Keerthiga_Sekar_ML2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "p1kBn0xM_JGm",
   "metadata": {
    "id": "p1kBn0xM_JGm"
   },
   "source": [
    "# Transport Preference Analysis Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yZvo8CHcetWN",
   "metadata": {
    "id": "yZvo8CHcetWN"
   },
   "source": [
    "## Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "empty-shanghai",
   "metadata": {
    "id": "empty-shanghai"
   },
   "source": [
    "### Context:\n",
    "\n",
    "You are in discussions with ABC Consulting company for providing transport for their employees. For this purpose, you are tasked with understanding how do the employees of ABC Consulting prefer to commute presently (between home and office). Based on the parameters like age, salary, work experience etc. given in the data set ‘Transport.csv’, you are required to predict the preferred mode of transport.\n",
    "\n",
    "### Objective:\n",
    "\n",
    "The project requires you to build several Machine Learning models and compare them so that the model can be finalised to predict the preffered mode of transport\n",
    "\n",
    "\n",
    "### Data Description\n",
    "\n",
    "Age: Age of the Employee in Years\n",
    "\n",
    "Gender: Gender of the Employee\n",
    "\n",
    "Engineer: For Engineer =1 , Non Engineer =0\n",
    "\n",
    "MBA: For MBA =1 , Non-MBA =0\n",
    "\n",
    "Work Exp: Experience in years\n",
    "\n",
    "Salary: Salary in Lakhs per Annum\n",
    "\n",
    "Distance: Distance in km from Home to Office\n",
    "\n",
    "license: If Employee has Driving Licence -1, If not, then 0\n",
    "\n",
    "Transport: Mode of Transport"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6hvxXjHae6S",
   "metadata": {
    "id": "e6hvxXjHae6S"
   },
   "source": [
    "## **Please read the instructions carefully before starting the project.**\n",
    "\n",
    "This is a commented Jupyter IPython Notebook file in which all the instructions and tasks to be performed are mentioned.\n",
    "* Blanks '_______' are provided in the notebook that\n",
    "needs to be filled with an appropriate code to get the correct result. With every '_______' blank, there is a comment that briefly describes what needs to be filled in the blank space.\n",
    "* Identify the task to be performed correctly, and only then proceed to write the required code.\n",
    "* Fill the code wherever asked by the commented lines like \"# write your code here\" or \"# complete the code\". Running incomplete code may throw error.\n",
    "* Please run the codes in a sequential manner from the beginning to avoid any unnecessary errors.\n",
    "* Add the results/observations (wherever mentioned) derived from the analysis in the presentation and submit the same."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Lm7obbsV_RUT",
   "metadata": {
    "id": "Lm7obbsV_RUT"
   },
   "source": [
    "## Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "244c79bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Obtaining dependency information for xgboost from https://files.pythonhosted.org/packages/45/6d/8c1d2570a52db6263d855c3ee3daf8f4bdf4a365cd6610772d6fce5fd904/xgboost-2.0.3-py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.macosx_12_0_x86_64.whl.metadata\n",
      "  Downloading xgboost-2.0.3-py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.macosx_12_0_x86_64.whl.metadata (2.0 kB)\n",
      "Requirement already satisfied: numpy in ./anaconda3/lib/python3.11/site-packages (from xgboost) (1.24.3)\n",
      "Requirement already satisfied: scipy in ./anaconda3/lib/python3.11/site-packages (from xgboost) (1.10.1)\n",
      "Downloading xgboost-2.0.3-py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.macosx_12_0_x86_64.whl (2.2 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: xgboost\n",
      "Successfully installed xgboost-2.0.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "canadian-maple",
   "metadata": {
    "id": "canadian-maple"
   },
   "outputs": [],
   "source": [
    "# this will help in making the Python code more structured automatically (good coding practice)\n",
    "#%load_ext nb_black\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Libraries to help with reading and manipulating data\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Library to split data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# libaries to help with data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Removes the limit for the number of displayed columns\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "# Sets the limit for the number of displayed rows\n",
    "pd.set_option(\"display.max_rows\", 100)\n",
    "\n",
    "\n",
    "# Libraries different ensemble classifiers\n",
    "from sklearn.ensemble import (\n",
    "    BaggingClassifier,\n",
    "    RandomForestClassifier,\n",
    "    AdaBoostClassifier,\n",
    "    GradientBoostingClassifier,\n",
    "    StackingClassifier,\n",
    ")\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Libraries to get different metric scores\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import (\n",
    "    confusion_matrix,\n",
    "    accuracy_score,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    f1_score,\n",
    ")\n",
    "\n",
    "# To tune different models\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thorough-passion",
   "metadata": {
    "id": "thorough-passion"
   },
   "source": [
    "## Importing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "alleged-teaching",
   "metadata": {
    "id": "alleged-teaching"
   },
   "outputs": [],
   "source": [
    "trans = pd.read_csv('Transport.csv') ##  Fill the blank to read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "falling-annual",
   "metadata": {
    "id": "falling-annual"
   },
   "outputs": [],
   "source": [
    "# copying data to another variable to avoid any changes to original data\n",
    "data = trans.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mq-1s9p-_aKl",
   "metadata": {
    "id": "mq-1s9p-_aKl"
   },
   "source": [
    "## Overview of the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aboriginal-wrist",
   "metadata": {
    "id": "aboriginal-wrist"
   },
   "source": [
    "#### View the first and last 5 rows of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "FFCVyejfa2Ea",
   "metadata": {
    "id": "FFCVyejfa2Ea"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
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       "      <th>Engineer</th>\n",
       "      <th>MBA</th>\n",
       "      <th>Work Exp</th>\n",
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       "      <td>14.3</td>\n",
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       "      <td>8.3</td>\n",
       "      <td>3.3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>Male</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>13.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0</td>\n",
       "      <td>Public Transport</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>13.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "      <td>Public Transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>13.4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0</td>\n",
       "      <td>Public Transport</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Engineer  MBA  Work Exp  Salary  Distance  license  \\\n",
       "0   28    Male         0    0         4    14.3       3.2        0   \n",
       "1   23  Female         1    0         4     8.3       3.3        0   \n",
       "2   29    Male         1    0         7    13.4       4.1        0   \n",
       "3   28  Female         1    1         5    13.4       4.5        0   \n",
       "4   27    Male         1    0         4    13.4       4.6        0   \n",
       "\n",
       "          Transport  \n",
       "0  Public Transport  \n",
       "1  Public Transport  \n",
       "2  Public Transport  \n",
       "3  Public Transport  \n",
       "4  Public Transport  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() ##  Complete the code to view top 5 rows of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "BBnRk1Pfa2yI",
   "metadata": {
    "id": "BBnRk1Pfa2yI"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Engineer</th>\n",
       "      <th>MBA</th>\n",
       "      <th>Work Exp</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Distance</th>\n",
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       "      <th>Transport</th>\n",
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       "      <th>439</th>\n",
       "      <td>40</td>\n",
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       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>57.0</td>\n",
       "      <td>21.4</td>\n",
       "      <td>1</td>\n",
       "      <td>Private Transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>44.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>441</th>\n",
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       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>1</td>\n",
       "      <td>Private Transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>37</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>47.0</td>\n",
       "      <td>22.8</td>\n",
       "      <td>1</td>\n",
       "      <td>Private Transport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>39</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>50.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>1</td>\n",
       "      <td>Private Transport</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     Age Gender  Engineer  MBA  Work Exp  Salary  Distance  license  \\\n",
       "439   40   Male         1    0        20    57.0      21.4        1   \n",
       "440   38   Male         1    0        19    44.0      21.5        1   \n",
       "441   37   Male         1    0        19    45.0      21.5        1   \n",
       "442   37   Male         0    0        19    47.0      22.8        1   \n",
       "443   39   Male         1    1        21    50.0      23.4        1   \n",
       "\n",
       "             Transport  \n",
       "439  Private Transport  \n",
       "440  Private Transport  \n",
       "441  Private Transport  \n",
       "442  Private Transport  \n",
       "443  Private Transport  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail() ##  Complete the code to view last 5 rows of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accessory-camel",
   "metadata": {
    "id": "accessory-camel"
   },
   "source": [
    "#### Understand the shape of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "YzmErUYWa_8M",
   "metadata": {
    "id": "YzmErUYWa_8M"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(444, 9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape ##  Complete the code to view dimensions of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "assigned-berkeley",
   "metadata": {
    "id": "assigned-berkeley"
   },
   "source": [
    "#### Check the data types of the columns for the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "FO1wMkbGbuGw",
   "metadata": {
    "id": "FO1wMkbGbuGw"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 444 entries, 0 to 443\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Age        444 non-null    int64  \n",
      " 1   Gender     444 non-null    object \n",
      " 2   Engineer   444 non-null    int64  \n",
      " 3   MBA        444 non-null    int64  \n",
      " 4   Work Exp   444 non-null    int64  \n",
      " 5   Salary     444 non-null    float64\n",
      " 6   Distance   444 non-null    float64\n",
      " 7   license    444 non-null    int64  \n",
      " 8   Transport  444 non-null    object \n",
      "dtypes: float64(2), int64(5), object(2)\n",
      "memory usage: 31.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info() ## Complete the code to data type of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "going-validation",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "going-validation",
    "outputId": "f1eb7350-bba3-4668-e726-e6fb262fe35d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicate values\n",
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "operating-operation",
   "metadata": {
    "id": "operating-operation"
   },
   "source": [
    "- There are no duplicate values in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "standing-horizontal",
   "metadata": {
    "id": "standing-horizontal"
   },
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "american-venue",
   "metadata": {
    "id": "american-venue"
   },
   "source": [
    "#### Let's check the statistical summary of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5E_5qdBScJXo",
   "metadata": {
    "id": "5E_5qdBScJXo"
   },
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>Engineer</th>\n",
       "      <th>MBA</th>\n",
       "      <th>Work Exp</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Distance</th>\n",
       "      <th>license</th>\n",
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       "      <th>count</th>\n",
       "      <td>444.000000</td>\n",
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       "      <td>444.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>27.747748</td>\n",
       "      <td>0.754505</td>\n",
       "      <td>0.252252</td>\n",
       "      <td>6.299550</td>\n",
       "      <td>16.238739</td>\n",
       "      <td>11.323198</td>\n",
       "      <td>0.234234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.416710</td>\n",
       "      <td>0.430866</td>\n",
       "      <td>0.434795</td>\n",
       "      <td>5.112098</td>\n",
       "      <td>10.453851</td>\n",
       "      <td>3.606149</td>\n",
       "      <td>0.423997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.725000</td>\n",
       "      <td>13.425000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age    Engineer         MBA    Work Exp      Salary    Distance  \\\n",
       "count  444.000000  444.000000  444.000000  444.000000  444.000000  444.000000   \n",
       "mean    27.747748    0.754505    0.252252    6.299550   16.238739   11.323198   \n",
       "std      4.416710    0.430866    0.434795    5.112098   10.453851    3.606149   \n",
       "min     18.000000    0.000000    0.000000    0.000000    6.500000    3.200000   \n",
       "25%     25.000000    1.000000    0.000000    3.000000    9.800000    8.800000   \n",
       "50%     27.000000    1.000000    0.000000    5.000000   13.600000   11.000000   \n",
       "75%     30.000000    1.000000    1.000000    8.000000   15.725000   13.425000   \n",
       "max     43.000000    1.000000    1.000000   24.000000   57.000000   23.400000   \n",
       "\n",
       "          license  \n",
       "count  444.000000  \n",
       "mean     0.234234  \n",
       "std      0.423997  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      0.000000  \n",
       "max      1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe() ##  Complete the code to print the statistical summary of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cutting-bookmark",
   "metadata": {
    "id": "cutting-bookmark"
   },
   "source": [
    "#### Let's check the count of each unique category in each of the categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "minute-helmet",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "minute-helmet",
    "outputId": "ee7ace2b-db52-4268-ce58-3ed79488d725"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male      316\n",
      "Female    128\n",
      "Name: Gender, dtype: int64\n",
      "--------------------------------------------------\n",
      "Public Transport     300\n",
      "Private Transport    144\n",
      "Name: Transport, dtype: int64\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Making a list of all catrgorical variables\n",
    "cat_col = list(data.select_dtypes(\"object\").columns)\n",
    "\n",
    "# Printing number of count of each unique value in each column\n",
    "for column in cat_col:\n",
    "    print(data[column].value_counts())\n",
    "    print(\"-\" * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wooden-christian",
   "metadata": {
    "id": "wooden-christian"
   },
   "source": [
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "superb-springfield",
   "metadata": {
    "id": "superb-springfield"
   },
   "outputs": [],
   "source": [
    "def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):\n",
    "    \"\"\"\n",
    "    Boxplot and histogram combined\n",
    "\n",
    "    data: dataframe\n",
    "    feature: dataframe column\n",
    "    figsize: size of figure (default (15,10))\n",
    "    kde: whether to show the density curve (default False)\n",
    "    bins: number of bins for histogram (default None)\n",
    "    \"\"\"\n",
    "    f2, (ax_box2, ax_hist2) = plt.subplots(\n",
    "        nrows=2,  # Number of rows of the subplot grid= 2\n",
    "        sharex=True,  # x-axis will be shared among all subplots\n",
    "        gridspec_kw={\"height_ratios\": (0.25, 0.75)},\n",
    "        figsize=figsize,\n",
    "    )  # creating the 2 subplots\n",
    "    sns.boxplot(\n",
    "        data=data, x=feature, ax=ax_box2, showmeans=True, color=\"violet\"\n",
    "    )  # boxplot will be created and a triangle will indicate the mean value of the column\n",
    "    sns.histplot(\n",
    "        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins\n",
    "    ) if bins else sns.histplot(\n",
    "        data=data, x=feature, kde=kde, ax=ax_hist2\n",
    "    )  # For histogram\n",
    "    ax_hist2.axvline(\n",
    "        data[feature].mean(), color=\"green\", linestyle=\"--\"\n",
    "    )  # Add mean to the histogram\n",
    "    ax_hist2.axvline(\n",
    "        data[feature].median(), color=\"black\", linestyle=\"-\"\n",
    "    )  # Add median to the histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comic-inventory",
   "metadata": {
    "id": "comic-inventory"
   },
   "source": [
    "#### Observations on age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "loaded-commercial",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 706
    },
    "id": "loaded-commercial",
    "outputId": "9f8c76ea-0395-4520-c52c-009aee707104"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram_boxplot(data, \"Age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "upset-merit",
   "metadata": {
    "id": "upset-merit"
   },
   "source": [
    "* The distribution of the age is right skewed\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "metric-apparel",
   "metadata": {
    "id": "metric-apparel"
   },
   "source": [
    "#### Observations on work experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "_Jd9tHlTc45S",
   "metadata": {
    "id": "_Jd9tHlTc45S"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram_boxplot(data, 'Work Exp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "proof-drove",
   "metadata": {
    "id": "proof-drove"
   },
   "outputs": [],
   "source": [
    "# function to create labeled barplots\n",
    "\n",
    "\n",
    "def labeled_barplot(data, feature, perc=False, n=None):\n",
    "    \"\"\"\n",
    "    Barplot with percentage at the top\n",
    "\n",
    "    data: dataframe\n",
    "    feature: dataframe column\n",
    "    perc: whether to display percentages instead of count (default is False)\n",
    "    n: displays the top n category levels (default is None, i.e., display all levels)\n",
    "    \"\"\"\n",
    "\n",
    "    total = len(data[feature])  # length of the column\n",
    "    count = data[feature].nunique()\n",
    "    if n is None:\n",
    "        plt.figure(figsize=(count + 2, 6))\n",
    "    else:\n",
    "        plt.figure(figsize=(n + 2, 6))\n",
    "\n",
    "    plt.xticks(rotation=90, fontsize=15)\n",
    "    ax = sns.countplot(\n",
    "        data=data,\n",
    "        x=feature,\n",
    "        palette=\"Paired\",\n",
    "        order=data[feature].value_counts().index[:n],\n",
    "    )\n",
    "\n",
    "    for p in ax.patches:\n",
    "        if perc == True:\n",
    "            label = \"{:.1f}%\".format(\n",
    "                100 * p.get_height() / total\n",
    "            )  # percentage of each class of the category\n",
    "        else:\n",
    "            label = p.get_height()  # count of each level of the category\n",
    "\n",
    "        x = p.get_x() + p.get_width() / 2  # width of the plot\n",
    "        y = p.get_height()  # height of the plot\n",
    "\n",
    "        ax.annotate(\n",
    "            label,\n",
    "            (x, y),\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            size=12,\n",
    "            xytext=(0, 5),\n",
    "            textcoords=\"offset points\",\n",
    "        )  # annotate the percentage\n",
    "\n",
    "    plt.show()  # show the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bulgarian-parts",
   "metadata": {
    "id": "bulgarian-parts"
   },
   "source": [
    "#### Observations on Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "artistic-accounting",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 603
    },
    "id": "artistic-accounting",
    "outputId": "2b101104-7c5b-496b-d373-064cda81cd46"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labeled_barplot(data, \"Gender\", perc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "editorial-command",
   "metadata": {
    "id": "editorial-command"
   },
   "source": [
    "#### Observations on Transport preferred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "uk1lp-MwdThX",
   "metadata": {
    "id": "uk1lp-MwdThX"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labeled_barplot(data,'Transport',perc=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equivalent-aging",
   "metadata": {
    "id": "equivalent-aging"
   },
   "source": [
    "### Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "FVTo3VYpd93H",
   "metadata": {
    "id": "FVTo3VYpd93H"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols_list = data.select_dtypes(include=np.number).columns.tolist()\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "sns.heatmap(\n",
    "    data[cols_list].corr(), annot=True, vmin=-1, vmax=1, fmt=\".2f\", cmap=\"Spectral\"\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blond-convertible",
   "metadata": {
    "id": "blond-convertible"
   },
   "source": [
    "**Creating functions that will help us with further analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "adaptive-recipient",
   "metadata": {
    "id": "adaptive-recipient"
   },
   "outputs": [],
   "source": [
    "### function to plot distributions wrt target\n",
    "\n",
    "\n",
    "def distribution_plot_wrt_target(data, predictor, target):\n",
    "\n",
    "    fig, axs = plt.subplots(2, 2, figsize=(12, 10))\n",
    "\n",
    "    target_uniq = data[target].unique()\n",
    "\n",
    "    axs[0, 0].set_title(\"Distribution of target for target=\" + str(target_uniq[0]))\n",
    "    sns.histplot(\n",
    "        data=data[data[target] == target_uniq[0]],\n",
    "        x=predictor,\n",
    "        kde=True,\n",
    "        ax=axs[0, 0],\n",
    "        color=\"teal\",\n",
    "        stat=\"density\",\n",
    "    )\n",
    "\n",
    "    axs[0, 1].set_title(\"Distribution of target for target=\" + str(target_uniq[1]))\n",
    "    sns.histplot(\n",
    "        data=data[data[target] == target_uniq[1]],\n",
    "        x=predictor,\n",
    "        kde=True,\n",
    "        ax=axs[0, 1],\n",
    "        color=\"orange\",\n",
    "        stat=\"density\",\n",
    "    )\n",
    "\n",
    "    axs[1, 0].set_title(\"Boxplot w.r.t target\")\n",
    "    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette=\"gist_rainbow\")\n",
    "\n",
    "    axs[1, 1].set_title(\"Boxplot (without outliers) w.r.t target\")\n",
    "    sns.boxplot(\n",
    "        data=data,\n",
    "        x=target,\n",
    "        y=predictor,\n",
    "        ax=axs[1, 1],\n",
    "        showfliers=False,\n",
    "        palette=\"gist_rainbow\",\n",
    "    )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "third-sheriff",
   "metadata": {
    "id": "third-sheriff"
   },
   "outputs": [],
   "source": [
    "def stacked_barplot(data, predictor, target):\n",
    "    \"\"\"\n",
    "    Print the category counts and plot a stacked bar chart\n",
    "\n",
    "    data: dataframe\n",
    "    predictor: independent variable\n",
    "    target: target variable\n",
    "    \"\"\"\n",
    "    count = data[predictor].nunique()\n",
    "    sorter = data[target].value_counts().index[-1]\n",
    "    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(\n",
    "        by=sorter, ascending=False\n",
    "    )\n",
    "    print(tab1)\n",
    "    print(\"-\" * 120)\n",
    "    tab = pd.crosstab(data[predictor], data[target], normalize=\"index\").sort_values(\n",
    "        by=sorter, ascending=False\n",
    "    )\n",
    "    tab.plot(kind=\"bar\", stacked=True, figsize=(count + 5, 5))\n",
    "    plt.legend(\n",
    "        loc=\"lower left\", frameon=False,\n",
    "    )\n",
    "    plt.legend(loc=\"upper left\", bbox_to_anchor=(1, 1))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dressed-excuse",
   "metadata": {
    "id": "dressed-excuse"
   },
   "source": [
    "#### Let's find out if Gender  has any impact on Transport Preference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "kneEFeszecFn",
   "metadata": {
    "id": "kneEFeszecFn"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transport  Private Transport  Public Transport  All\n",
      "Gender                                             \n",
      "All                      144               300  444\n",
      "Male                      93               223  316\n",
      "Female                    51                77  128\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stacked_barplot(data, 'Gender', 'Transport')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ofYxeRyTew8A",
   "metadata": {
    "id": "ofYxeRyTew8A"
   },
   "source": [
    "Checking if the Work Experience  of the employee has any relation with preference of the transport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "Qv0NOZqUiIZ4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "id": "Qv0NOZqUiIZ4",
    "outputId": "ddb618c1-f6ef-4213-9b5d-05f3ae8aa10d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.boxplot(data=data, x=\"Work Exp\", y=\"Transport\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "changing-kansas",
   "metadata": {
    "id": "changing-kansas"
   },
   "source": [
    "#### Checking if the Age of the employee has any relation with preference of the Transport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "FiFNuWbIejTd",
   "metadata": {
    "id": "FiFNuWbIejTd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "sns.boxplot(data=data, x='Age', y='Transport')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qBWlk20UBUAx",
   "metadata": {
    "id": "qBWlk20UBUAx"
   },
   "source": [
    "## Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "allied-association",
   "metadata": {
    "id": "allied-association"
   },
   "source": [
    "### Outlier Check\n",
    "\n",
    "- Let's check for outliers in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8B40YuUMfmSK",
   "metadata": {
    "id": "8B40YuUMfmSK",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1200 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outlier detection using boxplot\n",
    "numeric_columns = data.select_dtypes(include=np.number).columns.tolist()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(15, 12))\n",
    "\n",
    "for i, variable in enumerate(numeric_columns):\n",
    "    plt.subplot(3, 3, i + 1)\n",
    "    sns.boxplot(data[variable])\n",
    "    plt.title(variable)\n",
    " ## Complete the code to create boxplots for all the numeric columns\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "flexible-independence",
   "metadata": {
    "id": "flexible-independence"
   },
   "source": [
    "### Data Preparation for modeling\n",
    "\n",
    "- We want to predict which visa will be certified.\n",
    "- Before we proceed to build a model, we'll have to encode categorical features.\n",
    "- We'll split the data into train and test to be able to evaluate the model that we build on the train data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "htJpQOE4f2SP",
   "metadata": {
    "id": "htJpQOE4f2SP"
   },
   "outputs": [],
   "source": [
    "data[\"Gender\"] = data[\"Gender\"].apply(lambda x: 1 if x == \"Male\" else 0) # encode the object type of data into numerical\n",
    "data[\"Transport\"]= data[\"Transport\"].apply(lambda x: 1 if x == \"Public Transport\" else 0) # encode the object type of data into numerical\n",
    "\n",
    "X = data.drop(columns=[\"Transport\"]) ## Complete the code to drop Transport from the data\n",
    "Y = data[\"Transport\"]\n",
    "\n",
    "\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "\n",
    "# Splitting data in train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,Y, test_size=0.30, random_state=1, stratify=Y\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "empty-typing",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "empty-typing",
    "outputId": "0fb45b2d-f8df-48e0-f89b-0b211e82387e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Training set :  (310, 8)\n",
      "Shape of test set :  (134, 8)\n",
      "Percentage of classes in training set:\n",
      "1    0.674194\n",
      "0    0.325806\n",
      "Name: Transport, dtype: float64\n",
      "Percentage of classes in test set:\n",
      "1    0.679104\n",
      "0    0.320896\n",
      "Name: Transport, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of Training set : \", X_train.shape)\n",
    "print(\"Shape of test set : \", X_test.shape)\n",
    "print(\"Percentage of classes in training set:\")\n",
    "print(y_train.value_counts(normalize=True))\n",
    "print(\"Percentage of classes in test set:\")\n",
    "print(y_test.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dr7q6-dbfiQB",
   "metadata": {
    "id": "dr7q6-dbfiQB"
   },
   "source": [
    "## Model evaluation criterion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "foreign-yemen",
   "metadata": {
    "id": "foreign-yemen"
   },
   "source": [
    "**Model can make wrong predictions as**:\n",
    "\n",
    "1. Model predicts that the Public mode of transport is preffered but employee prefers private mode\n",
    "2. Model predicts that that the Private mode of transport is preffered but employee prefers public mode\n",
    "\n",
    "**Which case is more important?**\n",
    "\n",
    "Both are important to correctly estimate the number of employees preffering the private transport\n",
    "\n",
    "**How to reduce the losses?**\n",
    "\n",
    "* `F1 Score` can be used a the metric for evaluation of the model, greater the F1  score higher are the chances of minimizing False Negatives and False Positives.\n",
    "* We will use balanced class weights so that model focuses equally on both classes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "capital-charlotte",
   "metadata": {
    "id": "capital-charlotte"
   },
   "source": [
    "First, let's create functions to calculate different metrics and confusion matrix so that we don't have to use the same code repeatedly for each model.\n",
    "* The model_performance_classification_sklearn function will be used to check the model performance of models.\n",
    "* The confusion_matrix_sklearn function will be used to plot the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "mexican-database",
   "metadata": {
    "id": "mexican-database"
   },
   "outputs": [],
   "source": [
    "# defining a function to compute different metrics to check performance of a classification model built using sklearn\n",
    "\n",
    "\n",
    "def model_performance_classification_sklearn(model, predictors, target):\n",
    "    \"\"\"\n",
    "    Function to compute different metrics to check classification model performance\n",
    "\n",
    "    model: classifier\n",
    "    predictors: independent variables\n",
    "    target: dependent variable\n",
    "    \"\"\"\n",
    "\n",
    "    # predicting using the independent variables\n",
    "    pred = model.predict(predictors)\n",
    "\n",
    "    acc = accuracy_score(target, pred)  # to compute Accuracy\n",
    "    recall = recall_score(target, pred)  # to compute Recall\n",
    "    precision = precision_score(target, pred)  # to compute Precision\n",
    "    f1 = f1_score(target, pred)  # to compute F1-score\n",
    "\n",
    "    # creating a dataframe of metrics\n",
    "    df_perf = pd.DataFrame(\n",
    "        {\"Accuracy\": acc, \"Recall\": recall, \"Precision\": precision, \"F1\": f1,},\n",
    "        index=[0],\n",
    "    )\n",
    "\n",
    "    return df_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "recreational-topic",
   "metadata": {
    "id": "recreational-topic"
   },
   "outputs": [],
   "source": [
    "def confusion_matrix_sklearn(model, predictors, target):\n",
    "    \"\"\"\n",
    "    To plot the confusion_matrix with percentages\n",
    "\n",
    "    model: classifier\n",
    "    predictors: independent variables\n",
    "    target: dependent variable\n",
    "    \"\"\"\n",
    "    y_pred = model.predict(predictors)\n",
    "    cm = confusion_matrix(target, y_pred)\n",
    "    labels = np.asarray(\n",
    "        [\n",
    "            [\"{0:0.0f}\".format(item) + \"\\n{0:.2%}\".format(item / cm.flatten().sum())]\n",
    "            for item in cm.flatten()\n",
    "        ]\n",
    "    ).reshape(2, 2)\n",
    "\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.heatmap(cm, annot=labels, fmt=\"\")\n",
    "    plt.ylabel(\"True label\")\n",
    "    plt.xlabel(\"Predicted label\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Pe39R57-Bpld",
   "metadata": {
    "id": "Pe39R57-Bpld"
   },
   "source": [
    "## Bagging - Model Building and Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aging-corruption",
   "metadata": {
    "id": "aging-corruption"
   },
   "source": [
    "### Bagging Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "jTiu1dLDgRw4",
   "metadata": {
    "id": "jTiu1dLDgRw4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "BaggingClassifier(random_state=1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging_classifier = BaggingClassifier(random_state=1)\n",
    "bagging_classifier.fit(X_train, y_train) ## Complete the code to fit bagging classifier on the train data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suffering-growing",
   "metadata": {
    "id": "suffering-growing"
   },
   "source": [
    "#### Checking model performance on training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "MHszf1mbgf8N",
   "metadata": {
    "id": "MHszf1mbgf8N"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix_sklearn(bagging_classifier, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "GH0-ZnDylUeT",
   "metadata": {
    "id": "GH0-ZnDylUeT"
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.996774</td>\n",
       "      <td>0.995215</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.997602</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy    Recall  Precision        F1\n",
       "0  0.996774  0.995215        1.0  0.997602"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging_classifier_model_train_perf = model_performance_classification_sklearn(\n",
    "    bagging_classifier, X_train, y_train\n",
    ")\n",
    "bagging_classifier_model_train_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "verified-obligation",
   "metadata": {
    "id": "verified-obligation"
   },
   "source": [
    "#### Checking model performance on test set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "QPZ33Dygldb_",
   "metadata": {
    "id": "QPZ33Dygldb_"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix_sklearn(bagging_classifier, X_test, y_test)## Complete the code to create confusion matrix for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "FlcrZjObleGD",
   "metadata": {
    "id": "FlcrZjObleGD"
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.813433</td>\n",
       "      <td>0.89011</td>\n",
       "      <td>0.84375</td>\n",
       "      <td>0.86631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy   Recall  Precision       F1\n",
       "0  0.813433  0.89011    0.84375  0.86631"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging_classifier_model_test_perf = model_performance_classification_sklearn(bagging_classifier, X_test, y_test) ## Complete the code to check performance for test data\n",
    "bagging_classifier_model_test_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "increased-civilian",
   "metadata": {
    "id": "increased-civilian"
   },
   "source": [
    "### Hyperparameter Tuning - Bagging Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "jEsJzz3EluIP",
   "metadata": {
    "id": "jEsJzz3EluIP"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.5 s, sys: 79.1 ms, total: 13.6 s\n",
      "Wall time: 13.6 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>BaggingClassifier(max_features=0.5, max_samples=0.8, n_estimators=100,\n",
       "                  random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(max_features=0.5, max_samples=0.8, n_estimators=100,\n",
       "                  random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "BaggingClassifier(max_features=0.5, max_samples=0.8, n_estimators=100,\n",
       "                  random_state=1)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Choose the type of classifier.\n",
    "bagging_estimator_tuned = BaggingClassifier(random_state=1)\n",
    "\n",
    "# Grid of parameters to choose from\n",
    "parameters = {\n",
    "    \"max_samples\": [0.7, 0.8],\n",
    "    \"max_features\": [0.5,0.7,1],\n",
    "    \"n_estimators\": np.arange(50,110,25),\n",
    "}\n",
    "\n",
    "# Type of scoring used to compare parameter combinations\n",
    "acc_scorer = metrics.make_scorer(metrics.f1_score)\n",
    "\n",
    "# Run the grid search\n",
    "grid_obj = GridSearchCV(bagging_estimator_tuned, parameters, scoring=acc_scorer, cv=5)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "bagging_estimator_tuned = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data.\n",
    "bagging_estimator_tuned.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "protecting-douglas",
   "metadata": {
    "id": "protecting-douglas"
   },
   "source": [
    "#### Checking model performance on training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "R91FHzuZl4e0",
   "metadata": {
    "id": "R91FHzuZl4e0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict on the training set\n",
    "y_train_pred_tuned = bagging_estimator_tuned.predict(X_train)\n",
    "\n",
    "# Create a confusion matrix\n",
    "confusion_matrix_sklearn(bagging_estimator_tuned, X_train, y_train)\n",
    " ## Complete the code to create confusion matrix for train data on tuned estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eIap0n4ol4cL",
   "metadata": {
    "id": "eIap0n4ol4cL"
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.993548</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.990521</td>\n",
       "      <td>0.995238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy  Recall  Precision        F1\n",
       "0  0.993548     1.0   0.990521  0.995238"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging_estimator_tuned_model_train_perf = model_performance_classification_sklearn(bagging_estimator_tuned, X_train, y_train) ## Complete the code to check performance for train data on tuned estimator\n",
    "bagging_estimator_tuned_model_train_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elegant-bookmark",
   "metadata": {
    "id": "elegant-bookmark"
   },
   "source": [
    "#### Checking model performance on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "O9peA4hKl_Um",
   "metadata": {
    "id": "O9peA4hKl_Um"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bagging_estimator_tuned_model_test_perf = confusion_matrix_sklearn(bagging_estimator_tuned, X_test, y_test)\n",
    "bagging_estimator_tuned_model_test_perf\n",
    " ## Complete the code to create confusion matrix for test data on tuned estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "MtOU_tVtl_R8",
   "metadata": {
    "id": "MtOU_tVtl_R8"
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.820896</td>\n",
       "      <td>0.967033</td>\n",
       "      <td>0.807339</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy    Recall  Precision    F1\n",
       "0  0.820896  0.967033   0.807339  0.88"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging_estimator_tuned_model_test_perf = model_performance_classification_sklearn(bagging_estimator_tuned, X_test, y_test) ## Complete the code to check performance for test data on tuned estimator\n",
    "bagging_estimator_tuned_model_test_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "colonial-society",
   "metadata": {
    "id": "colonial-society"
   },
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "BSIxGIUjmH_t",
   "metadata": {
    "id": "BSIxGIUjmH_t"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', random_state=1)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the model\n",
    "rf_estimator = RandomForestClassifier(random_state=1, class_weight='balanced') ## Complete the code to define random forest with random state = 1 and class_weight = balanced\n",
    "rf_estimator.fit(X_train, y_train) ## Complete the code to fit random forest on the train data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "possible-vinyl",
   "metadata": {
    "id": "possible-vinyl"
   },
   "source": [
    "#### Checking model performance on training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "KLyHnpeOmOk3",
   "metadata": {
    "id": "KLyHnpeOmOk3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix_sklearn(rf_estimator, X_train, y_train) ## Complete the code to create confusion matrix for train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "AvgB2qmImOVV",
   "metadata": {
    "id": "AvgB2qmImOVV"
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy  Recall  Precision   F1\n",
       "0       1.0     1.0        1.0  1.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating different metrics\n",
    "rf_estimator_model_train_perf = model_performance_classification_sklearn(rf_estimator, X_train, y_train) ## Complete the code to check performance on train data\n",
    "rf_estimator_model_train_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "theoretical-poverty",
   "metadata": {
    "id": "theoretical-poverty"
   },
   "source": [
    "#### Checking model performance on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "yLMuLgsJmYc4",
   "metadata": {
    "id": "yLMuLgsJmYc4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix_sklearn(rf_estimator, X_test, y_test) ## Complete the code to create confusion matrix for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5qV4AeyPmZEG",
   "metadata": {
    "id": "5qV4AeyPmZEG"
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.828358</td>\n",
       "      <td>0.956044</td>\n",
       "      <td>0.820755</td>\n",
       "      <td>0.883249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy    Recall  Precision        F1\n",
       "0  0.828358  0.956044   0.820755  0.883249"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_estimator_model_test_perf = model_performance_classification_sklearn(rf_estimator, X_test, y_test) ## Complete the code to check performance for test data\n",
    "rf_estimator_model_test_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beneficial-bosnia",
   "metadata": {
    "id": "beneficial-bosnia"
   },
   "source": [
    "### Hyperparameter Tuning - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "CanzCvoBmhGd",
   "metadata": {
    "id": "CanzCvoBmhGd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=200, oob_score=True, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=200, oob_score=True, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=200, oob_score=True, random_state=1)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose the type of classifier.\n",
    "rf_tuned = RandomForestClassifier(random_state=1, oob_score=True, bootstrap=True)\n",
    "\n",
    "parameters = {\n",
    "    'n_estimators': [100, 200],  \n",
    "    'max_depth': [None, 10, 20],    # minimum two hyperparameters to be used for tuning\n",
    "}\n",
    "\n",
    "# Type of scoring used to compare parameter combinations\n",
    "acc_scorer = metrics.make_scorer(metrics.f1_score)\n",
    "\n",
    "# Run the grid search\n",
    "grid_obj = GridSearchCV(rf_tuned, parameters, scoring=acc_scorer, cv=5, n_jobs=-1) ## Complete the code to run grid search with cv = 5 and n_jobs = -1\n",
    "grid_obj = grid_obj.fit(X_train, y_train) ## Complete the code to fit the grid_obj on the train data\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "rf_tuned = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data.\n",
    "rf_tuned.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "joint-appendix",
   "metadata": {
    "id": "joint-appendix"
   },
   "source": [
    "#### Checking model performance on training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "scLNOYbCm8JS",
   "metadata": {
    "id": "scLNOYbCm8JS"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zCKL6fqNy/M6MH9HwjwUwqmKNrIeqbyRTIeR/O99/mXvUxuND5/Hk8AUAgOJ+Eqx93keFhjUBACa1qiDj+j2knH6xliPjeixMXKoi48Z9VBndA6lh15AWeVuDKyZ6YeKE4dgQvB0bgn8CAEz2n4VOndpg5IhPMWPm/Hz1R3w+CLH3H2Ky/ywAwI0bt+Du3hCTJ45UJv7x44fh8OETWLBwJQBgwcKVaP2BJ8aPH4aBg8aghrMTUlKe4Zdf9gIAjh0/g7p1XXDg4F/o188HWdnZ2L37oC4un6hA78QYqqGhIezt7WFvb8+kr216erDu0Qr6psZ4FvFPgVX0zU0h5OUhNyX9rU3pmxnDPXw1PC6sRd0t02Hm6qxyPPXcdVh84AbjGi/WapjWc0LF5nXw5K8XXwQyrsfCuIY9jKpYQ1rVGiY17JFxIxbG1WWw7dsWsfN/KoELJrEyNDREkyYNEHr4uEp5aOhxtPD0KPAcz+buCA1Vrf9n6DG4uzeAgYHBqzqHT7xW51Wb0bdiYGpqgkaN6qNy5UrwcG+IK1euo3LlSgj42h/jJ6gO/VMp09Gz+k+cOAFvb284ODhAIpFg9+7dKseHDBkCiUSisnl6eqrUUSgUGDduHKytrWFmZobu3bvjwYMHal8yH9krEqZ1qqHB/nnQkxohN/05bgxdiMx/8v/BSKSGqD5zIJJ2nkJuWuYb28uIfojoCSuRfj0WBuamsB/+Edz2zkVk+8l4/u+UwsOVu2FQ0RRNTi2DkJsHib4e7gX9hOTdL57PkBn9ELFBP8F1x1cAgHuB25AZ/RD1f/4ad7/ZgkrtGsHRvw+E7FzEfLUBqWHXtfCbofLK2toSBgYGSExQHbVKTEyGncy2wHPsZLZITHytfkIyDA0NYW1tCbk8ETKZDRISVd/Kl5CYBNm/b+97+jQFn/n6IXjDMpgYG2NryK/4M/Q41q9bhO9XBcO5uiN27QyGoaEB5nyzGDt37i/Bqya16WioPz09HQ0bNsRnn32Gjz/+uMA6Xbp0QXBwsHLfyMhI5bifnx/27duH7du3w8rKCpMnT0a3bt0QEREBff2ir21j4heJzNtxiGw/BQYWZrDq2hwuy8fiSs9ZKslfYqCP2msmAhIJ7nyx/q3tpV2IRtqFaOV+6t830DB0Iex9P0LMzA0AAOserWDzcWv8M2oZMm7eh5lrdTjP+QxZCY+R9POLXpV885+Qb/5T2Y5t37bITcvEs/B/0OT0clzqMg1SByvUXjMR4c1GQ8jKKclfC4nA608ll0gk+creXj9/eWFt7tnzB/bs+UO536Z1C7i61sH4CTNw8/ppDBw0BvKEJJw9/TtOngxDUtIjta+LSoiOFul5eXnBy8vrrXWkUilkMlmBx1JSUvDjjz9iy5Yt6NChAwBg69atcHR0xOHDh9G5c+cix/JODPWT9gnZOXh+V460S7dxL3Ab0qPuwWHYR8rjEgN91F43CcbVbBHVd85be/sFf4CAtMjbMKnx6hbM6l8PwoOVu5G85zQybsQi6dcTiFv3O6qO61VgEwaW5nCc1Bt3ZvwI8yYuyLwTh+cxcqScjoLEUB8mNRw0unYSp+Tkx8jJyYHdvz3xl2xsrJCYkFTgOQnyRNjZvVbf1hrZ2dl49OgJAEAuT4LMTnXEwNbGGgkJBa+HMTIywooVgRg9ehree88ZBgYGOHEyDP/8cxv/RN9B82ZNNL1EKgnv0O18x44dg62tLWrVqoXhw4cjMTFReSwiIgLZ2dno1KmTsszBwQGurq5vvTW+IEz8YiWRKG+9Uyb9Gva42mcOcp5ottrYzLU6shKeKPf1TKT55sGE3DxI9CSvnwoAcJ7zGeLW/Y6s+MeAvh4kBq8GpCT6+pDo88+Vii47OxsXLlxGh/atVco7dGiNs2HhBZ4Tdi4CHTqo1u/YoQ0iIi4jJyfnVZ32H7xW581tzpzhh0N/HMXFyKvQ19eDgcGrIVlDQ0Po8e+6VBXnAT4KhQKpqakq2+tPki0qLy8vhISE4MiRI1i0aBHOnz+PDz/8UNmeXC6HkZERKldWvdXazs4Ocrlcrc/iUL8IVJveH0+PXIQiLhn6Ziaw9mkFi5b1EPXJPEBfD7V/8EcFN2dcGxQEiZ4eDG0qAQBynqZByH7xj53LinHIin+Ee4HbAACOk3vjWcQ/yLwT/2KOf9hHMKtfHXemv3pb1ePQcFSd8DEUD5P/Hep3RpWR3ZDw09F8MVq0bgCTGvaIHrcCAJB28RZM3nNApQ8bQ+pgBSE3D5m3899+RfQ2S5atx6bgZYiIuISwcxEY7jsQ1RyrYO26LQCAeXO/gIODPT4b+uK21LXrtmD0qM/w3cJZ+GFDCDybu2PoZ/0wYNAYZZsrVvyIo0d+wxT/0di77xC6e3dG+/YfoE3bnvk+v169Wuj9v+5wb9oRAHDjxm3k5Qn4bEg/JCQkoU7tmggPv6SD3wRpQ1BQEGbPnq1SNmvWLAQEBKjdVt++fZU/u7q6wsPDA05OTti/fz969Sp4lBR4Me0kkRTcmXoTJn4RMLKxgMvKcTCyrYycZxnIuHYPUZ/MQ8qJy5A62sCqS1MAQOMji1TOu9JrFlLPRAEApFWsVR5RaVDRDDW/Gwkjm0rIeZaB9CsxuOrzNdIu3lLWifnyR1Sb1g815g+HoVVFZCU8gXxzKO4v/lXlc/SMjVAz0Bc3RywB/p0nzZI/RsyMDXBZOhp5WTmInrASec+ztPL7ofLrl1/2wsqyMmbOmAh7e1tcjboJ7+6DEBv74lWvMpkdqjm+mkK6e/c+vLsPwnffBWDUqMGIi0uA38SvlbfyAcDZsHD0Hzgac2ZPxeyAKbh95x4+GTAKf5+/mO/z16xaCP8pAcjIeDF19vz5c/gOm4jly+ZBKjXC+AkzERenXm+NSlgxhuynT5+OSZMmqZRJpdLiRgTgxdNtnZycEB39Yi2VTCZDVlYWnjx5otLrT0xMRMuWLdVqWyK8bZVLGXVa9r/SDoFI69o8PlvaIRBpXU7WQ622nzYl/0hNUVX4dpdG50kkEuzatQs+Pj5vrPPo0SNUqVIF69atw6effoqUlBTY2Nhg69atyofbxcfHo2rVqjhw4IBai/vY4yciIvHS0ar+tLQ03Lr1nxHRmBhERkbC0tISlpaWCAgIwMcffwx7e3vcvXsXX375JaytrdGz54svJhYWFvD19cXkyZNhZWUFS0tL+Pv7w83NTbnKv6iY+ImISLx0dB9/eHg42rVrp9x/OUUwePBgrF69GleuXMHmzZvx9OlT2Nvbo127dtixYwfMzc2V5yxZsgQGBgbo06cPMjMz0b59e2zcuFGte/gBDvUTlVkc6icx0PZQ/zM/b43PNV+6rwQj0R3eR0JERCQiHOonIiLx4tv5iIiIRESEr/1m4iciIvFij5+IiEhEmPiJiIjEoxze2FYoruonIiISEfb4iYhIvDjUT0REJCJM/EREROIhMPETERGJCBM/ERGRiIjv+T1c1U9ERCQm7PETEZFocY6fiIhITJj4iYiIRESEc/xM/EREJFoc6iciIhITEfb4uaqfiIhIRNjjJyIi0eJQPxERkZiIcKifiZ+IiERLYOInIiISESZ+IiIi8RBjj5+r+omIiESEPX4iIhIv9viJiIjEQ8jTfFPHiRMn4O3tDQcHB0gkEuzevVt5LDs7G9OmTYObmxvMzMzg4OCATz/9FHFxcSpttG3bFhKJRGXr16+f2tfMxE9ERKKlq8Sfnp6Ohg0bYuXKlfmOZWRk4MKFC/jqq69w4cIF7Ny5E//88w+6d++er+7w4cMRHx+v3NauXav2NXOon4iIREtXi/u8vLzg5eVV4DELCwuEhoaqlK1YsQLNmjVDbGwsqlWrpiw3NTWFTCYrVixFSvzLly8vcoPjx4/XOBgiIiKdEiSlHUGBUlJSIJFIUKlSJZXykJAQbN26FXZ2dvDy8sKsWbNgbm6uVttFSvxLliwpUmMSiYSJn4iIREGhUEChUKiUSaVSSKXSYrX7/PlzfPHFF+jfvz8qVqyoLB8wYACcnZ0hk8lw9epVTJ8+HZcuXco3WlCYIiX+mJgY9aImIiIqA4oz1B8UFITZs2erlM2aNQsBAQEat5mdnY1+/fohLy8Pq1atUjk2fPhw5c+urq5wcXGBh4cHLly4gCZNmhT5MzSe48/KykJMTAxq1qwJAwMuFSAiorJHyNN8qH/69OmYNGmSSllxevvZ2dno06cPYmJicOTIEZXefkGaNGkCQ0NDREdHq5X41V7Vn5GRAV9fX5iamqJ+/fqIjY0F8GJuf/78+eo2R0REVGqKs6pfKpWiYsWKKpumif9l0o+Ojsbhw4dhZWVV6DlRUVHIzs6Gvb29Wp+lduJ/Oadw7NgxGBsbK8s7dOiAHTt2qNscERFRqREEicabOtLS0hAZGYnIyEgAL6bQIyMjERsbi5ycHPzvf/9DeHg4QkJCkJubC7lcDrlcjqysLADA7du3MWfOHISHh+Pu3bs4cOAAevfujcaNG6NVq1ZqxaL2GP3u3buxY8cOeHp6QiJ5deH16tXD7du31W2OiIio1Ojqdr7w8HC0a9dOuf9yimDw4MEICAjA3r17AQCNGjVSOe/o0aNo27YtjIyM8Ndff2HZsmVIS0uDo6MjunbtilmzZkFfX1+tWNRO/ElJSbC1tc1Xnp6ervJFgIiIiF5o27YtBEF44/G3HQMAR0dHHD9+vERiUXuov2nTpti/f79y/2WyX79+PVq0aFEiQREREemCkCfReCur1O7xBwUFoUuXLrh27RpycnKwbNkyREVF4ezZsyX2bYSIiEgXCulol0tq9/hbtmyJ06dPIyMjAzVr1sSff/4JOzs7nD17Fu7u7tqIkYiISCvY4y8iNzc3bNq0qaRjISIi0qmynMA1pVHiz83Nxa5du3D9+nVIJBLUrVsXPXr04IN8iIioTBHjUL/amfrq1avo0aMH5HI5ateuDQD4559/YGNjg71798LNza3EgyQiIqKSofYc/7Bhw1C/fn08ePAAFy5cwIULF3D//n00aNAAn3/+uTZiJCIi0grO8RfBpUuXEB4ejsqVKyvLKleujHnz5qFp06YlGhwREZE2qfsEvvJA7R5/7dq1kZCQkK88MTER7733XokERUREpAvFeVZ/WVWkHn9qaqry58DAQIwfPx4BAQHw9PQEAISFhWHOnDlYsGCBdqIkIiLSgjwR9viLlPgrVaqk8jheQRDQp08fZdnLRw16e3sjNzdXC2ESERGVPDEO9Rcp8R89elTbcRAREZEOFCnxt2nTRttxEBER6VxZXp2vKY2fuJORkYHY2Fjlu4JfatCgQbGDIiIi0gU+wKcIkpKS8Nlnn+HgwYMFHuccPxERlRVi7PGrfTufn58fnjx5grCwMJiYmOCPP/7Apk2b4OLigr1792ojRiIiIq3IEyQab2WV2j3+I0eOYM+ePWjatCn09PTg5OSEjh07omLFiggKCkLXrl21EScRERGVALV7/Onp6bC1tQUAWFpaIikpCcCLN/ZduHChZKMjIiLSIkGQaLyVVRo9ue/mzZsAgEaNGmHt2rV4+PAh1qxZA3t7+xIPkIiISFsEQfOtrFJ7qN/Pzw/x8fEAgFmzZqFz584ICQmBkZERNm7cWNLxERERaU1ZnqvXlNqJf8CAAcqfGzdujLt37+LGjRuoVq0arK2tSzQ4IiIibSrLQ/aa0vg+/pdMTU3RpEmTkoiFiIhIp8rykL2mipT4J02aVOQGFy9erHEwREREpF1FSvwXL14sUmP/fZEPERHRu45z/G9Q1l7S0+bx2dIOgUjrMuNOlnYIRGUe5/iJiIhERIw9frXv4yciIiovhGJs6jhx4gS8vb3h4OAAiUSC3bt3q8YhCAgICICDgwNMTEzQtm1bREVFqdRRKBQYN24crK2tYWZmhu7du+PBgwfqXjITPxERiZeuntWfnp6Ohg0bYuXKlQUeX7hwIRYvXoyVK1fi/PnzkMlk6NixI549e6as4+fnh127dmH79u04deoU0tLS0K1bN7VfjicRhPJ3M4OBUZXSDoFI6zjHT2JgaF1Dq+2fsf9Y43Nbxv+m0XkSiQS7du2Cj48PgBe9fQcHB/j5+WHatGkAXvTu7ezssGDBAowYMQIpKSmwsbHBli1b0LdvXwBAXFwcHB0dceDAAXTu3LnIn88ePxERiVZxntWvUCiQmpqqsikUCrVjiImJgVwuR6dOnZRlUqkUbdq0wZkzZwAAERERyM7OVqnj4OAAV1dXZZ2i0ijxb9myBa1atYKDgwPu3bsHAFi6dCn27NmjSXNERESlIq8YW1BQECwsLFS2oKAgtWOQy+UAADs7O5VyOzs75TG5XA4jIyNUrlz5jXWKSu3Ev3r1akyaNAkfffQRnj59qpxbqFSpEpYuXapuc0RERKVGgETjbfr06UhJSVHZpk+frnEsrz8LRxCEQp+PU5Q6r1M78a9YsQLr16/HjBkzoK+vryz38PDAlStX1G2OiIio1OQJmm9SqRQVK1ZU2aRSqdoxyGQyAMjXc09MTFSOAshkMmRlZeHJkydvrFNUaif+mJgYNG7cOF+5VCpFenq6us0RERGVmjxINN5KirOzM2QyGUJDQ5VlWVlZOH78OFq2bAkAcHd3h6GhoUqd+Ph4XL16VVmnqNR+gI+zszMiIyPh5OSkUn7w4EHUq1dP3eaIiIjKvbS0NNy6dUu5HxMTg8jISFhaWqJatWrw8/NDYGAgXFxc4OLigsDAQJiamqJ///4AAAsLC/j6+mLy5MmwsrKCpaUl/P394ebmhg4dOqgVi9qJf8qUKRgzZgyeP38OQRDw999/46effkJQUBB++OEHdZsjIiIqNUIJ9tzfJjw8HO3atVPuv3z53eDBg7Fx40ZMnToVmZmZGD16NJ48eYLmzZvjzz//hLm5ufKcJUuWwMDAAH369EFmZibat2+PjRs3qky7F4VG9/GvX78ec+fOxf379wEAVapUQUBAAHx9fdVtSit4Hz+JAe/jJzHQ9n38oXZ9NT63Y8KOEoxEd4r1AJ/k5GTk5eXB1ta2JGMqNiZ+EgMmfhIDbSf+P+36aXxup4TtJRiJ7hTrJT3W1tYlFQcREZHO5ZV2AKVAo8V9b7tn8M6dO8UKiIiISFeY+IvAz89PZT87OxsXL17EH3/8gSlTppRUXERERKQFaif+CRMmFFj+/fffIzw8vNgBERER6YquVvW/S0rsJT1eXl747TfN3lRERERUGvIkmm9lVbEW9/3Xr7/+CktLy5JqjoiISOtK8gl8ZYXaib9x48Yqi/sEQYBcLkdSUhJWrVpVosERERFpk8b3s5dhaid+Hx8flX09PT3Y2Nigbdu2qFOnTknFRUREpHVc1V+InJwcVK9eHZ07d1a+TYiIiIjKDrUW9xkYGGDUqFFQKBTaioeIiEhn8iQSjbeySu1V/c2bN8fFixe1EQsREZFOCcXYyiq15/hHjx6NyZMn48GDB3B3d4eZmZnK8QYNGpRYcERERNrEOf63GDp0KJYuXYq+fV+8yWj8+PHKYxKJBIIgQCKRIDc3t+SjJCIi0oKyfD++poqc+Ddt2oT58+cjJiZGm/EQERHpDO/jf4uXb+91cnLSWjBERESkXWrN8b/trXxERERlTVlepKcptRJ/rVq1Ck3+jx8/LlZAREREusI5/kLMnj0bFhYW2oqFiIhIp7iqvxD9+vWDra2ttmIhIiLSKQ71vwXn94mIqLwR41B/kZ/c93JVPxEREZVdRe7x5+WJcSaEiIjKMzFmNrUf2UtERFReMPETERGJiCDCOX4mfiIiEi0x9vjVfi0vERFReZFXjE0d1atXh0QiybeNGTMGADBkyJB8xzw9PUviEvNhj5+IiEjLzp8/r/L22qtXr6Jjx47o3bu3sqxLly4IDg5W7hsZGWklFiZ+IiISLV3dqG5jY6OyP3/+fNSsWRNt2rRRlkmlUshkMq3HwqF+IiISrTyJ5ptCoUBqaqrKplAoCv3MrKwsbN26FUOHDlV5ON6xY8dga2uLWrVqYfjw4UhMTNTKNTPxExGRaBVnjj8oKAgWFhYqW1BQUKGfuXv3bjx9+hRDhgxRlnl5eSEkJARHjhzBokWLcP78eXz44YdF+iKhLolQDh/JZ2BUpbRDINK6zLiTpR0CkdYZWtfQavuLqg3U+Nyx0T/mS8xSqRRSqfSt53Xu3BlGRkbYt2/fG+vEx8fDyckJ27dvR69evTSOsSCc4yciItEqTs+3KEn+dffu3cPhw4exc+fOt9azt7eHk5MToqOjixFhwTjUT0REpCPBwcGwtbVF165d31rv0aNHuH//Puzt7Us8BiZ+IiISreIs7lP7s/LyEBwcjMGDB8PA4NWAe1paGvz9/XH27FncvXsXx44dg7e3N6ytrdGzZ88SvNoXONRPRESipcsn9x0+fBixsbEYOnSoSrm+vj6uXLmCzZs34+nTp7C3t0e7du2wY8cOmJubl3gcTPxERCRaulzd3qlTpwJfcW9iYoJDhw7pLA4mfiIiEq08nab+dwMTPxERiRZf0kNERETlGnv8REQkWuIb6GfiJyIiERPjUD8TPxERiZYm9+OXdUz8REQkWlzVT0REJCLiS/tc1U9ERCQq7PETEZFocXEfERGRiHCOn4iISETEl/aZ+ImISMQ41E+kgZEjBmPypJGwt7dF1LV/MHnyLJw6/Xdph0VUqPWbd+Dw8dOIufcAxlIjNHKrh4mjhsLZqaqyjiAIWLUhBL/uOYjUZ2lwq18bMyeNwXs1nJR1Yh/E4bvvf8DFy1HIysrG+54emD5xFKwtK5fGZZEaxDjUz1X9VCy9e3fH4kUBCJq/HB7NOuPUqb/x+76tcHR0KO3QiAoVHnkFn/TyxrZ1S7BuaSBycnPx+cQZyMh8rqyzIeQXbN6+E19OGo3tPy6DtWVlDPf7EunpGQCAjMzn+HziDEggwY/L52PLmkXIzs7B2KkByMsTY3+S3nUSoaCXA5dxBkZVSjsE0Thzah8uXLyKseOmK8uuXD6GvXv/wIyZ80sxsvIvM+5kaYdQ7jx+8hStu32Cjd8vhEcjNwiCgHY9BmBQHx/4DuwDAMjKykIb7/6YOGoo+vh8hNPnIjDK/2uc+eNnVDAzAwCkpD5DK68+WL80EC2aNi7NSyrzDK1raLX9idX7aXzukrvbSzAS3WGPnzRmaGiIJk0aIPTwcZXy0NDjaOHpUUpREWku7d9evEVFcwDAgzg5kh89QctmTZR1jIyM4NHIDZFXrgEAsrOzIZEARoaGyjpSqRH09PRw4XKUDqMnTeQVYyurmPhJY9bWljAwMEBiQrJKeWJiMuxktqUUFZFmBEHAwuXr0KRBfbjUqA4ASH78BABgVVl1rt7KspLyWIP6dWBibIzFqzYg8/lzZGQ+x6KVPyIvLw/Jjx7r9BpIfUIx/iurmPip2F6fLZJIJPnKiN518xavwj+3Y7Bw9rR8xyQS1Te5CMKrMsvKlbDomy9x7PQ5NOvQCy06f4xn6emoV/s96Onxn9h3nRh7/FzVTxpLTn6MnJwc2MlsVMptbKyQmJBUSlERqS9w8SocPRWGTd9/C5ntq7/nl6vykx8/ho21pbL88ZOnsKpcSbnfqrk7/vglGE+epkBfXx8VzSugjXd/dLGX6ewaiIqKX0dJY9nZ2bhw4TI6tG+tUt6hQ2ucDQsvpaiIik4QBMxbtAqHj5/BhuXzUdVBNVFXdZDB2qoyzp6/qCzLzs5GeOQVNHKrl6+9ypUsUNG8As5FROLxk6do976n1q+BiicPgsZbWcUePxXLkmXrsSl4GSIiLiHsXASG+w5ENccqWLtuS2mHRlSouYu+x4HQY1g+/2uYmZoo5+QrVDCDsVQKiUSCQX18sH7zDlSr6gAnxypYv3kHjKVSdO3YVtnOrv1/ooaTIypXssClqBuYv3QNPu3bU+V5APRuKrvpW3NM/FQsv/yyF1aWlTFzxkTY29viatRNeHcfhNjYh6UdGlGhduzaDwD4bKzqvP7cLyfBp2tHAMDQAb3xXJGFuYu+R+qzNDSoVxvrls6DmZmpsv7d2AdYumYjUlKfoYq9HT4f3A+f9u2puwshjZXlnrumeB8/URnF+/hJDLR9H//w6r01Pnf93V9KMBLdYY+fiIhEqyzflqepd3px3/379zF06NDSDoOIiKjceKcT/+PHj7Fp06a31lEoFEhNTVXZyuHsBRERaYGu7uMPCAiARCJR2WSyV3eRCIKAgIAAODg4wMTEBG3btkVUlHae/FiqQ/179+596/E7d+4U2kZQUBBmz56tUibRqwCJfsVixUZEROWfLof669evj8OHDyv39fX1lT8vXLgQixcvxsaNG1GrVi3MnTsXHTt2xM2bN2Fubl6icZRq4vfx8Sn0KW+vPzHrddOnT8ekSZNUyipb1SmR+IiIqHzT5RP4DAwMVHr5LwmCgKVLl2LGjBno1asXAGDTpk2ws7PDtm3bMGLEiBKNo1SH+u3t7fHbb78hLy+vwO3ChQuFtiGVSlGxYkWVrbAvC/TCyBGDEX3zLNJSb+Nc2EG836rZW+u3/sAT58IOIi31Nv65cQafDx+Ur07Pnh/h8qWjSH92B5cvHUWPHl1Ujn/ySU/E3D6PRPlVLAiaqXLMyakqrkWdhLl5heJfHIlSQlIyps1eiFZefeDxoQ8+HjwGUTeilcddW3kVuG0I+bVI7R84fAyurbww/os5KuXrN+9AX9/xaNahF1p37YfxX8xBzL0HKnWCt/2K1t0+Qetun2Dz9l0qxy5H3UCfoeOQm5ur4ZWTpvIEQeOtoKlmhULxxs+Kjo6Gg4MDnJ2d0a9fP+WodkxMDORyOTp16qSsK5VK0aZNG5w5c6bEr7lUE7+7u/tbkzuf+a49vXt3x+JFAQiavxwezTrj1Km/8fu+rXB0dCiwfvXqjti3dwtOnfobHs06Y/6CFVi6ZA569vxIWcezuTt+ClmNkJDf0MSjI0JCfsP2bWvQ7N/XklpZVca6Nd9i6hff4KOuAzBoUG985NVeef73K4Lw5YxAPHuWpt2Lp3IpJfUZBo2cDEMDA6xZ9A32hKzFlHHDYF7BTFnn2N4Qle2bLydCIpGgY9tWhbYfJ0/AopU/wL2ha75j4ZFX8Ekvb2xbtwTrlgYiJzcXn0+cgYzM5wCAf27H4PsftuLb2dOwMGAqlq3diOg7dwEA2Tk5mPPtCnw1ZazK0C/phlCMLSgoCBYWFipbUFBQgZ/TvHlzbN68GYcOHcL69eshl8vRsmVLPHr0CHK5HABgZ2enco6dnZ3yWEkq1aH+KVOmID09/Y3H33vvPRw9elSHEYnHxAnDsSF4OzYE/wQAmOw/C506tcHIEZ9ixsz5+eqP+HwQYu8/xGT/WQCAGzduwd29ISZPHIlduw4AAMaPH4bDh09gwcKVAIAFC1ei9QeeGD9+GAYOGoMazk5ISXmGX355sbbj2PEzqFvXBQcO/oV+/XyQlZ2N3bsP6uLyqRzaEPILZLY2mDvj1dRfFXvVf0itrSxV9o+eDEOzJg3gWMX+rW3n5uZi2uyFGO07CBcuXcWzNNV/t9YunquyP/fLiWjd7RNcuxkNj0ZuuHP3PmrVrI7m7o0AALXec8adu/fhUqM6gkN+hXsjN7jVra3uJVMpK2iqWSqVFljXy8tL+bObmxtatGiBmjVrYtOmTfD0fPFo5/wvgxK0MoJdqj3+Dz74AF26dHnjcTMzM7Rp00aHEYmDoaEhmjRpgNDDx1XKQ0OPo4WnR4HneDZ3R2ioav0/Q4/B3b0BDAwMXtU5fOK1Oq/ajL4VA1NTEzRqVB+VK1eCh3tDXLlyHZUrV0LA1/4YP0F16J9IHUdPhaF+HRdMmjkPrbv2w/+GjMGve9/8RTL58ROcOPM3enXrXGjbq4O3oXIlC3zsXXhdAEhLzwAAWFR8sSjLpWZ13L3/EPHyRMTJE3Dv/kO8V8MJsQ/isOfgYYwf/mmR2qWSV5xn9Rc01fymxP86MzMzuLm5ITo6Wjnv/3rvPjExMd8oQEl4p2/nI+2wtraEgYEBEhOSVcoTE5NhJ7Mt8Bw7mS0SE1+rn5AMQ0NDWP/71jKZzAYJiapv5UtITILs37f3PX2ags98/RC8YRnOnv4dW0N+xZ+hx7FwwVf4flUwnKs74vzfhxB58S/06tW1pC6XROJBnBw7du9HtapVsHbJXPTx6YqgJWuw5+DhAuvvPXgYpqYm6NDm7cP8Fy5HYdfvhzB72oQixSEIAhYuX4cmDerDpUZ1AEDN6tUwYcQQDPf7Ep/7zcCEEUNQs3o1zPl2BSaNHorTf0fAZ+BI/G/IGIRHXlHruql4hGL8VxwKhQLXr1+Hvb09nJ2dIZPJEBoaqjyelZWF48ePo2XLlsW9xHz45D4Re339RGFrKvLXz19eWJt79vyBPXv+UO63ad0Crq51MH7CDNy8fhoDB42BPCEJZ0//jpMnw5CU9Ejt6yJxyssTUL+OC/xGDgEA1K31Hm7F3MPPu/ajh1eHfPV3/f4nunVqB6nU6I1tpqdnYPqcbxEwbQIqV7IoUhzzFq/CP7djsHn1dyrlfXt2Rd+er77Q7t4fClNTEzR0rQvvT4Zj+w/LkJCYjClfz8ehX4NhZPTmuKjk6GpVv7+/P7y9vVGtWjUkJiZi7ty5SE1NxeDBgyGRSODn54fAwEC4uLjAxcUFgYGBMDU1Rf/+/Us8FiZ+EUpOfoycnBzYyWxUym1srJCYkFTgOQnyRNjZvVbf1hrZ2dl49OgJAEAuT4LMTnXEwNbGGgmvjSy8ZGRkhBUrAjF48Di8954zDAwMcOJkGADgn+g7aN6sCX7fH1rguUSvs7GyRM3q1VTKalR3xOFjp/PVjYi8ipjYB/h2zvS3tnn/YTwexidg7LQAZVle3osvsg1bd8W+betRreqrBbGBi1fh6KkwbPr+W8hsbV5vTunJ0xSs2bgNG79fiCtRN+HkWEW55eTm4O79h6hV07kol03FpKuX9Dx48ACffPIJkpOTYWNjA09PT4SFhcHJyQkAMHXqVGRmZmL06NF48uQJmjdvjj///LPE7+EHmPhFKTs7GxcuXEaH9q1Vet8dOrTGvn2HCjwn7FwEuv77trKXOnZog4iIy8jJyVHW6dD+Ayxbvv4/dVrjbFh4gW3OnOGHQ38cxcXIq2jUqD4MDF6taDY0NISePmeiqOgaN6iHu7Gqt9Ddi30I+wKmr3b+fgj1arugjsvbXwDj7OSIXVtWq5StWLcZ6RkZ+MJvJOz//TIsCAICF6/GXyfOIHjlAlR1yH+v9n/NX7YWg/r4QGZrg6vX/1H+fwgAcnPzkJery7vLxU1XD/DZvn37W49LJBIEBAQgICBA67Ew8YvUkmXrsSl4GSIiLiHsXASG+w5ENccqWLtuCwBg3twv4OBgj8+GvpjXXLtuC0aP+gzfLZyFHzaEwLO5O4Z+1g8DBo1RtrlixY84euQ3TPEfjb37DqG7d2e0b/8B2rTN/3rSevVqoff/usO96YsvEzdu3EZenoDPhvRDQkIS6tSuifDwSzr4TVB5MaivDwaNmIx1m7ajS/vWuHLtJn7dexCzpo5XqZeWno4/j56E/9jhBbYz/ZvvYGtthYmjPoNUaqScp3/p5e2B/y2fu+h7HAg9huXzv4aZqQmSHz0GAFSoYAbj1xZ7nfn7AmIfxCHoK38AgFu92oi59wAnz56HPDEJenp6qO5UtTi/CqK3YuIXqV9+2Qsry8qYOWMi7O1tcTXqJry7D0Js7EMAgExmh2r/uaf/7t378O4+CN99F4BRowYjLi4BfhO/Vt7KBwBnw8LRf+BozJk9FbMDpuD2nXv4ZMAo/H3+Yr7PX7NqIfynBCAjIxMA8Pz5c/gOm4jly+ZBKjXC+AkzERdX8vevUvnlVrc2lgZ9hWVrNmLNxm2oYi/DtAkj0K3zhyr1Dh4+DkEAPurYtsB24hMSoafmLVQ7du0HAHw2dppK+dwvJ8HnPyNlzxUKBC5ehe/mTIee3osRLTsba0yfOAozA5fAyNAQ82ZOzvdlgbRHjGMrEqEcPiHHwKhKaYdApHWZcSdLOwQirTO0fvt0THH1rOat8bm7YveVYCS6wx4/ERGJlq4W971LmPiJiEi0xDjUz8RPRESipcvX8r4reL8UERGRiLDHT0REosU5fiIiIhEphze2FYqJn4iIRIuL+4iIiEREjIv7mPiJiEi0xDjHz1X9REREIsIePxERiRYX9xEREYmIGIf6mfiJiEi0uLiPiIhIRPI41E9ERCQe4kv7XNVPREQkKuzxExGRaHFxHxERkYgw8RMREYkI7+MnIiISEfb4iYiIRESM9/FzVT8REZGIsMdPRESiJcY5fvb4iYhItPIgaLypIygoCE2bNoW5uTlsbW3h4+ODmzdvqtQZMmQIJBKJyubp6VmSlwuAiZ+IiERMEASNN3UcP34cY8aMQVhYGEJDQ5GTk4NOnTohPT1dpV6XLl0QHx+v3A4cOFCSlwuAQ/1ERCRiulrV/8cff6jsBwcHw9bWFhEREWjdurWyXCqVQiaTaTUW9viJiEi0hGL8p1AokJqaqrIpFIoifW5KSgoAwNLSUqX82LFjsLW1Ra1atTB8+HAkJiaW+DUz8RMREWkgKCgIFhYWKltQUFCh5wmCgEmTJuH999+Hq6urstzLywshISE4cuQIFi1ahPPnz+PDDz8s8peJopII5XBJo4FRldIOgUjrMuNOlnYIRFpnaF1Dq+272mm+eC4i9ni+pCyVSiGVSt963pgxY7B//36cOnUKVatWfWO9+Ph4ODk5Yfv27ejVq5fGcb6Oc/xERCRaxXmAT1GS/OvGjRuHvXv34sSJE29N+gBgb28PJycnREdHaxxjQZj4iYhItPJ0NOgtCALGjRuHXbt24dixY3B2di70nEePHuH+/fuwt7cv0Vg4x09ERKJVnMV96hgzZgy2bt2Kbdu2wdzcHHK5HHK5HJmZmQCAtLQ0+Pv74+zZs7h79y6OHTsGb29vWFtbo2fPniV6zezxExGRaOmqx7969WoAQNu2bVXKg4ODMWTIEOjr6+PKlSvYvHkznj59Cnt7e7Rr1w47duyAubl5icbCxE9ERKRlha2jNzExwaFDh3QSCxM/ERGJlhjfzsfET0REoqWrof53CRM/ERGJFnv8REREIiIIeaUdgs4x8RMRkWjp6iU97xLex09ERCQi7PETEZFolcPX1RSKiZ+IiERLjEP9TPxERCRa7PETERGJCO/jJyIiEhEx3sfPVf1EREQiwh4/ERGJFuf4iYiIRISr+omIiESEPX4iIiIR4ap+IiIiERFjj5+r+omIiESEPX4iIhItLu4jIiISETEO9TPxExGRaHFxHxERkYjwkb1ERERUrrHHT0REosWhfiIiIhHh4j4iIiIR4Rw/ERGRiAiCoPGmiVWrVsHZ2RnGxsZwd3fHyZMnS/iKCsfET0REoqXLxL9jxw74+flhxowZuHjxIj744AN4eXkhNjZWC1f2ZhKhHE5wGBhVKe0QiLQuM073PQUiXTO0rqHd9ouRL7KzHqpVv3nz5mjSpAlWr16tLKtbty58fHwQFBSkcRzqYo+fiIhESyjGplAokJqaqrIpFIoCPycrKwsRERHo1KmTSnmnTp1w5swZrVzbm5TLxX05an4Lo+JRKBQICgrC9OnTIZVKSzscIq3g33n5VJx8ERAQgNmzZ6uUzZo1CwEBAfnqJicnIzc3F3Z2dirldnZ2kMvlGsegiXI51E+6lZqaCgsLC6SkpKBixYqlHQ6RVvDvnF6nUCjy9fClUmmBXwzj4uJQpUoVnDlzBi1atFCWz5s3D1u2bMGNGze0Hu9L5bLHT0REpG1vSvIFsba2hr6+fr7efWJiYr5RAG3jHD8REZGWGRkZwd3dHaGhoSrloaGhaNmypU5jYY+fiIhIByZNmoRBgwbBw8MDLVq0wLp16xAbG4uRI0fqNA4mfio2qVSKWbNmccETlWv8O6fi6tu3Lx49eoQ5c+YgPj4erq6uOHDgAJycnHQaBxf3ERERiQjn+ImIiESEiZ+IiEhEmPiJiIhEhImfiIhIRJj4qdjehddMEmnLiRMn4O3tDQcHB0gkEuzevbu0QyIqFiZ+KpZ35TWTRNqSnp6Ohg0bYuXKlaUdClGJ4O18VCzvymsmiXRBIpFg165d8PHxKe1QiDTGHj9p7F16zSQRERUNEz9p7F16zSQRERUNEz8Vm0QiUdkXBCFfGRERvRuY+Elj79JrJomIqGiY+Elj79JrJomIqGj4dj4qlnflNZNE2pKWloZbt24p92NiYhAZGQlLS0tUq1atFCMj0gxv56NiW7VqFRYuXKh8zeSSJUvQunXr0g6LqEQcO3YM7dq1y1c+ePBgbNy4UfcBERUTEz8REZGIcI6fiIhIRJj4iYiIRISJn4iISESY+ImIiESEiZ+IiEhEmPiJiIhEhImfiIhIRJj4ibQkICAAjRo1Uu4PGTKkVN7jfvfuXUgkEkRGRr6xTvXq1bF06dIit7lx40ZUqlSp2LFJJBLs3r272O0QUdEx8ZOoDBkyBBKJBBKJBIaGhqhRowb8/f2Rnp6u9c9etmxZkZ/0VpRkTUSkCT6rn0SnS5cuCA4ORnZ2Nk6ePIlhw4YhPT0dq1evzlc3OzsbhoaGJfK5FhYWJdIOEVFxsMdPoiOVSiGTyeDo6Ij+/ftjwIAByuHml8PzGzZsQI0aNSCVSiEIAlJSUvD555/D1tYWFStWxIcffohLly6ptDt//nzY2dnB3Nwcvr6+eP78ucrx14f68/LysGDBArz33nuQSqWoVq0a5s2bBwBwdnYGADRu3BgSiQRt27ZVnhccHIy6devC2NgYderUwapVq1Q+5++//0bjxo1hbGwMDw8PXLx4Ue3f0eLFi+Hm5gYzMzM4Ojpi9OjRSEtLy1dv9+7dqFWrFoyNjdGxY0fcv39f5fi+ffvg7u4OY2Nj1KhRA7Nnz0ZOTo7a8RBRyWHiJ9EzMTFBdna2cv/WrVv4+eef8dtvvymH2rt27Qq5XI4DBw4gIiICTZo0Qfv27fH48WMAwM8//4xZs2Zh3rx5CA8Ph729fb6E/Lrp06djwYIF+Oqrr3Dt2jVs27YNdnZ2AF4kbwA4fPgw4uPjsXPnTgDA+vXrMWPGDMybNw/Xr19HYGAgvvrqK2zatAkAkJ6ejm7duqF27dqIiIhAQEAA/P391f6d6OnpYfny5bh69So2bdqEI0eOYOrUqSp1MjIyMG/ePGzatAmnT59Gamoq+vXrpzx+6NAhDBw4EOPHj8e1a9ewdu1abNy4UfnlhohKiUAkIoMHDxZ69Oih3D937pxgZWUl9OnTRxAEQZg1a5ZgaGgoJCYmKuv89ddfQsWKFYXnz5+rtFWzZk1h7dq1giAIQosWLYSRI0eqHG/evLnQsGHDAj87NTVVkEqlwvr16wuMMyYmRgAgXLx4UaXc0dFR2LZtm0rZN998I7Ro0UIQBEFYu3atYGlpKaSnpyuPr169usC2/svJyUlYsmTJG4///PPPgpWVlXI/ODhYACCEhYUpy65fvy4AEM6dOycIgiB88MEHQmBgoEo7W7ZsEezt7ZX7AIRdu3a98XOJqORxjp9E5/fff0eFChWQk5OD7Oxs9OjRAytWrFAed3Jygo2NjXI/IiICaWlpsLKyUmknMzMTt2/fBgBcv34dI0eOVDneokULHD16tMAYrl+/DoVCgfbt2xc57qSkJNy/fx++vr4YPny4sjwnJ0e5fuD69eto2LAhTE1NVeJQ19GjRxEYGIhr164hNTUVOTk5eP78OdLT02FmZgYAMDAwgIeHh/KcOnXqoFKlSrh+/TqaNWuGiIgInD9/XqWHn5ubi+fPnyMjI0MlRiLSHSZ+Ep127dph9erVMDQ0hIODQ77Fey8T20t5eXmwt7fHsWPH8rWl6S1tJiYmap+Tl5cH4MVwf/PmzVWO6evrAwCEEnjL9r179/DRRx9h5MiR+Oabb2BpaYlTp07B19dXZUoEeHE73uteluXl5WH27Nno1atXvjrGxsbFjpOINMPET6JjZmaG9957r8j1mzRpArlcDgMDA1SvXr3AOnXr1kVYWBg+/fRTZVlYWNgb23RxcYGJiQn++usvDBs2LN9xIyMjAC96yC/Z2dmhSpUquHPnDgYMGFBgu/Xq1cOWLVuQmZmp/HLxtjgKEh4ejpycHCxatAh6ei+WAf3888/56uXk5CA8PBzNmjUDANy8eRNPnz5FnTp1ALz4vd28eVOt3zURaR8TP1EhOnTogBYtWsDHxwcLFixA7dq1ERcXhwMHDsDHxwceHh6YMGECBg8eDA8PD7z//vsICQlBVFQUatSoUWCbxsbGmDZtGqZOnQojIyO0atUKSUlJiIqKgq+vL2xtbWFiYoI//vgDVatWhbGxMSwsLBAQEIDx48ejYsWK8PLygkKhQHh4OJ48eYJJkyahf//+mDFjBnx9fTFz5kzcvXsX3333nVrXW7NmTeTk5GDFihXw9vbG6dOnsWbNmnz1DA0NMW7cOCxfvhyGhoYYO3YsPD09lV8Evv76a3Tr1g2Ojo7o3bs39PT0cPnyZVy5cgVz585V/38IIioRXNVPVAiJRIIDBw6gdevWGDp0KGrVqoV+/frh7t27ylX4ffv2xddff41p06bB3d0d9+7dw6hRo97a7ldffYXJkyfj66+/Rt26ddG3b18kJiYCeDF/vnz5cqxduxYODg7o0aMHAGDYsGH44YcfsHHjRri5uaFNmzbYuHGj8va/ChUqYN++fbh27RoaN26MGTNmYMGCBWpdb6NGjbB48WIsWLAArq6uCAkJQVBQUL56pqammDZtGvr3748WLVrAxMQE27dvVx7v3Lkzfv/9d4SGhqJp06bw9PTE4sWL4eTkpFY8RFSyJEJJTAoSERFRmcAePxERkYgw8RMREYkIEz8REZGIMPETERGJCBM/ERGRiDDxExERiQgTPxERkYgw8RMREYkIEz8REZGIMPETERGJCBM/ERGRiDDxExERicj/Ad6kpYyXSRiqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix_sklearn(rf_tuned, X_train, y_train) ## Complete the code to create confusion matrix for train data on tuned estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "hEWNk03xm-C_",
   "metadata": {
    "id": "hEWNk03xm-C_"
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy  Recall  Precision   F1\n",
       "0       1.0     1.0        1.0  1.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_tuned_model_train_perf = model_performance_classification_sklearn(rf_tuned, X_train, y_train) ## Complete the code to check performance for train data on tuned estimator\n",
    "rf_tuned_model_train_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "joined-newman",
   "metadata": {
    "id": "joined-newman"
   },
   "source": [
    "#### Checking model performance on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "wFME9Q9EnDAV",
   "metadata": {
    "id": "wFME9Q9EnDAV"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " confusion_matrix_sklearn(rf_tuned, X_test, y_test)## Complete the code to create confusion matrix for test data on tuned estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8wdjwrnBnC8r",
   "metadata": {
    "id": "8wdjwrnBnC8r"
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.835821</td>\n",
       "      <td>0.956044</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.887755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy    Recall  Precision        F1\n",
       "0  0.835821  0.956044   0.828571  0.887755"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_tuned_model_test_perf = model_performance_classification_sklearn(rf_tuned, X_test, y_test) ## Complete the code to check performance for test data on tuned estimator\n",
    "rf_tuned_model_test_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mqL4gfWyBytf",
   "metadata": {
    "id": "mqL4gfWyBytf"
   },
   "source": [
    "## Boosting - Model Building and Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "divine-legend",
   "metadata": {
    "id": "divine-legend"
   },
   "source": [
    "### AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "k4u1jsyBnKWv",
   "metadata": {
    "id": "k4u1jsyBnKWv"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(random_state=1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_classifier = AdaBoostClassifier(random_state=1) ## Complete the code to define AdaBoost Classifier with random state = 1\n",
    "ab_classifier.fit(X_train, y_train) ## Complete the code to fit AdaBoost Classifier on the train data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "grand-popularity",
   "metadata": {
    "id": "grand-popularity"
   },
   "source": [
    "#### Checking model performance on training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "t4ZjMiH3nOKT",
   "metadata": {
    "id": "t4ZjMiH3nOKT"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix_sklearn(ab_classifier, X_train, y_train) ## Complete the code to create confusion matrix for train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "aI_bD8ECnPBW",
   "metadata": {
    "id": "aI_bD8ECnPBW"
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.985646</td>\n",
       "      <td>0.865546</td>\n",
       "      <td>0.9217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy    Recall  Precision      F1\n",
       "0  0.887097  0.985646   0.865546  0.9217"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_classifier_model_train_perf = model_performance_classification_sklearn(ab_classifier, X_train, y_train) ## Complete the code to check performance on train data\n",
    "ab_classifier_model_train_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "french-breed",
   "metadata": {
    "id": "french-breed"
   },
   "source": [
    "#### Checking model performance on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "vs6BUDZ3nV7K",
   "metadata": {
    "id": "vs6BUDZ3nV7K"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix_sklearn(ab_classifier, X_test, y_test) ## Complete the code to create confusion matrix for test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bX0bjFrSnX_g",
   "metadata": {
    "id": "bX0bjFrSnX_g"
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.843284</td>\n",
       "      <td>0.956044</td>\n",
       "      <td>0.836538</td>\n",
       "      <td>0.892308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy    Recall  Precision        F1\n",
       "0  0.843284  0.956044   0.836538  0.892308"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab_classifier_model_test_perf = model_performance_classification_sklearn(ab_classifier, X_test, y_test) ## Complete the code to check performance for test data\n",
    "ab_classifier_model_test_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "increased-sharing",
   "metadata": {
    "id": "increased-sharing"
   },
   "source": [
    "### Hyperparameter Tuning - AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "NDITsePZnfVi",
   "metadata": {
    "id": "NDITsePZnfVi"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(learning_rate=0.1, n_estimators=100, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(learning_rate=0.1, n_estimators=100, random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostClassifier(learning_rate=0.1, n_estimators=100, random_state=1)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose the type of classifier.\n",
    "abc_tuned = AdaBoostClassifier(random_state=1)\n",
    "\n",
    "# Grid of parameters to choose from\n",
    "parameters = {\n",
    "    \n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'learning_rate': [0.01, 0.1, 1.0],  # minimum two hyperparameters to be used for tuning\n",
    "}\n",
    "\n",
    "# Type of scoring used to compare parameter  combinations\n",
    "acc_scorer = metrics.make_scorer(metrics.f1_score)\n",
    "\n",
    "# Run the grid search\n",
    "grid_obj = GridSearchCV(abc_tuned, parameters, scoring=acc_scorer, cv=5) ## Complete the code to run grid search with cv = 5\n",
    "grid_obj = grid_obj.fit(X_train, y_train) ## Complete the code to fit the grid_obj on train data\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "abc_tuned = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data.\n",
    "abc_tuned.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adolescent-carroll",
   "metadata": {
    "id": "adolescent-carroll"
   },
   "source": [
    "#### Checking model performance on training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "Zwu3oJ0mnlU-",
   "metadata": {
    "id": "Zwu3oJ0mnlU-"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix_sklearn(abc_tuned, X_train, y_train) ## Complete the code to create confusion matrix for train data on tuned estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "TeI5LX6nnmtx",
   "metadata": {
    "id": "TeI5LX6nnmtx"
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.83871</td>\n",
       "      <td>0.976077</td>\n",
       "      <td>0.819277</td>\n",
       "      <td>0.89083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy    Recall  Precision       F1\n",
       "0   0.83871  0.976077   0.819277  0.89083"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abc_tuned_model_train_perf = model_performance_classification_sklearn(abc_tuned, X_train, y_train) ## Complete the code to check performance for train data on tuned estimator\n",
    "abc_tuned_model_train_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fitting-cameroon",
   "metadata": {
    "id": "fitting-cameroon"
   },
   "source": [
    "#### Checking model performance on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "516820e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix_sklearn(abc_tuned, X_test, y_test) ## Complete the code to create confusion matrix for test data on tuned estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "zNxoiaIbntNJ",
   "metadata": {
    "id": "zNxoiaIbntNJ"
   },
   "outputs": [],
   "source": [
    "abc_tuned_model_test_perf = model_performance_classification_sklearn(abc_tuned, X_test, y_test) ## Complete the code to create confusion matrix for test data on tuned estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "P8N5Ms__nuYA",
   "metadata": {
    "id": "P8N5Ms__nuYA"
   },
   "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>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.80597</td>\n",
       "      <td>0.945055</td>\n",
       "      <td>0.803738</td>\n",
       "      <td>0.868687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accuracy    Recall  Precision        F1\n",
       "0   0.80597  0.945055   0.803738  0.868687"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abc_tuned_model_test_perf = model_performance_classification_sklearn(abc_tuned, X_test, y_test) ## Complete the code to check performance for test data on tuned estimator\n",
    "abc_tuned_model_test_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mechanical-clarity",
   "metadata": {
    "id": "mechanical-clarity"
   },
   "source": [
    "## Model Comparison and Final Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "royal-panel",
   "metadata": {
    "id": "royal-panel"
   },
   "source": [
    "**Comparing all models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "phaZ84G0n6um",
   "metadata": {
    "id": "phaZ84G0n6um"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training performance comparison:\n"
     ]
    },
    {
     "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>Bagging Classifier</th>\n",
       "      <th>Tuned Bagging Classifier</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>Tuned Random Forest</th>\n",
       "      <th>Adaboost Classifier</th>\n",
       "      <th>Tuned Adaboost Classifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.996774</td>\n",
       "      <td>0.993548</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.887097</td>\n",
       "      <td>0.838710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.995215</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.985646</td>\n",
       "      <td>0.976077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990521</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.865546</td>\n",
       "      <td>0.819277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.997602</td>\n",
       "      <td>0.995238</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.921700</td>\n",
       "      <td>0.890830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Bagging Classifier  Tuned Bagging Classifier  Random Forest  \\\n",
       "Accuracy             0.996774                  0.993548            1.0   \n",
       "Recall               0.995215                  1.000000            1.0   \n",
       "Precision            1.000000                  0.990521            1.0   \n",
       "F1                   0.997602                  0.995238            1.0   \n",
       "\n",
       "           Tuned Random Forest  Adaboost Classifier  Tuned Adaboost Classifier  \n",
       "Accuracy                   1.0             0.887097                   0.838710  \n",
       "Recall                     1.0             0.985646                   0.976077  \n",
       "Precision                  1.0             0.865546                   0.819277  \n",
       "F1                         1.0             0.921700                   0.890830  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training performance comparison\n",
    "\n",
    "models_train_comp_df = pd.concat(\n",
    "    [\n",
    "        bagging_classifier_model_train_perf.T,\n",
    "        bagging_estimator_tuned_model_train_perf.T,\n",
    "        rf_estimator_model_train_perf.T,\n",
    "        rf_tuned_model_train_perf.T,\n",
    "        ab_classifier_model_train_perf.T,\n",
    "        abc_tuned_model_train_perf.T,\n",
    "    ],\n",
    "    axis=1,\n",
    ")\n",
    "models_train_comp_df.columns = [\n",
    "\n",
    "    \"Bagging Classifier\",\n",
    "    \"Tuned Bagging Classifier\",\n",
    "    \"Random Forest\",\n",
    "    \"Tuned Random Forest\",\n",
    "    \"Adaboost Classifier\",\n",
    "    \"Tuned Adaboost Classifier\",\n",
    "\n",
    "]\n",
    "print(\"Training performance comparison:\")\n",
    "models_train_comp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "G3TYNb-Bn8yz",
   "metadata": {
    "id": "G3TYNb-Bn8yz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing performance comparison:\n"
     ]
    },
    {
     "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>Bagging Classifier</th>\n",
       "      <th>Tuned Bagging Classifier</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>Tuned Random Forest</th>\n",
       "      <th>Adaboost Classifier</th>\n",
       "      <th>Tuned Adaboost Classifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.813433</td>\n",
       "      <td>0.820896</td>\n",
       "      <td>0.828358</td>\n",
       "      <td>0.835821</td>\n",
       "      <td>0.843284</td>\n",
       "      <td>0.805970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.890110</td>\n",
       "      <td>0.967033</td>\n",
       "      <td>0.956044</td>\n",
       "      <td>0.956044</td>\n",
       "      <td>0.956044</td>\n",
       "      <td>0.945055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.843750</td>\n",
       "      <td>0.807339</td>\n",
       "      <td>0.820755</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.836538</td>\n",
       "      <td>0.803738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.866310</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.883249</td>\n",
       "      <td>0.887755</td>\n",
       "      <td>0.892308</td>\n",
       "      <td>0.868687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Bagging Classifier  Tuned Bagging Classifier  Random Forest  \\\n",
       "Accuracy             0.813433                  0.820896       0.828358   \n",
       "Recall               0.890110                  0.967033       0.956044   \n",
       "Precision            0.843750                  0.807339       0.820755   \n",
       "F1                   0.866310                  0.880000       0.883249   \n",
       "\n",
       "           Tuned Random Forest  Adaboost Classifier  Tuned Adaboost Classifier  \n",
       "Accuracy              0.835821             0.843284                   0.805970  \n",
       "Recall                0.956044             0.956044                   0.945055  \n",
       "Precision             0.828571             0.836538                   0.803738  \n",
       "F1                    0.887755             0.892308                   0.868687  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing performance comparison\n",
    "\n",
    "# Testing performance comparison\n",
    "models_test_comp_df = pd.concat(\n",
    "    [\n",
    "        bagging_classifier_model_test_perf.T,\n",
    "        bagging_estimator_tuned_model_test_perf.T,\n",
    "        rf_estimator_model_test_perf.T,\n",
    "        rf_tuned_model_test_perf.T,\n",
    "        ab_classifier_model_test_perf.T,\n",
    "        abc_tuned_model_test_perf.T,\n",
    "    ],\n",
    "    axis=1,\n",
    ")\n",
    "models_test_comp_df.columns = [\n",
    "    \"Bagging Classifier\",\n",
    "    \"Tuned Bagging Classifier\",\n",
    "    \"Random Forest\",\n",
    "    \"Tuned Random Forest\",\n",
    "    \"Adaboost Classifier\",\n",
    "    \"Tuned Adaboost Classifier\",\n",
    "]\n",
    "print(\"Testing performance comparison:\")\n",
    "models_test_comp_df\n",
    " ## Complete the code to check performance for test data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stone-venezuela",
   "metadata": {
    "id": "stone-venezuela"
   },
   "source": [
    "### Important features of the final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "IPF1T_CFoKWY",
   "metadata": {
    "id": "IPF1T_CFoKWY"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = X_train.columns\n",
    "importances = abc_tuned.feature_importances_  # complete the code with the variable corresponding to the best model\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.title(\"Feature Importances\")\n",
    "plt.barh(range(len(indices)), importances[indices], color=\"violet\", align=\"center\")\n",
    "plt.yticks(range(len(indices)), [feature_names[i] for i in indices])\n",
    "plt.xlabel(\"Relative Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "congressional-knock",
   "metadata": {
    "id": "congressional-knock"
   },
   "source": [
    "## Actionable Insights and Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "oq2XAZHdlpPM",
   "metadata": {
    "id": "oq2XAZHdlpPM"
   },
   "source": [
    "-\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "SLIcPS4hlpWf",
   "metadata": {
    "id": "SLIcPS4hlpWf"
   },
   "source": [
    "___"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "Lm7obbsV_RUT",
    "thorough-passion",
    "mq-1s9p-_aKl",
    "aboriginal-wrist",
    "accessory-camel",
    "assigned-berkeley",
    "standing-horizontal",
    "american-venue",
    "cutting-bookmark",
    "wooden-christian",
    "comic-inventory",
    "metric-apparel",
    "bulgarian-parts",
    "editorial-command",
    "equivalent-aging",
    "dressed-excuse",
    "changing-kansas",
    "qBWlk20UBUAx",
    "allied-association",
    "flexible-independence",
    "dr7q6-dbfiQB",
    "elegant-bookmark",
    "beneficial-bosnia",
    "joint-appendix",
    "joined-newman",
    "mqL4gfWyBytf",
    "divine-legend",
    "french-breed",
    "increased-sharing",
    "adolescent-carroll",
    "fitting-cameroon",
    "mechanical-clarity",
    "stone-venezuela",
    "congressional-knock"
   ],
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}