047e07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['Sex']).mean()[[\"Survived\"]]\n",
    "\n",
    "# Interpretation- The survival among female passengers was more than male passengers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9712442c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GenderClass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>child</th>\n",
       "      <td>0.576923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.163569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived\n",
       "GenderClass          \n",
       "child        0.576923\n",
       "female       0.760000\n",
       "male         0.163569"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['GenderClass']).mean()[[\"Survived\"]]\n",
    "\n",
    "#Interpretation- The survival among passengers was highest in females followed by children. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "8ab3e716",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"3\" valign=\"top\">child</th>\n",
       "      <th>1</th>\n",
       "      <td>0.800000</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.407407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>1</th>\n",
       "      <td>0.978261</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.504274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>1</th>\n",
       "      <td>0.352941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.080808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.118750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Survived\n",
       "GenderClass Pclass          \n",
       "child       1       0.800000\n",
       "            2       1.000000\n",
       "            3       0.407407\n",
       "female      1       0.978261\n",
       "            2       0.909091\n",
       "            3       0.504274\n",
       "male        1       0.352941\n",
       "            2       0.080808\n",
       "            3       0.118750"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['GenderClass', 'Pclass']).mean()[[\"Survived\"]]\n",
    "\n",
    "# Interpretation- The survival among passengers of 1st and 2nd class among Children as well females was high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c797cb2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#7. Post Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb5b61ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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",
       "      <th>Family Size</th>\n",
       "      <th>GenderClass</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",
       "      <td>2</td>\n",
       "      <td>male</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",
       "      <td>2</td>\n",
       "      <td>female</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",
       "      <td>1</td>\n",
       "      <td>female</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",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  Family Size GenderClass  \n",
       "0      0         A/5 21171   7.2500        S            2        male  \n",
       "1      0          PC 17599  71.2833        C            2      female  \n",
       "2      0  STON/O2. 3101282   7.9250        S            1      female  \n",
       "3      0            113803  53.1000        S            2      female  \n",
       "4      0            373450   8.0500        S            1        male  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1536af5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#7.1 Feature Selection\n",
    "#Here we will visualize  the correlation of input features using Heatmap.\n",
    "#If we see a case of correlation we will remove the highly correlated feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "625db1a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.034212</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "      <td>-0.040143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.064910</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>0.016639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.339898</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.065997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.034212</td>\n",
       "      <td>-0.064910</td>\n",
       "      <td>-0.339898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.233296</td>\n",
       "      <td>-0.172482</td>\n",
       "      <td>0.096688</td>\n",
       "      <td>-0.245619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.233296</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.890712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.172482</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>0.783111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096688</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.217138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family Size</th>\n",
       "      <td>-0.040143</td>\n",
       "      <td>0.016639</td>\n",
       "      <td>0.065997</td>\n",
       "      <td>-0.245619</td>\n",
       "      <td>0.890712</td>\n",
       "      <td>0.783111</td>\n",
       "      <td>0.217138</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.034212 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.064910 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.339898  0.083081  0.018443   \n",
       "Age             0.034212 -0.064910 -0.339898  1.000000 -0.233296 -0.172482   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.233296  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.172482  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096688  0.159651  0.216225   \n",
       "Family Size    -0.040143  0.016639  0.065997 -0.245619  0.890712  0.783111   \n",
       "\n",
       "                 Fare  Family Size  \n",
       "PassengerId  0.012658    -0.040143  \n",
       "Survived     0.257307     0.016639  \n",
       "Pclass      -0.549500     0.065997  \n",
       "Age          0.096688    -0.245619  \n",
       "SibSp        0.159651     0.890712  \n",
       "Parch        0.216225     0.783111  \n",
       "Fare         1.000000     0.217138  \n",
       "Family Size  0.217138     1.000000  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3df709bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cor = data.corr()\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(cor, cmap='YlGnBu', annot=True, square=True)\n",
    "plt.show()\n",
    "\n",
    "# cmap gives color to map of yellow green and blue. Annot helps in putting correlation values in each square\n",
    "# and square helps in making the garph more presentable\n",
    "\n",
    "# Interpretation:-\n",
    "\n",
    "# Age and Pclass are negatively corelated with Survived.\n",
    "#FamilySize is made from Parch and SibSb only therefore high positive corelation among them.\n",
    "#Fare and FamilySize are positively coorelated with Survived.\n",
    "#With high corelation we face redundancy issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7a6354e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will drop few features like 'Name','Ticket','SibSp','Parch', 'Ticket', 'PassengerId'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0010c56",
   "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",
       "      <th>Family Size</th>\n",
       "      <th>GenderClass</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",
       "      <td>2</td>\n",
       "      <td>male</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",
       "      <td>2</td>\n",
       "      <td>female</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",
       "      <td>1</td>\n",
       "      <td>female</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",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  Family Size GenderClass  \n",
       "0      0         A/5 21171   7.2500        S            2        male  \n",
       "1      0          PC 17599  71.2833        C            2      female  \n",
       "2      0  STON/O2. 3101282   7.9250        S            1      female  \n",
       "3      0            113803  53.1000        S            2      female  \n",
       "4      0            373450   8.0500        S            1        male  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48fdf9d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(labels= ['PassengerId','Name','Sex','SibSp','Parch','Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4d1b72b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass   Age     Fare Embarked  Family Size GenderClass\n",
       "0         0       3  22.0   7.2500        S            2        male\n",
       "1         1       1  38.0  71.2833        C            2      female\n",
       "2         1       3  26.0   7.9250        S            1      female\n",
       "3         1       1  35.0  53.1000        S            2      female\n",
       "4         0       3  35.0   8.0500        S            1        male"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4df375c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#7.2 Data Encoding\n",
    "#In this section, we will encode our categorical features such as Sex, Embarked, Title using one hot encoding.\n",
    "\n",
    "#Hot encoding is a technique that we use to represent categorical variables as numerical values in a machine learning model. \n",
    "#The advantages of using one hot encoding include: It allows the use of categorical variables in models that \n",
    "#require numerical input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a7a8045d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Fare</th>\n",
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       "      <th>GenderClass_male</th>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass   Age     Fare  Family Size  Embarked_C  Embarked_Q  \\\n",
       "0         0       3  22.0   7.2500            2           0           0   \n",
       "1         1       1  38.0  71.2833            2           1           0   \n",
       "\n",
       "   Embarked_S  GenderClass_child  GenderClass_female  GenderClass_male  \n",
       "0           1                  0                   0                 1  \n",
       "1           0                  0                   1                 0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Performing one hot encoding over sex, embarked, title\n",
    "data = pd.get_dummies(data=data, columns=['Embarked', 'GenderClass'])\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b6cca85",
   "metadata": {},
   "outputs": [],
   "source": [
    "#7.3 Data Preparation\n",
    "#No data scaling is required for Decision Tree as they are giant if-else conditional statements.\n",
    "\n",
    "#Spliting of data into dependent and independent variables for further development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0cc1e30d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass   Age     Fare  Family Size  Embarked_C  Embarked_Q  Embarked_S  \\\n",
       "0       3  22.0   7.2500            2           0           0           1   \n",
       "1       1  38.0  71.2833            2           1           0           0   \n",
       "2       3  26.0   7.9250            1           0           0           1   \n",
       "3       1  35.0  53.1000            2           0           0           1   \n",
       "4       3  35.0   8.0500            1           0           0           1   \n",
       "\n",
       "   GenderClass_child  GenderClass_female  GenderClass_male  \n",
       "0                  0                   0                 1  \n",
       "1                  0                   1                 0  \n",
       "2                  0                   1                 0  \n",
       "3                  0                   1                 0  \n",
       "4                  0                   0                 1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instatiatig input and output data by dropping unnecessary data features\n",
    "X = data.loc[ : ,data.columns!='Survived']\n",
    "\n",
    "X.head()\n",
    "\n",
    "# The loc command The function . loc is typically used for label indexing and \n",
    "#can access multiple columns, while . iloc is used for integer indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d88b2c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = data.Survived\n",
    "\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d41413c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#7.4 Splitting data into test and train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5274e830",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape:  (712, 10)\n",
      "y_train shape:  (712,)\n",
      "X_test shape:  (179, 10)\n",
      "y_test shape:  (179,)\n"
     ]
    }
   ],
   "source": [
    "#Splitting data into testing and training dataset)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=42,  test_size=0.20, stratify=y)    \n",
    "    \n",
    "#Display the shape of training and testing data\n",
    "print('X_train shape: ', X_train.shape)\n",
    "print('y_train shape: ', y_train.shape)\n",
    "print('X_test shape: ', X_test.shape)\n",
    "print('y_test shape: ', y_test.shape)\n",
    "\n",
    "#random_state command helps to split  In other words, it ensures that the same randomization is used each time you run \n",
    "#the code, resulting in the same splits of the data\n",
    "\n",
    "#test_size test_size determines the portion of the data which will go into test sets \n",
    "# and a random state is used for data reproducibility\n",
    "\n",
    "#The stratify command The sole purpose of the argument is to ensure that the same proportion of examples \n",
    "# is preserved across each class of training and testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0a732339",
   "metadata": {},
   "outputs": [],
   "source": [
    "#8. Model Developement and Evaluation\n",
    "\n",
    "#In this section we will develop a decision Tree model and Random Forest\n",
    "#Then we will analyze the results obtained and make our observations.\n",
    "#For evaluation purpose we will focus on Precision, Recall and Accuracy scores.\n",
    "#We will do hyperparameter tuning and see if the scores improves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9a275cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#8.1 Base model using random forest classifier\n",
    "\n",
    "clf = RandomForestClassifier(random_state=42,class_weight='balanced')\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "#predicting training and testing labels\n",
    "y_train_pred_count = clf.predict(X_train)\n",
    "y_test_pred_count = clf.predict(X_test)\n",
    "\n",
    "#plotting confusion matrix of test and train data\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, sharex=False, figsize=(15, 7))\n",
    "plot_confusion_matrix(estimator=clf, X=X_train, y_true=y_train, values_format='.5g', cmap='YlGnBu', ax=ax1)\n",
    "plot_confusion_matrix(estimator=clf, X=X_test, y_true=y_test, values_format='.5g', cmap='YlGnBu', ax=ax2)\n",
    "ax1.set_title(label='Train Data', size=14)\n",
    "ax2.set_title(label='Test Data', size=14)\n",
    "ax1.grid(b=False)\n",
    "ax2.grid(b=False)\n",
    "plt.suptitle(t='Confusion Matrix', size=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa1ee2c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Data:\n",
    "#Model predicted 433 instances correctly for negative class while 266 instances were predicted correctly for positive class.\n",
    "#Model identified 6 instances negative but in actual they were positive.\n",
    "#Model identified 7 instances positive but in actual they were negative.\n",
    "#Test Data:\n",
    "#Model predicted 97 instances correctly for negative class while 49 instances were predicted correctly for positive class.\n",
    "#Model identified 13 instance negative but in actual it was positive.\n",
    "#Model identified 20 instances positive but in actual they were negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7554892e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Training Report          \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.99       439\n",
      "           1       0.98      0.97      0.98       273\n",
      "\n",
      "    accuracy                           0.98       712\n",
      "   macro avg       0.98      0.98      0.98       712\n",
      "weighted avg       0.98      0.98      0.98       712\n",
      "\n",
      "                    Testing Report           \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.88      0.85       110\n",
      "           1       0.79      0.71      0.75        69\n",
      "\n",
      "    accuracy                           0.82       179\n",
      "   macro avg       0.81      0.80      0.80       179\n",
      "weighted avg       0.81      0.82      0.81       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_report = classification_report(y_train, y_train_pred_count)\n",
    "test_report = classification_report(y_test, y_test_pred_count)\n",
    "print('                    Training Report          ')\n",
    "print(train_report)\n",
    "print('                    Testing Report           ')\n",
    "print(test_report)\n",
    "\n",
    "#The model shwons accuracy of 98% in train data and 82% in test data.\n",
    "#Difference in train and test accuracy is more than 5% so the model is overfitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "61ca4c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Feature Importance\n",
    "\n",
    "features = X_train.columns\n",
    "importances = clf.feature_importances_\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "plt.figure(figsize=[15, 7])\n",
    "plt.barh(y=range(len(indices)), width=importances[indices], color='g', align='center')\n",
    "plt.yticks(ticks=range(len(indices)), labels=[features[i] for i in indices], size=12)\n",
    "plt.grid(b=True)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#Feature importance refers to techniques that assign a score to input features based on how \n",
    "#useful they are at predicting a target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f04eccc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#8.2 Hyperparameter Optimized Model Development & Evaluation\n",
    "#Here we will develop random forest classifier using some hyperparameter setting.\n",
    "#We will be applying RandomizedsearchCV method for exhaustive search over specified parameter values of estimator.\n",
    "#To know more about the different parameters in random forest classifier, refer the documentation.\n",
    "#Below we will apply gridsearch over the following parameters:\n",
    "#criterion\n",
    "#max_depth\n",
    "#n_estimators\n",
    "#min_samples_split\n",
    "#min_samples_leaf\n",
    "#You can change other parameters also and compare the impact of it via calculating accuracy score & confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "64237c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Specify parameters and distributions to sample from\n",
    "param_dist = {\"max_depth\": range(2,5),\n",
    "              \"min_samples_split\": sp_randint(2,11),\n",
    "              \"min_samples_leaf\": sp_randint(1,11),\n",
    "              \"bootstrap\": [True,False],\n",
    "              \"n_estimators\": [100, 400, 700, 1000, 1500],\n",
    "              'max_features': ['sqrt','log2', None]\n",
    "}\n",
    "\n",
    "\n",
    "#run randomized search\n",
    "\n",
    "random_search = RandomizedSearchCV(estimator=clf,\n",
    "                                  param_distributions=param_dist,\n",
    "                                  n_iter=50,\n",
    "                                  n_jobs=-1)\n",
    "\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "#Predicting training and testing labels\n",
    "y_train_pred_count = random_search.predict(X_train)\n",
    "y_test_pred_count = random_search.predict(X_test)\n",
    "\n",
    "\n",
    "# Plotting confusion maxtrix of train and test data\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, sharex=False, figsize=(15, 7))\n",
    "plot_confusion_matrix(estimator=random_search, X=X_train, y_true=y_train, values_format='.5g', cmap='YlGnBu', ax=ax1)\n",
    "plot_confusion_matrix(estimator=random_search, X=X_test, y_true=y_test, values_format='.5g', cmap='YlGnBu', ax=ax2)\n",
    "ax1.set_title(label='Train Data', size=14)\n",
    "ax2.set_title(label='Test Data', size=14)\n",
    "ax1.grid(b=False)\n",
    "ax2.grid(b=False)\n",
    "plt.suptitle(t='Confusion Matrix', size=16)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "00710c38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Training Report          \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.90      0.87       439\n",
      "           1       0.81      0.73      0.77       273\n",
      "\n",
      "    accuracy                           0.83       712\n",
      "   macro avg       0.83      0.81      0.82       712\n",
      "weighted avg       0.83      0.83      0.83       712\n",
      "\n",
      "                    Testing Report           \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.89      0.86       110\n",
      "           1       0.81      0.72      0.76        69\n",
      "\n",
      "    accuracy                           0.83       179\n",
      "   macro avg       0.82      0.81      0.81       179\n",
      "weighted avg       0.83      0.83      0.82       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_report = classification_report(y_train, y_train_pred_count)\n",
    "test_report = classification_report(y_test, y_test_pred_count)\n",
    "print('                    Training Report          ')\n",
    "print(train_report)\n",
    "print('                    Testing Report           ')\n",
    "print(test_report)\n",
    "\n",
    "#The accuracy score of train and test data improved. \n",
    "#We can observe that our hyperoptimized model results have improved from the baseline model resuls.\n",
    "#Now, our model is generalizing very well i.e same result on test set as that of train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "384a4c2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#9 Interpretation\n",
    "#We have seen the impact of various factors such as Gender, Age, Port of Embarkment, FamilySize on the rate of survival.\n",
    "#Women have a higher chances of survival than men.\n",
    "#We have done feature selection and removed the features which does not seem to have any effect in our objective.\n",
    "#We performed model development using default and custom parater settings using RandomSearchCV.\n",
    "#RandomSearchCV turns out to give better results than baseline model.\n",
    "\n",
    "\n",
    "#Modelling has shown that by adding several criterias like Gini, entropy, which help in selection of attribute\n",
    "# of splitting, other decision tree pruning methods for managing overfitting like\n",
    "# maximum depth, minimum sample leaf and minimum sample split, bootstrap, n_estimators,maximum featuers managed to \n",
    "#improve the difference between the accuracy score of test as well as train data\n",
    "#But for final interpretation on which model fits best we need statistical tests like AUC\n",
    "# for interpretation.\n"
   ]
  }
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