Loan Eligibility Prediction Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "991307d0",
   "metadata": {},
   "source": [
    "# <font color=#4863A0> Import Necessary Libraries📦</font>\n",
    "This comment indicates that we are importing the necessary libraries for our machine learning code. These libraries include **pandas**, **numpy**, **matplotlib**, **seaborn**, and **scikit-learn**, which are commonly used for *data manipulation*, *analysis*, *visualization*, and *machine learning algorithms*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd8a06a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[7m\u001b[32mTHE REQUIRED LIBRARIES WERE SUCCESSFULLY IMPORTED...\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Standard library imports\n",
    "import warnings # For warning handling\n",
    "\n",
    "# Third-party imports\n",
    "import pandas as pd # for data manipulation and analysis, CSV file I/O\n",
    "import numpy as np # For numerical operations and mathematical functions\n",
    "import matplotlib.pyplot as plt # For data visualization\n",
    "import seaborn as sns # For statistical graphics\n",
    "from sklearn.model_selection import train_test_split # For data splitting (Training & Testing) in machine learning\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler # For feature standardization & Normalization\n",
    "from sklearn.metrics import accuracy_score, classification_report # For model evaluation\n",
    "from termcolor import colored # For colored text printing\n",
    "\n",
    "# For warning handling\n",
    "warnings.filterwarnings('ignore') # For ignoring warnings\n",
    "\n",
    "# Print a success message in green color\n",
    "print(colored(\"THE REQUIRED LIBRARIES WERE SUCCESSFULLY IMPORTED...\", \"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e384ed40",
   "metadata": {},
   "source": [
    "# <font color=#4863A0> Load The Dataset🔃</font>\n",
    "This comment indicates that we are loading the **\"Finance Loan approval Prediction Data\"** dataset into our program. The dataset will be stored in a pandas DataFrame called **\"loan_data\"**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d9156b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[7m\u001b[32mTHE DATASET LOADED SUCCESSFULLY...\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Define file path\n",
    "# The \"r\" in front of the string makes it a \"raw string\".  It tells Python to treat backslashes (\\) in the string as normal characters, not special ones.\n",
    "file_path = r'D:\\AOU\\TM471-B\\The Project Code\\Dataset\\Data.csv'\n",
    "\n",
    "try:\n",
    "    # Read the CSV file and save it in \"loan_data\" variable\n",
    "    loan_data = pd.read_csv(file_path)\n",
    "    \n",
    "    # Print a success message\n",
    "    print(colored(\"THE DATASET LOADED SUCCESSFULLY...\", \"green\", attrs=['reverse']))\n",
    "\n",
    "except FileNotFoundError:\n",
    "    # Handle file not found error\n",
    "    print(colored(\"ERROR: File not found!\", \"red\", attrs=['reverse']))\n",
    "\n",
    "except Exception as e:\n",
    "    # Handle other exceptions\n",
    "    print(colored(f\"ERROR: {e}\", \"red\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33065ae3",
   "metadata": {},
   "source": [
    "## <font color=#4863A0> Dataset Information </font>\n",
    "\n",
    "Welcome to the Loan Prediction Dataset. This dataset provides insights into loan applications, inclusive **614** records and **13** attributes.\n",
    "\n",
    "| No. | Attribute          | Description                               | Data type        |\n",
    "|:---:|:-------------------|:------------------------------------------|:-----------------|\n",
    "|  1  | LoanID             | 🆔 Unique Loan ID                         | Categorical      |\n",
    "|  2  | Gender             | 👫 Gender (Male/Female)                   | Categorical      |\n",
    "|  3  | Married            | 💍 Married (Y/N)                          | Categorical      |\n",
    "|  4  | Dependents         | 👨‍👩‍👧‍👦 Dependents(عدد المعالين)                              | Categorical      |\n",
    "|  5  | Education          | 🎓 Education (Graduate/Undergraduate)     | Categorical      |\n",
    "|  6  | SelfEmployed       | 💼 Self-employed (Y/N)                    | Categorical      |\n",
    "|  7  | ApplicantIncome    | 💰 Applicant's Income                     | Numeric/Integer  |\n",
    "|  8  | CoapplicantIncome  | 💵 Coapplicant's Income                   | Numeric/float    |\n",
    "|  9  | LoanAmount         | 💸 Loan Amount (in thousands)             | Numeric/float    |\n",
    "| 10  | LoanAmountTerm     | ⏰ Loan Term (in months)                  | Numeric/float    |\n",
    "| 11  | CreditHistory      | 📜 Credit History                         | Numeric/float    |\n",
    "| 12  | PropertyArea       | 🏡 Property Area (Urban/Semi-Urban/Rural) | Categorical      |\n",
    "| 13  | LoanStatus         | 📋 Loan Status (Approved/Not Approved)    | Categorical      |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b034323",
   "metadata": {},
   "source": [
    "# <font color=#4863A0> Initial Information About The Dataset 🧪</font>\n",
    "This comment signifies that we are going to display initial information of the dataset to get a glimpse of the data's structure and values.\n",
    "- Displaying the first few rows of the dataset to get a glimpse of the data.\n",
    "- Checking the shape of the dataset (number of rows and columns).\n",
    "- Exploring the column names and data types.\n",
    "- Descriptive Statistics includes:\n",
    "    - Numeric Variables.\n",
    "    - Categorical Variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e03c9e55",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[7m\u001b[32mAs you can see, the first 7 rows in the dataset:\n",
      "\u001b[0m\n",
      "\u001b[7m\u001b[97mRow 1:\u001b[0m\n",
      "Loan_ID              LP001002\n",
      "Gender                   Male\n",
      "Married                    No\n",
      "Dependents                  0\n",
      "Education            Graduate\n",
      "Self_Employed              No\n",
      "ApplicantIncome          5849\n",
      "CoapplicantIncome         0.0\n",
      "LoanAmount                NaN\n",
      "Loan_Amount_Term        360.0\n",
      "Credit_History            1.0\n",
      "Property_Area           Urban\n",
      "Loan_Status                 Y\n",
      "Name: 0, dtype: object\n",
      "--------------------------------------\n",
      "\u001b[7m\u001b[97mRow 2:\u001b[0m\n",
      "Loan_ID              LP001003\n",
      "Gender                   Male\n",
      "Married                   Yes\n",
      "Dependents                  1\n",
      "Education            Graduate\n",
      "Self_Employed              No\n",
      "ApplicantIncome          4583\n",
      "CoapplicantIncome      1508.0\n",
      "LoanAmount              128.0\n",
      "Loan_Amount_Term        360.0\n",
      "Credit_History            1.0\n",
      "Property_Area           Rural\n",
      "Loan_Status                 N\n",
      "Name: 1, dtype: object\n",
      "--------------------------------------\n",
      "\u001b[7m\u001b[97mRow 3:\u001b[0m\n",
      "Loan_ID              LP001005\n",
      "Gender                   Male\n",
      "Married                   Yes\n",
      "Dependents                  0\n",
      "Education            Graduate\n",
      "Self_Employed             Yes\n",
      "ApplicantIncome          3000\n",
      "CoapplicantIncome         0.0\n",
      "LoanAmount               66.0\n",
      "Loan_Amount_Term        360.0\n",
      "Credit_History            1.0\n",
      "Property_Area           Urban\n",
      "Loan_Status                 Y\n",
      "Name: 2, dtype: object\n",
      "--------------------------------------\n",
      "\u001b[7m\u001b[97mRow 4:\u001b[0m\n",
      "Loan_ID                  LP001006\n",
      "Gender                       Male\n",
      "Married                       Yes\n",
      "Dependents                      0\n",
      "Education            Not Graduate\n",
      "Self_Employed                  No\n",
      "ApplicantIncome              2583\n",
      "CoapplicantIncome          2358.0\n",
      "LoanAmount                  120.0\n",
      "Loan_Amount_Term            360.0\n",
      "Credit_History                1.0\n",
      "Property_Area               Urban\n",
      "Loan_Status                     Y\n",
      "Name: 3, dtype: object\n",
      "--------------------------------------\n",
      "\u001b[7m\u001b[97mRow 5:\u001b[0m\n",
      "Loan_ID              LP001008\n",
      "Gender                   Male\n",
      "Married                    No\n",
      "Dependents                  0\n",
      "Education            Graduate\n",
      "Self_Employed              No\n",
      "ApplicantIncome          6000\n",
      "CoapplicantIncome         0.0\n",
      "LoanAmount              141.0\n",
      "Loan_Amount_Term        360.0\n",
      "Credit_History            1.0\n",
      "Property_Area           Urban\n",
      "Loan_Status                 Y\n",
      "Name: 4, dtype: object\n",
      "--------------------------------------\n",
      "\u001b[7m\u001b[97mRow 6:\u001b[0m\n",
      "Loan_ID              LP001011\n",
      "Gender                   Male\n",
      "Married                   Yes\n",
      "Dependents                  2\n",
      "Education            Graduate\n",
      "Self_Employed             Yes\n",
      "ApplicantIncome          5417\n",
      "CoapplicantIncome      4196.0\n",
      "LoanAmount              267.0\n",
      "Loan_Amount_Term        360.0\n",
      "Credit_History            1.0\n",
      "Property_Area           Urban\n",
      "Loan_Status                 Y\n",
      "Name: 5, dtype: object\n",
      "--------------------------------------\n",
      "\u001b[7m\u001b[97mRow 7:\u001b[0m\n",
      "Loan_ID                  LP001013\n",
      "Gender                       Male\n",
      "Married                       Yes\n",
      "Dependents                      0\n",
      "Education            Not Graduate\n",
      "Self_Employed                  No\n",
      "ApplicantIncome              2333\n",
      "CoapplicantIncome          1516.0\n",
      "LoanAmount                   95.0\n",
      "Loan_Amount_Term            360.0\n",
      "Credit_History                1.0\n",
      "Property_Area               Urban\n",
      "Loan_Status                     Y\n",
      "Name: 6, dtype: object\n",
      "--------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Displaying the first 7 rows.\n",
    "loan_data_rows = loan_data.head(7)  # .head() the default value = 5\n",
    "\n",
    "print(colored('As you can see, the first 7 rows in the dataset:\\n', 'green', attrs=['reverse']))\n",
    "\n",
    "# Iterate over each row in the loan_data_rows DataFrame\n",
    "for index, row in loan_data_rows.iterrows():\n",
    "    # Print the index label of the current row, \"index + 1\" start with 1 not 0 \n",
    "    print(colored(f\"Row {index + 1}:\", \"white\", attrs=['reverse']))\n",
    "    \n",
    "    # Print the content of the current row\n",
    "    print(row)\n",
    "    \n",
    "    # Print a separator line\n",
    "    print(\"--------------------------------------\")\n",
    "\n",
    "\n",
    "# .iterrows() function:\n",
    "# Returns: (index, data|row|series)\n",
    "#     Index: the index of the row.\n",
    "#     Data: the data of the row as a series.\n",
    "# Note: The column names will also be returned, in addition to the specified rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4c56fcc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape = (614, 13)\n",
      "Number of Rows: 614\n",
      "Number of Columns: 13\n",
      "Number of Features: 12\n",
      "Number of All Data: 7982\n",
      "\u001b[7m\u001b[32mThe task has been completed without any errors....\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Show the shape of the dataset\n",
    "print(\"The shape =\", loan_data.shape)\n",
    "\n",
    "# Dataset dimensions and statistics\n",
    "num_rows, num_cols = loan_data.shape\n",
    "num_features = num_cols - 1\n",
    "num_data = num_rows * num_cols\n",
    "\n",
    "# Print the information about the dataset\n",
    "print(f\"Number of Rows: {num_rows}\")\n",
    "print(f\"Number of Columns: {num_cols}\")\n",
    "print(f\"Number of Features: {num_features}\")\n",
    "print(f\"Number of All Data: {num_data}\")\n",
    "\n",
    "# Check and ensure running\n",
    "print(colored(\"The task has been completed without any errors....\", \"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "257c9102",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n",
      "None\n",
      "\u001b[7m\u001b[32mThe task has been completed without any errors....\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Get basic information from dataset like \"Column names\", \"Data types\" and \"Non-null values counts\"\n",
    "print(loan_data.info())\n",
    "\n",
    "# Check and ensure running\n",
    "print(colored(\"The task has been completed without any errors....\", \"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "504b48aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <td>614.0</td>\n",
       "      <td>5403.46</td>\n",
       "      <td>6109.04</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2877.5</td>\n",
       "      <td>3812.5</td>\n",
       "      <td>5795.00</td>\n",
       "      <td>81000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <td>614.0</td>\n",
       "      <td>1621.25</td>\n",
       "      <td>2926.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1188.5</td>\n",
       "      <td>2297.25</td>\n",
       "      <td>41667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanAmount</th>\n",
       "      <td>592.0</td>\n",
       "      <td>146.41</td>\n",
       "      <td>85.59</td>\n",
       "      <td>9.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>168.00</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <td>600.0</td>\n",
       "      <td>342.00</td>\n",
       "      <td>65.12</td>\n",
       "      <td>12.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.00</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <td>564.0</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count     mean      std    min     25%     50%      75%  \\\n",
       "ApplicantIncome    614.0  5403.46  6109.04  150.0  2877.5  3812.5  5795.00   \n",
       "CoapplicantIncome  614.0  1621.25  2926.25    0.0     0.0  1188.5  2297.25   \n",
       "LoanAmount         592.0   146.41    85.59    9.0   100.0   128.0   168.00   \n",
       "Loan_Amount_Term   600.0   342.00    65.12   12.0   360.0   360.0   360.00   \n",
       "Credit_History     564.0     0.84     0.36    0.0     1.0     1.0     1.00   \n",
       "\n",
       "                       max  \n",
       "ApplicantIncome    81000.0  \n",
       "CoapplicantIncome  41667.0  \n",
       "LoanAmount           700.0  \n",
       "Loan_Amount_Term     480.0  \n",
       "Credit_History         1.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive Statistics of Numeric Variables only.\n",
    "loan_data.describe().T.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b20b3d10",
   "metadata": {},
   "source": [
    "- **count =** The number of non-null values.\n",
    "- **mean =** Avarage.\n",
    "- **Standard Deviation (std)=** A measure of the spread of the variable's values around the mean. A higher standard deviation indicates greater variability in the data.\n",
    "- **min =** The smallest value. \n",
    "- **25% =** It represents the value below which 25% of the data fall\n",
    "- **50% =** It represents the value below which 50% of the data fall\n",
    "- **75% =** It represents the value below which 75% of the data fall\n",
    "- **max =** The largest value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "54676c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614</td>\n",
       "      <td>601</td>\n",
       "      <td>611</td>\n",
       "      <td>599</td>\n",
       "      <td>614</td>\n",
       "      <td>582</td>\n",
       "      <td>614</td>\n",
       "      <td>614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>614</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>489</td>\n",
       "      <td>398</td>\n",
       "      <td>345</td>\n",
       "      <td>480</td>\n",
       "      <td>500</td>\n",
       "      <td>233</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Loan_ID Gender Married Dependents Education Self_Employed  \\\n",
       "count        614    601     611        599       614           582   \n",
       "unique       614      2       2          4         2             2   \n",
       "top     LP001002   Male     Yes          0  Graduate            No   \n",
       "freq           1    489     398        345       480           500   \n",
       "\n",
       "       Property_Area Loan_Status  \n",
       "count            614         614  \n",
       "unique             3           2  \n",
       "top        Semiurban           Y  \n",
       "freq             233         422  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive analysis of Categorical Variables only.\n",
    "loan_data.describe(include=object)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c08a89e",
   "metadata": {},
   "source": [
    "# <font color=\"#686868\" size=\"8\"> Data Exploration(EDA) 🔍</font>\n",
    "Exploratory Data Analysis (EDA), is an essential step in the machine learning pipeline. It involves **examining** and **visualizing the characteristics of a dataset to gain insights**, **understand patterns**, **detect anomalies**, and **prepare the data for further analysis** and **modeling**. some common techniques used in data exploration:\n",
    "- Data Visualization\n",
    "- Data Cleaning\n",
    "- Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5bee373",
   "metadata": {},
   "source": [
    "# <font color=#4863A0> Data Visualization 📊</font>\n",
    "Creating visual representations such as **histograms**,**scatter plots**, **box plots**, and **heatmaps** to explore the distribution, relationships, and trends within the dataset. Visualization aids in identifying **patterns**, **correlations**, and **potential insights** that may not be apparent from just looking at the raw data. there are some ways to discover it:\n",
    "- Graphical Analysis\n",
    "    - Bar Charts\n",
    "    - Pie Charts \n",
    "    - Histograms\n",
    "- Correlation Analysis\n",
    "- Data Relationships Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbcea27d",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">1. Graphical Analysis</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bdc35210",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male      489\n",
      "Female    112\n",
      "NaN        13\n",
      "Name: Gender, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \"dropna\" as False to count NaN (Not-a-Number) values\n",
    "GenderAnalysis = loan_data.Gender.value_counts(dropna=False)\n",
    "print(GenderAnalysis)\n",
    "\n",
    "# Bar Charts Analysis \"For Gender feature\"\n",
    "sns.countplot(x=\"Gender\", data=loan_data, palette=\"flare\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb21cb20",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes    398\n",
      "No     213\n",
      "NaN      3\n",
      "Name: Married, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \"dropna\" as False to count NaN values\n",
    "MarriedAnalysis = loan_data.Married.value_counts(dropna=False)\n",
    "print(MarriedAnalysis)\n",
    "\n",
    "# Create a pie chart \"For Married feature\"\n",
    "plt.figure(figsize=(10, 5)) # figure in inches\n",
    "\n",
    "# labels by descending order\n",
    "plt.pie(MarriedAnalysis, \n",
    "        labels=[(\"Married\"),(\"Single\"),(\"NaN\")], \n",
    "        startangle=216, \n",
    "        autopct='%1.1f%%', \n",
    "        colors=sns.color_palette(\"flare\", \n",
    "        len(MarriedAnalysis)))\n",
    "\n",
    "plt.axis('equal')  # Used to set the aspect ratio of the plot to be equal.\n",
    "plt.title('Marital Status Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb1bce9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      345\n",
      "1      102\n",
      "2      101\n",
      "3+      51\n",
      "NaN     15\n",
      "Name: Dependents, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \"dropna\" as False to count NaN values\n",
    "DependentsAnalysis = loan_data.Dependents.value_counts(dropna=False)\n",
    "print(DependentsAnalysis)\n",
    "\n",
    "# Bar Charts Analysis \"For Dependents feature\"\n",
    "sns.countplot(x=\"Dependents\", data=loan_data, palette=\"flare\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "171ab5fc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graduate        480\n",
      "Not Graduate    134\n",
      "Name: Education, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \"dropna\" as False to count NaN values\n",
    "EducationAnalysis = loan_data.Education.value_counts(dropna=False)\n",
    "print(EducationAnalysis)\n",
    "\n",
    "# Bar Charts Analysis \"For Education feature\"\n",
    "sns.countplot(x=\"Education\", data=loan_data, palette=\"flare\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ecfaf895",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No     500\n",
      "Yes     82\n",
      "NaN     32\n",
      "Name: Self_Employed, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \"dropna\" as False to count NaN values\n",
    "Self_EmployedAnalysis = loan_data.Self_Employed.value_counts(dropna=False)\n",
    "print(Self_EmployedAnalysis)\n",
    "\n",
    "# Bar Charts Analysis \"For Self Employed feature\"\n",
    "sns.countplot(x=\"Self_Employed\", data=loan_data, palette=\"flare\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5ddb3d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Average Income: 5403.46 \n",
      "The ratio of people with income above average to below average: 40.18 \n",
      "Number of people income above the average: 176\n",
      "Number of people income below the average: 438\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the average income\n",
    "average_income = loan_data['ApplicantIncome'].mean()\n",
    "print(f\"The Average Income: {average_income:.2f} \")\n",
    "\n",
    "# Count incomes higher and lower than average\n",
    "above_average_count = (loan_data['ApplicantIncome'] > average_income).sum()\n",
    "below_average_count = (loan_data['ApplicantIncome'] <= average_income).sum()\n",
    "\n",
    "# Calculate ratio and print the results\n",
    "ratio = above_average_count / below_average_count\n",
    "print(f\"The ratio of people with income above average to below average: {ratio*100:.2f} \")\n",
    "print(f\"Number of people income above the average: {above_average_count}\")\n",
    "print(f\"Number of people income below the average: {below_average_count}\")\n",
    "\n",
    "# Plot the ratio using Seaborn\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.barplot(x=['Above Average', 'Below Average'], y=[above_average_count, below_average_count], palette=\"flare\")\n",
    "plt.title('Ratio of People with Income Above Average to Below Average')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc873bf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0    475\n",
      "0.0     89\n",
      "NaN     50\n",
      "Name: Credit_History, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \"dropna\" as False to count NaN values\n",
    "Credit_HistoryAnalysis = loan_data.Credit_History.value_counts(dropna=False)\n",
    "print(Credit_HistoryAnalysis)\n",
    "\n",
    "# Bar Charts Analysis \"For Credit History feature\"\n",
    "sns.countplot(x=\"Credit_History\", data=loan_data, palette=\"flare\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e4b7bba0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Semiurban    233\n",
      "Urban        202\n",
      "Rural        179\n",
      "Name: Property_Area, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \"dropna\" as False to count NaN values\n",
    "Property_AreaAnalysis = loan_data.Property_Area.value_counts(dropna=False)\n",
    "print(Property_AreaAnalysis)\n",
    "\n",
    "# Bar Charts Analysis \"For Property Area feature\"\n",
    "sns.countplot(x=\"Property_Area\", data=loan_data, palette=\"flare\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c5cda687",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y    422\n",
      "N    192\n",
      "Name: Loan_Status, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \"dropna\" as False to count NaN values\n",
    "Loan_StatusAnalysis = loan_data.Loan_Status.value_counts(dropna=False)\n",
    "print(Loan_StatusAnalysis)\n",
    "\n",
    "# Bar Charts Analysis \"For Loan Status column\"\n",
    "sns.countplot(x=\"Loan_Status\", data=loan_data, palette=\"flare\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4c41d51",
   "metadata": {},
   "source": [
    "The number of approved loans is higher compared to rejected loans. It also can be seen, there is **no missing values** in this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "867b62d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "360.0    512\n",
      "180.0     44\n",
      "480.0     15\n",
      "NaN       14\n",
      "300.0     13\n",
      "240.0      4\n",
      "84.0       4\n",
      "120.0      3\n",
      "60.0       2\n",
      "36.0       2\n",
      "12.0       1\n",
      "Name: Loan_Amount_Term, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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dAADgN6oUdp544gk9+eSTnm8jBwAA8FdV+qOCOTk5uuOOO6q7FgAAgGpXpbBzxx13aMOGDdVdCwAAQLWr0sdYl19+uR599FF98sknio2NVVBQkNf6Bx98sFqKAwAAOF9VCjtLly5V/fr1lZqaqtTUVK91DoeDsAMAAPxGlcJORkZGddcBAABQI6p0zQ4AAMCFokpndu65554zrn/55ZerVAwAAEB1q1LYycnJ8XpcXFys9PR0HT16tNwvCAUAAPCVKoWd5OTkMmOlpaUaO3asWrVqdd5FAQAAVJdqu2anTp06euihh7RgwYLq2iQAAMB5q9YLlL/++mudPHmyOjcJAABwXqr0MdbEiRO9HhtjlJmZqbffflsjR46slsIAAACqQ5XCzq5du7we16lTR40bN9bTTz991ju1AAAAalOVws6mTZuquw4AAIAaUaWwc8qPP/6o/fv3y+Fw6IorrlDjxo2rqy4AAIBqUaULlI8fP6577rlHTZs21Y033qgbbrhBbrdbo0eP1okTJ6q7RgAAgCqrUtiZOHGiUlNT9be//U1Hjx7V0aNH9eabbyo1NVWTJk2q7hoBAACqrEofY61du1Z//etf1aNHD8/Yb3/7W4WEhGjw4MFasmRJddUHAABwXqp0ZufEiROKiooqM96kSRM+xgIAAH6lSmGnS5cumjVrln755RfPWEFBgR577DF16dKl2ooDAAA4X1X6GGvhwoXq37+/mjVrpo4dO8rhcGj37t1yOp3asGFDddcIAABQZVUKO7GxsTp48KBWr16tL7/8UsYY3XnnnRo2bJhCQkKqu0YAAIAqq1LYSUxMVFRUlP7nf/7Ha/zll1/Wjz/+qGnTplVLcQAAAOerStfsvPjii2rbtm2Z8fbt2+uFF14476IAAACqS5XCTlZWlpo2bVpmvHHjxsrMzDzvogAAAKpLlcJOdHS0PvroozLjH330kdxu93kXBQAAUF2qdM3Ovffeq/j4eBUXF6tnz56SpPfff19Tp07lLygDAAC/UqWwM3XqVP38888aO3asioqKJEl169bVtGnTNGPGjGotEAAA4HxUKew4HA499dRTevTRR7Vv3z6FhISodevWcjqd1V0fAADAealS2Dmlfv36uvbaa6urFgAAgGpXpQuUAQAALhSEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFbzadhJTEzUtddeq7CwMDVp0kS33Xab9u/f7zXHGKPZs2fL7XYrJCREPXr00N69e73mFBYWavz48WrUqJFCQ0M1aNAgHT58uDZbAQAAfsqnYSc1NVUPPPCAPvnkE6WkpOjkyZPq27evjh8/7pkzb948zZ8/X4sWLdL27dvlcrnUp08fHTt2zDMnPj5eycnJSkpK0tatW5Wfn68BAwaopKTEF20BAAA/4jDGGF8XccqPP/6oJk2aKDU1VTfeeKOMMXK73YqPj9e0adMk/fssTlRUlJ566indd999ys3NVePGjbVq1SoNGTJEkvTDDz8oOjpa77zzjvr161fmdQoLC1VYWOh5nJeXp+joaOXm5io8PLx2mgUAWGV85zG+LqFCz336gq9LqBF5eXmKiIg46+9vv7pmJzc3V5IUGRkpScrIyFBWVpb69u3rmeN0OtW9e3dt27ZNkpSWlqbi4mKvOW63WzExMZ45p0tMTFRERIRniY6OrqmWAACAj/lN2DHGaOLEierWrZtiYmIkSVlZWZKkqKgor7lRUVGedVlZWQoODlaDBg0qnHO6GTNmKDc317McOnSoutsBAAB+ItDXBZwybtw4ff7559q6dWuZdQ6Hw+uxMabM2OnONMfpdMrpdFa9WAAAcMHwizM748eP1/r167Vp0yY1a9bMM+5yuSSpzBma7Oxsz9kel8uloqIi5eTkVDgHAABcvHwadowxGjdunNatW6cPPvhALVu29FrfsmVLuVwupaSkeMaKioqUmpqqrl27SpLi4uIUFBTkNSczM1Pp6emeOQAA4OLl04+xHnjgAa1Zs0ZvvvmmwsLCPGdwIiIiFBISIofDofj4eCUkJKh169Zq3bq1EhISVK9ePQ0dOtQzd/To0Zo0aZIaNmyoyMhITZ48WbGxserdu7cv2wMAAH7Ap2FnyZIlkqQePXp4ja9YsUKjRo2SJE2dOlUFBQUaO3ascnJy1LlzZ23YsEFhYWGe+QsWLFBgYKAGDx6sgoIC9erVSytXrlRAQEBttQIAAPyUX/2dHV+p7H36AABUhL+zU/suyL+zAwAAUN0IOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Xwadj788EMNHDhQbrdbDodDb7zxhtd6Y4xmz54tt9utkJAQ9ejRQ3v37vWaU1hYqPHjx6tRo0YKDQ3VoEGDdPjw4VrsAgAA+DOfhp3jx4+rY8eOWrRoUbnr582bp/nz52vRokXavn27XC6X+vTpo2PHjnnmxMfHKzk5WUlJSdq6davy8/M1YMAAlZSU1FYbAADAjwX68sX79++v/v37l7vOGKOFCxdq5syZuv322yVJr7zyiqKiorRmzRrdd999ys3N1fLly7Vq1Sr17t1bkrR69WpFR0dr48aN6tevX631AgAA/JPfXrOTkZGhrKws9e3b1zPmdDrVvXt3bdu2TZKUlpam4uJirzlut1sxMTGeOeUpLCxUXl6e1wIAAOzkt2EnKytLkhQVFeU1HhUV5VmXlZWl4OBgNWjQoMI55UlMTFRERIRniY6OrubqAQCAv/DbsHOKw+HwemyMKTN2urPNmTFjhnJzcz3LoUOHqqVWAADgf/w27LhcLkkqc4YmOzvbc7bH5XKpqKhIOTk5Fc4pj9PpVHh4uNcCAADs5Ldhp2XLlnK5XEpJSfGMFRUVKTU1VV27dpUkxcXFKSgoyGtOZmam0tPTPXMAAMDFzad3Y+Xn5+urr77yPM7IyNDu3bsVGRmpyy67TPHx8UpISFDr1q3VunVrJSQkqF69eho6dKgkKSIiQqNHj9akSZPUsGFDRUZGavLkyYqNjfXcnQUAAC5uPg07O3bs0E033eR5PHHiREnSyJEjtXLlSk2dOlUFBQUaO3ascnJy1LlzZ23YsEFhYWGe5yxYsECBgYEaPHiwCgoK1KtXL61cuVIBAQG13g8AAPA/DmOM8XURvpaXl6eIiAjl5uZy/Q4AoErGdx7j6xIq9NynL/i6hBpR2d/ffnvNDgAAQHUg7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWC3Q1wUAgC+8Oije1yWc0ZD1C31dAmANzuwAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoG+LgAAAPiHYdfc4+sSzujPO1+u0vM4swMAAKzGmR0AgE89duN4X5dwRrM+fM7XJeA8cWYHAABYjbADAACsRtgBAABWI+wAAACrWRN2Fi9erJYtW6pu3bqKi4vTli1bfF0SAADwA1bcjfXqq68qPj5eixcv1vXXX68XX3xR/fv31xdffKHLLrvM1+UBHtvv89+7Tq59kTtOLjTP953g6xLO6IENz/i6BECSJWFn/vz5Gj16tO69915J0sKFC/Xee+9pyZIlSkxM9HF1vrX/6Wm+LuGM2kx6qlLz0h/274N6TMLFc1DfOORBX5dwRr1ffdbXJQDwMxd82CkqKlJaWpqmT5/uNd63b19t27at3OcUFhaqsLDQ8zg3N1eSlJeX5zXvmz9X7hexr7QadvYgk/9L4Vnn+NLpP/OK5Bda0kdRUQ1XUnWV7eF4sf/2IFW+jxPFF/57quDkhd+DJP1y0o73VFGJ//ZR2R6K/bgHqWwfpx4bY878RHOBO3LkiJFkPvroI6/xJ5980lxxxRXlPmfWrFlGEgsLCwsLC4sFy6FDh86YFS74MzunOBwOr8fGmDJjp8yYMUMTJ070PC4tLdXPP/+shg0bVvic85WXl6fo6GgdOnRI4eHhNfIaNc2GHiT68Cc29CDZ0YcNPUj04U9qowdjjI4dOya3233GeRd82GnUqJECAgKUlZXlNZ6dna2oqKhyn+N0OuV0Or3GLrnkkpoq0Ut4ePgF+8Y9xYYeJPrwJzb0INnRhw09SPThT2q6h4iIiLPOueBvPQ8ODlZcXJxSUlK8xlNSUtS1a1cfVQUAAPzFBX9mR5ImTpyoESNGqFOnTurSpYuWLl2q77//XmPGjPF1aQAAwMesCDtDhgzRTz/9pDlz5igzM1MxMTF655131Lx5c1+X5uF0OjVr1qwyH59dSGzoQaIPf2JDD5IdfdjQg0Qf/sSfenAYc7b7tQAAAC5cF/w1OwAAAGdC2AEAAFYj7AAAAKsRdgAAgNUIO1Xw4YcfauDAgXK73XI4HHrjjTc864qLizVt2jTFxsYqNDRUbrdbd999t3744Yezbnft2rVq166dnE6n2rVrp+Tk5BrrYcmSJerQoYPnjz116dJFf//7373m7Nu3T4MGDVJERITCwsJ03XXX6fvvv/ebHiTpyJEjGj58uBo2bKh69erpqquuUlpamme9MUazZ8+W2+1WSEiIevToob179551u7XZx8mTJ/XII4+oZcuWCgkJUatWrTRnzhyVlpaWO/++++6Tw+HQwoULz7rtmuyjOvaDwsJCjR8/Xo0aNVJoaKgGDRqkw4cPn/W1Fy9erJYtW6pu3bqKi4vTli1bqr0HScrPz9e4cePUrFkzhYSE6Morr9SSJUv8qofExERde+21CgsLU5MmTXTbbbdp//79Fc6v6P3j6z7OdkyqzL7s6x4q04d09mOrP/Txa4mJiXI4HIqPj/eMXQj7hpdq+Hqqi84777xjZs6cadauXWskmeTkZM+6o0ePmt69e5tXX33VfPnll+bjjz82nTt3NnFxcWfc5rZt20xAQIBJSEgw+/btMwkJCSYwMNB88sknNdLD+vXrzdtvv232799v9u/fbx5++GETFBRk0tPTjTHGfPXVVyYyMtJMmTLF7Ny503z99dfmrbfeMv/85z/9poeff/7ZNG/e3IwaNcp8+umnJiMjw2zcuNF89dVXnjlz5841YWFhZu3atWbPnj1myJAhpmnTpiYvL89v+njiiSdMw4YNzVtvvWUyMjLM66+/burXr28WLlxYZm5ycrLp2LGjcbvdZsGCBWfcbk33UR37wZgxY8yll15qUlJSzM6dO81NN91kOnbsaE6ePFnh6yYlJZmgoCCzbNky88UXX5gJEyaY0NBQ891331VrD8YYc++995r/+I//MJs2bTIZGRnmxRdfNAEBAeaNN97wmx769etnVqxYYdLT083u3bvNLbfcYi677DKTn59fZu6Z3j++7uNsx6TK7Mu+7qEyfVTm2OoPfZzy2WefmRYtWpgOHTqYCRMmeMYvhH3j1wg756m8A+TpPvvsMyPpjP9YgwcPNjfffLPXWL9+/cydd95ZHWVWSoMGDcxLL71kjDFmyJAhZvjw4ef0/NruYdq0aaZbt24Vri8tLTUul8vMnTvXM/bLL7+YiIgI88ILL1T4vNru45ZbbjH33HOP19jtt99e5ud/+PBhc+mll5r09HTTvHnzs4ad2uyjKvvB0aNHTVBQkElKSvLMOXLkiKlTp4559913K9zOb37zGzNmzBivsbZt25rp06dXvQFTfg/t27c3c+bM8Rq75pprzCOPPOKXPRhjTHZ2tpFkUlNTvcbP9P7xxz6M+f9jUmX2ZX/t4dd9GHP2Y6s/9XHs2DHTunVrk5KSYrp37+4Vdi60fYOPsWpBbm6uHA7HGb9/6+OPP1bfvn29xvr166dt27bVcHVSSUmJkpKSdPz4cXXp0kWlpaV6++23dcUVV6hfv35q0qSJOnfuXOYU/+lqu4f169erU6dOuuOOO9SkSRNdffXVWrZsmWd9RkaGsrKyvGpyOp3q3r37GWuq7T66deum999/XwcOHJAk/eMf/9DWrVv129/+1jOntLRUI0aM0JQpU9S+fftKbdeX76nynL4fpKWlqbi42KtGt9utmJiYCmssKipSWlpamb769u1bI31169ZN69ev15EjR2SM0aZNm3TgwAH169fPb3vIzc2VJEVGRnrGzvb+8bc+Tj8mVWZf9rceyuujMsdWf+rjgQce0C233KLevXuXWXeh7RuEnRr2yy+/aPr06Ro6dOgZvwgtKyurzBeXRkVFlfmC0+q0Z88e1a9fX06nU2PGjFFycrLatWun7Oxs5efna+7cubr55pu1YcMG/e53v9Ptt9+u1NRUv+nhm2++0ZIlS9S6dWu99957GjNmjB588EH96U9/8tRzqoZzqam2+5g2bZruuusutW3bVkFBQbr66qsVHx+vu+66yzPnqaeeUmBgoB588MFKb9cX76mKlLcfZGVlKTg4WA0aNKh0jf/6179UUlJSa309++yzateunZo1a6bg4GDdfPPNWrx4sbp16+aXPRhjNHHiRHXr1k0xMTGe8bO9f/ylj4qOSZXZl/2lhzP1UZljq7/0kZSUpJ07dyoxMbHc9RfavmHF10X4q+LiYt15550qLS3V4sWLzzrf4XB4PTbGlBmrTm3atNHu3bt19OhRrV27ViNHjlRqaqrn/7xvvfVWPfTQQ5Kkq666Stu2bdMLL7yg7t27+0UPpaWl6tSpkxISEiRJV199tfbu3aslS5bo7rvvPq+aarOPV199VatXr9aaNWvUvn177d69W/Hx8XK73Ro5cqTS0tL0zDPPaOfOnedcQ22/p8pzrvuBP/37PPvss/rkk0+0fv16NW/eXB9++KHGjh2rpk2blvt/u+dST030MG7cOH3++efaunWrZ+x83j+13UdFx6TzeS1f/FvUxLG1Nvs4dOiQJkyYoA0bNqhu3brlzrnQ9g3O7NSQ4uJiDR48WBkZGUpJSTnr19u7XK4yyTU7O7tMwq1OwcHBuvzyy9WpUyclJiaqY8eOeuaZZ9SoUSMFBgaqXbt2XvOvvPLKM96NVds9NG3a9Iw1ulwuSTrnmmq7jylTpmj69Om68847FRsbqxEjRuihhx7y/B/Vli1blJ2drcsuu0yBgYEKDAzUd999p0mTJqlFixZ+00d5zrQfuFwuFRUVKScnp9I1NmrUSAEBAbXSV0FBgR5++GHNnz9fAwcOVIcOHTRu3DgNGTJEf/zjH/2uh/Hjx2v9+vXatGmTmjVr5hmvzPvHX/qo6JhUmX3ZX3o4Ux+VObb6Qx9paWnKzs5WXFyc5z2TmpqqZ599VoGBgTp+/PgFtW9IhJ0aceoAf/DgQW3cuFENGzY863O6dOmilJQUr7ENGzaoa9euNVVmGcYYFRYWKjg4WNdee22Z21cPHDhwxi9Xre0err/++jPW2LJlS7lcLq+aioqKlJqaesaaaruPEydOqE4d710xICDAc+v5iBEj9Pnnn2v37t2exe12a8qUKXrvvfcq3K6v31Nn2w/i4uIUFBTkVWNmZqbS09MrrDE4OFhxcXFl+kpJSan2voqLi1VcXHzGfxt/6MEYo3HjxmndunX64IMP1LJlS6/1lXn/+EMfFfVWWFhYqX3ZX3v4dR+VObb6Qx+9evXSnj17vN4znTp10rBhw7R7926VlJRcEPuGl/O6vPkidezYMbNr1y6za9cuI8nMnz/f7Nq1y3z33XemuLjYDBo0yDRr1szs3r3bZGZmepbCwkLPNkaMGOF1dflHH31kAgICzNy5c82+ffvM3Llza/R25xkzZpgPP/zQZGRkmM8//9w8/PDDpk6dOmbDhg3GGGPWrVtngoKCzNKlS83BgwfNc889ZwICAsyWLVv8pofPPvvMBAYGmieffNIcPHjQ/PnPfzb16tUzq1ev9syZO3euiYiIMOvWrTN79uwxd911V5nbVX3dx8iRI82ll17qufV83bp1plGjRmbq1KkVPqe8u7Fqu4/q2A/GjBljmjVrZjZu3Gh27txpevbsWebW1J49e5rnnnvO8/jUranLly83X3zxhYmPjzehoaHm22+/rdYejDGme/fupn379mbTpk3mm2++MStWrDB169Y1ixcv9pse7r//fhMREWE2b97s9XM+ceJEhc8p7/3j6z7OdkyqzL7s6x4q00dljq3+0MfpTr8b60LYN36NsFMFmzZtMpLKLCNHjjQZGRnlrpNkNm3a5NlG9+7dzciRI722+/rrr5s2bdqYoKAg07ZtW7N27doa6+Gee+4xzZs3N8HBwaZx48amV69enp3xlOXLl5vLL7/c1K1b13Ts2NHr7yf4Qw/GGPO3v/3NxMTEGKfTadq2bWuWLl3qtb60tNTMmjXLuFwu43Q6zY033mj27NnjV33k5eWZCRMmmMsuu8zUrVvXtGrVysycOdMrFJyuvF9Wtd1HdewHBQUFZty4cSYyMtKEhISYAQMGmO+//75Mr7NmzfIae/755z3v32uuuabMbdbV0YMxxmRmZppRo0YZt9tt6tata9q0aWOefvppU1pa6jc9VPRzXrFiRYXPKe/94+s+znZMqsy+7OseKtOHMWc/tvpDH6c7PexcCPvGrzmMMeb8zg0BAAD4L67ZAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAlho1apRuu+02X5dxVm3atFFwcLCOHDni61LOycqVK3XJJZdUau63334rh8NxxmX27Nk1Wi9wMQv0dQEALl5bt27VL7/8ojvuuEMrV67UzJkzfV1SjYiOjlZmZqbn8R//+Ee9++672rhxo2esfv3657TN4uJiBQUFVVuNgM04swNchFJTU/Wb3/xGTqdTTZs21fTp03Xy5EnP+nfffVfdunXTJZdcooYNG2rAgAH6+uuvPetPnalYt26dbrrpJtWrV08dO3bUxx9/fE51LF++XEOHDtWIESP08ssv6/Sv6mvRooWeeOIJ3X333apfv76aN2+uN998Uz/++KNuvfVW1a9fX7GxsdqxY4fX89auXav27dvL6XSqRYsWevrpp73WOxwOvfHGG15jl1xyiVauXFmp/jZv3qz//u//Vm5ubqXOzAQEBMjlcnmW+vXrKzAw0Gvs9ddf15VXXqm6deuqbdu2Wrx4cZmf92uvvaYePXqobt26Wr16tefsXUJCgqKionTJJZfoscce08mTJzVlyhRFRkaqWbNmevnll8/p3wWwznl/lSgAvzRy5Ehz6623lhk/fPiwqVevnhk7dqzZt2+fSU5ONo0aNfL65uG//vWvZu3atebAgQNm165dZuDAgSY2NtaUlJQYY4znW83btm1r3nrrLbN//37zX//1X6Z58+amuLi4UvXl5eWZ0NBQk56ebk6ePGmioqLMBx984DWnefPmJjIy0rzwwgvmwIED5v777zdhYWHm5ptvNq+99prZv3+/ue2228yVV17p+bblHTt2mDp16pg5c+aY/fv3mxUrVpiQkBCvbwGXZJKTk71eKyIiwjPnbP0VFhaahQsXmvDwcJOZmWkyMzPNsWPHKtW3McbMmjXLdOzY0fN46dKlpmnTpmbt2rXmm2++MWvXrjWRkZFm5cqVXvW0aNHCM+fIkSNm5MiRJiwszDzwwAPmyy+/NMuXLzeSTL9+/cyTTz5pDhw4YB5//HETFBRU5tumgYsJYQewVEVh5+GHHzZt2rTxhANjjHn++edN/fr1PWHmdNnZ2UaS2bNnjzHm/3/5vvTSS545e/fuNZLMvn37KlXf0qVLzVVXXeV5PGHCBDNs2DCvOc2bNzfDhw/3PM7MzDSSzKOPPuoZ+/jjj40kk5mZaYwxZujQoaZPnz5e25kyZYpp166d53Flw86Z+luxYoWJiIioVK+nOz3sREdHmzVr1njNefzxx02XLl286lm4cKHXnJEjR5rmzZt7/bu1adPG3HDDDZ7HJ0+eNKGhoeYvf/lLlWoFbMDHWMBFZt++ferSpYscDodn7Prrr1d+fr4OHz4sSfr66681dOhQtWrVSuHh4WrZsqUk6fvvv/faVocOHTz/3bRpU0lSdnZ2pepYvny5hg8f7nk8fPhwrVu3TkePHq3wNaKioiRJsbGxZcZOve6+fft0/fXXe23j+uuv18GDB1VSUlKp2sp77XPtr7J+/PFHHTp0SKNHj1b9+vU9yxNPPOH10aEkderUqczz27dvrzp1/v9QHhUV5fXzCQgIUMOGDau9buBCwgXKwEXGGOMVdE6NSfKMDxw4UNHR0Vq2bJncbrdKS0sVExOjoqIir+f9+gLZU88tLS09aw1ffPGFPv30U23fvl3Tpk3zjJeUlOgvf/mL7r///jO+xple90z9/fo5p48VFxeXqbOq/Z2LU9tbtmyZOnfu7LUuICDA63FoaOgZa5T+XWd5Y9VdN3AhIewAF5l27dpp7dq1XqFg27ZtCgsL06WXXqqffvpJ+/bt04svvqgbbrhB0r/vmqpOy5cv14033qjnn3/ea3zVqlVavny5V9g5V+3atStT77Zt23TFFVd4wkPjxo297o46ePCgTpw4cU6vExwcfM5nisoTFRWlSy+9VN98842GDRt23tsDUBZhB7BYbm6udu/e7TX2v//7v1q4cKHGjx+vcePGaf/+/Zo1a5YmTpyoOnXqqEGDBmrYsKGWLl2qpk2b6vvvv9f06dOrrabi4mKtWrVKc+bMUUxMjNe6e++9V/PmzdM//vEPdezYsUrbnzRpkq699lo9/vjjGjJkiD7++GMtWrTI6+6mnj17atGiRbruuutUWlqqadOmnfNt3C1atFB+fr7ef/99dezYUfXq1VO9evWqVPPs2bP14IMPKjw8XP3791dhYaF27NihnJwcTZw4sUrbBPD/uGYHsNjmzZt19dVXey2zZs3SO++8o88++0wdO3bUmDFjNHr0aD3yyCOSpDp16igpKUlpaWmKiYnRQw89pD/84Q/VVtP69ev1008/6Xe/+12Zda1bt1ZsbKyWL19e5e1fc801eu2115SUlKSYmBj9/ve/15w5czRq1CjPnKefflrR0dG68cYbNXToUE2ePPmcg0rXrl01ZswYDRkyRI0bN9a8efOqXPO9996rl156SStXrlRsbKy6d++ulStXeq6VAnB+HOb0D64BAAAswpkdAABgNcIOgGrXv39/r9uof70kJCT4urwasWXLlgp7PtevggBQvfgYC0C1O3LkiAoKCspdFxkZqcjIyFquqOYVFBSc8ctML7/88lqsBsCvEXYAAIDV+BgLAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGC1/wO7mB7mBaoiewAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \"dropna\" as False to count NaN values\n",
    "Loan_Amount_TermAnalysis = loan_data.Loan_Amount_Term.value_counts(dropna=False)\n",
    "print(Loan_Amount_TermAnalysis)\n",
    "\n",
    "# Bar Charts Analysis \"For Loan amount term feature\"\n",
    "sns.countplot(x=\"Loan_Amount_Term\", data=loan_data, palette=\"flare\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24ac228",
   "metadata": {},
   "source": [
    "As can be seen from the results, the **360** days loan duration is the most popular compared to others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "435a50f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram distribution for numerical values \"This enables us to see if there are outliers or not\"\n",
    "\n",
    "# Set the seaborn theme palette\n",
    "sns.set_theme(palette=\"flare\")\n",
    "\n",
    "# Before the improvement\n",
    "\"\"\"\n",
    "# Create a histogram distribution plot for ApplicantIncome\n",
    "plt.figure(figsize=(8, 4))\n",
    "# kernel density estimate (KDE) represents the data using a continuous probability density curve in one or more dimensions.\n",
    "sns.histplot(data=loan_data,x=\"ApplicantIncome\", kde=True)\n",
    "plt.title('Applicant Income Distribution')\n",
    "plt.xlabel('Applicant Income')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Create a histogram distribution plot for CoapplicantIncome\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.histplot(data=loan_data, x=\"CoapplicantIncome\", kde=True)\n",
    "plt.title('Coapplicant Income Distribution')\n",
    "plt.xlabel('Coapplicant Income')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Create a histogram distribution plot for LoanAmount\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.histplot(data=loan_data, x=\"LoanAmount\", kde=True)\n",
    "plt.title('Loan Amount Distribution')\n",
    "plt.xlabel('Loan Amount')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Create a histogram distribution plot for Loan_Amount_Term\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.histplot(data=loan_data, x=\"Loan_Amount_Term\", kde=True)\n",
    "plt.title('Loan Amount Term Distribution')\n",
    "plt.xlabel('Loan Amount Term')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Show all the plots\n",
    "plt.show()\n",
    "\"\"\"\n",
    "\n",
    "# After improvement and create a function for plot distribution \n",
    "def plot_distribution(column, title):\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    sns.histplot(data=loan_data, x=column, kde=True)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(column)\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.show()\n",
    "\n",
    "# Plot distribution for each numerical column\n",
    "plot_distribution(\"ApplicantIncome\", \"Applicant Income Distribution\")\n",
    "plot_distribution(\"CoapplicantIncome\", \"Coapplicant Income Distribution\")\n",
    "plot_distribution(\"LoanAmount\", \"Loan Amount Distribution\")\n",
    "plot_distribution(\"Loan_Amount_Term\", \"Loan Amount Term Distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b0ae058",
   "metadata": {},
   "source": [
    "From this analysis we found the distribution of **Applicant income**, **Co Applicant Income**, and **Loan Amount** are *positively skewed* (which most values are clustered around the left tail of the distribution while the right tail of the distribution is longer) and it has **outliers**. On the other hand, The distribution of **Loan Amount Term** is *negatively skewed* and it has **outliers**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22e7a157",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">2. Correlation Analysis</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "372e336b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x750 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix using heatmap\n",
    "# Calculates the correlation coefficients between all pairs of numerical variables in the dataset\n",
    "correlation_matrix = loan_data.corr()\n",
    "\n",
    "# Create a heatmap figure with specified size (in inches)\n",
    "plt.figure(figsize=(15, 7.5))\n",
    "\n",
    "# Generate a heatmap to visualize the correlation matrix\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='flare') # annot: write the data value in each cell\n",
    "\n",
    "# Add a title to the plot\n",
    "plt.title('Correlation Matrix')\n",
    "\n",
    "# Display the heatmap\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ee20ca0",
   "metadata": {},
   "source": [
    "There is positive correlation between **Loan Amount** and **Applicant Income**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b404a8e4",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">3. Data Relationships Analysis</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "12d9f2eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a cross-tabulation of 'Credit History' and 'Loan status' variables\n",
    "pd.crosstab(loan_data.Credit_History, loan_data.Loan_Status).plot(kind=\"bar\", figsize=(8, 4))\n",
    "\n",
    "# Add a title to the plot\n",
    "plt.title('Credit History VS Loan Status')\n",
    "\n",
    "# Label the x-axis\n",
    "plt.xlabel('Credit History')\n",
    "\n",
    "# Label the y-axis\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Rotate the x-axis labels to avoid overlap\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4123f3",
   "metadata": {},
   "source": [
    "The analysis appears a **good credit history** significantly *increases the chances of loan approval*. Because the percentage of people who have a good credit history and are approved is much better than a bad credit history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aabe2f35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a cross-tabulation of 'Property Area' and 'Loan Status' variables\n",
    "pd.crosstab(loan_data.Property_Area, loan_data.Loan_Status).plot(kind=\"bar\", figsize=(8, 4))\n",
    "\n",
    "# Add a title to the plot\n",
    "plt.title('Property Area VS Loan Status')\n",
    "\n",
    "# Label the x-axis\n",
    "plt.xlabel('Property Area')\n",
    "\n",
    "# Label the y-axis\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Rotate the x-axis labels to avoid overlap\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eb07aaa",
   "metadata": {},
   "source": [
    "Most of loan that got accepted has property in **Semiurban** compared to *Urban* and *Rural*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a13f1d31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a cross-tabulation of 'Gender' and 'Married' variables\n",
    "pd.crosstab(loan_data.Gender, loan_data.Married).plot(kind=\"bar\", figsize=(8, 4))\n",
    "\n",
    "# Add a title to the plot\n",
    "plt.title('Gender VS Married')\n",
    "\n",
    "# Label the x-axis\n",
    "plt.xlabel('Gender')\n",
    "\n",
    "# Label the y-axis\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Rotate the x-axis labels to avoid overlap\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22bc5517",
   "metadata": {},
   "source": [
    "Most **male applicants** are already **married** compared to **female applicants**. Also, the number of **not married male applicants** are **higher** compare to **female applicants** that had **not married**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0e34f823",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a cross-tabulation of 'Gender' and 'Loan Status' variables\n",
    "pd.crosstab(loan_data.Education, loan_data.Loan_Status).plot(kind=\"bar\", figsize=(8, 5))\n",
    "\n",
    "# Add a title to the plot\n",
    "plt.title('Education Status VS Loan Status')\n",
    "\n",
    "# Label the x-axis\n",
    "plt.xlabel('Education')\n",
    "\n",
    "# Label the y-axis\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Rotate the x-axis labels to avoid overlap\n",
    "plt.xticks(rotation=0)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0b2a119",
   "metadata": {},
   "source": [
    "The analysis appears a **graduate applicant** significantly increases the chances of *loan approval*. Because the percentage of people who graduated and were approved is much better than who didn't graduate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49159852",
   "metadata": {},
   "source": [
    "# <font color=#4863A0> Data Cleaning 🧹</font>\n",
    "Data cleaning refers to the process of **identifying**, **correcting**, and **removing errors**, **inconsistencies**, and **inaccuracies** from a dataset. It involves transforming raw data into a clean and structured format that is suitable for analysis, modeling, and other data-related tasks, including:\n",
    "- Remove Unneeded Features\n",
    "- Handling Missing Values\n",
    "- Encoding Categorical Variables\n",
    "- Handling Outliers\n",
    "- Handling Duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd65a579",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">1. Remove Unneeded Features</font>\n",
    "As Loan_ID is completely unique and not correlated with any of the other column, So we will drop it using .drop() function.\n",
    "- axis = 1 means column , axis = 0 means row.\n",
    "- inplace = True means the changes are reflected in the original DataFrame, inplace = False (default) means leaving the original DataFrame unchanged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "da6ac6ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Gender             601 non-null    object \n",
      " 1   Married            611 non-null    object \n",
      " 2   Dependents         599 non-null    object \n",
      " 3   Education          614 non-null    object \n",
      " 4   Self_Employed      582 non-null    object \n",
      " 5   ApplicantIncome    614 non-null    int64  \n",
      " 6   CoapplicantIncome  614 non-null    float64\n",
      " 7   LoanAmount         592 non-null    float64\n",
      " 8   Loan_Amount_Term   600 non-null    float64\n",
      " 9   Credit_History     564 non-null    float64\n",
      " 10  Property_Area      614 non-null    object \n",
      " 11  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 57.7+ KB\n",
      "None\n",
      "\u001b[7m\u001b[32mThe task has been completed without any errors....\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Dropping Loan_ID column\n",
    "loan_data.drop(['Loan_ID'], axis = 1 , inplace = True)\n",
    "\n",
    "# Check the Loan_ID is dropped\n",
    "print(loan_data.info())\n",
    "\n",
    "# Check and ensure running\n",
    "print(colored(\"The task has been completed without any errors....\", \"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0630b4c",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">2. Handling Missing Values</font>\n",
    "\n",
    "**Missing values** are data points that are absent or not recorded for certain variables in a dataset. This can happen because of mistakes such as data entry errors, equipment failures, or simply because the information is not available.\n",
    "\n",
    " **How to detect missing values?**\n",
    " - Look for blank cells and use data analysis tools that can identify missing values like .isnull() functoin.\n",
    "\n",
    " **Decision options:**\n",
    "1. Keep them \n",
    "    - Not ideal, as missing values can lead to biased analyses and inaccurate predictions.\n",
    "2. Reassign new values to them (Using specific techniques)\n",
    "    - Imputation (Y):\n",
    "        - Replace missing values with estimated or calculated values based on the available data. This can be done using various methods such as:\n",
    "            1. **Mean/Median Imputation:** Replace with the average (mean) or middle value (median) for numerical data.\n",
    "            2. **Mode Imputation:** Replace with the most frequent value.\n",
    "    - Forward/Backward Fill (N):\n",
    "        - For time-series data, missing values can be filled with the last observed value (forward fill) or the next observed value (backward fill).\n",
    "    - Interpolation (N):\n",
    "        - Estimate missing values based on the trend or pattern observed in the existing data points.\n",
    "3. Delete them (Sometimes necessary, especially if missing values are extensive and cannot be reasonably imputed without introducing significant bias.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8555cea7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender               13\n",
      "Married               3\n",
      "Dependents           15\n",
      "Education             0\n",
      "Self_Employed        32\n",
      "ApplicantIncome       0\n",
      "CoapplicantIncome     0\n",
      "LoanAmount           22\n",
      "Loan_Amount_Term     14\n",
      "Credit_History       50\n",
      "Property_Area         0\n",
      "Loan_Status           0\n",
      "dtype: int64\n",
      "_________________________________________________________________\n",
      "\u001b[7m\u001b[32mTotally, there are 149 null values in the dataset.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "null_counts = loan_data.isnull().sum()\n",
    "\n",
    "# Display the number of null values\n",
    "print(null_counts)\n",
    "print(\"_________________________________________________________________\")\n",
    "print(colored(f\"Totally, there are {null_counts.sum()} null values in the dataset.\", \"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d726a7bb",
   "metadata": {},
   "source": [
    "<font size=\"4\" face=\"WildWest\">There are two types of data:</font>\n",
    "- Numerical Data\n",
    "- Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2c752dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill the missing values for numerical data, .fillna = fill (NaN) values\n",
    "'''\n",
    "## 1- Mean Imputation:\n",
    "loan_data[\"Credit_History\"] = loan_data[\"Credit_History\"].fillna(loan_data[\"Credit_History\"].mean())\n",
    "\n",
    "## 1- Median Imputation:\n",
    "loan_data[\"Credit_History\"] = loan_data[\"Credit_History\"].fillna(loan_data[\"Credit_History\"].median())\n",
    "'''\n",
    "\n",
    "## 2- Mode Imputatoin:\n",
    "# The best way fill in the Credit History is the mode (Most common valus)\n",
    "# [0] This ensures that if there are multiple mode values, only the first one is selected.\n",
    "loan_data[\"Credit_History\"] = loan_data[\"Credit_History\"].fillna(loan_data[\"Credit_History\"].mode()[0])\n",
    "\n",
    "'''\n",
    "## 3- Forward Fill\n",
    "loan_data[\"Credit_History\"] = loan_data[\"Credit_History\"].fillna(method='ffill')\n",
    "\n",
    "## 3- Backward Fill\n",
    "loan_data[\"Credit_History\"] = loan_data[\"Credit_History\"].fillna(method='bfill')\n",
    "\n",
    "## 4- Interpolation\n",
    "loan_data[\"Credit_History\"] = loan_data[\"Credit_History\"].interpolate(method='linear') \n",
    "'''\n",
    "############################################################################################\n",
    "\n",
    "## 1- Mean Imputation:\n",
    "# The best way fill in the Loan Amount is the Mean (The average).\n",
    "loan_data[\"LoanAmount\"] = loan_data[\"LoanAmount\"].fillna(loan_data[\"LoanAmount\"].mean())\n",
    "\n",
    "'''\n",
    "## 1- Median Imputation:\n",
    "loan_data[\"LoanAmount\"] = loan_data[\"LoanAmount\"].fillna(loan_data[\"LoanAmount\"].median())\n",
    "\n",
    "\n",
    "## 2- Mode Imputatoin:\n",
    "loan_data[\"LoanAmount\"] = loan_data[\"LoanAmount\"].fillna(loan_data[\"LoanAmount\"].mode()[0])\n",
    "'''\n",
    "############################################################################################\n",
    "'''\n",
    "## 1- Mean Imputation:\n",
    "loan_data[\"Loan_Amount_Term\"] = loan_data[\"Loan_Amount_Term\"].fillna(loan_data[\"Loan_Amount_Term\"].mean())\n",
    "\n",
    "## 1- Median Imputation:\n",
    "loan_data[\"Loan_Amount_Term\"] = loan_data[\"Loan_Amount_Term\"].fillna(loan_data[\"Loan_Amount_Term\"].median())\n",
    "'''\n",
    "\n",
    "## 2- Mode Imputatoin:\n",
    "# The best way fill in the Credit History is the mode (Most common valus)\n",
    "loan_data[\"Loan_Amount_Term\"] = loan_data[\"Loan_Amount_Term\"].fillna(loan_data[\"Loan_Amount_Term\"].mode()[0])\n",
    "\n",
    "############################################################################################\n",
    "\n",
    "# Fill the missing values for categorical data.\n",
    "loan_data[\"Gender\"] = loan_data[\"Gender\"].fillna(loan_data[\"Gender\"].mode()[0])\n",
    "loan_data[\"Married\"] = loan_data[\"Married\"].fillna(loan_data[\"Married\"].mode()[0])\n",
    "loan_data[\"Dependents\"] = loan_data[\"Dependents\"].fillna(loan_data[\"Dependents\"].mode()[0])\n",
    "loan_data[\"Self_Employed\"] = loan_data[\"Self_Employed\"].fillna(loan_data[\"Self_Employed\"].mode()[0])\n",
    "\n",
    "\n",
    "# Delete them (but it is not the best choose for me [Low accuracy] )\n",
    "# loan_data = loan_data.dropna(subset=['Gender', 'Married', 'Dependents', 'Self_Employed', 'LoanAmount', 'Loan_Amount_Term', 'Credit_History'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "978f0e9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender               0\n",
      "Married              0\n",
      "Dependents           0\n",
      "Education            0\n",
      "Self_Employed        0\n",
      "ApplicantIncome      0\n",
      "CoapplicantIncome    0\n",
      "LoanAmount           0\n",
      "Loan_Amount_Term     0\n",
      "Credit_History       0\n",
      "Property_Area        0\n",
      "Loan_Status          0\n",
      "dtype: int64\n",
      "_________________________________________________________________\n",
      "\u001b[7m\u001b[32mTotally, there are 0 null values in the dataset.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values after handling\n",
    "null_counts = loan_data.isnull().sum()\n",
    "\n",
    "# Display the number of null values after handling\n",
    "print(null_counts)\n",
    "print(\"_________________________________________________________________\")\n",
    "print(colored(f\"Totally, there are {null_counts.sum()} null values in the dataset.\", \"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0403c61c",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">3. Encoding Categorical Variables</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c3ecba74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[7m\u001b[34mColumn Names:\u001b[0m\n",
      "- ApplicantIncome\n",
      "- CoapplicantIncome\n",
      "- LoanAmount\n",
      "- Loan_Amount_Term\n",
      "- Credit_History\n",
      "- Gender\n",
      "- Married\n",
      "- Dependents_0\n",
      "- Dependents_1\n",
      "- Dependents_2\n",
      "- Dependents_3+\n",
      "- Education\n",
      "- Self_Employed\n",
      "- Property_Area_Rural\n",
      "- Property_Area_Semiurban\n",
      "- Property_Area_Urban\n",
      "- Loan_Status\n",
      "The shape = (614, 17)\n",
      "_______________________________________________\n",
      "\u001b[7m\u001b[34mTransformed Target Variable (Y):\u001b[0m\n",
      "0    1\n",
      "1    0\n",
      "2    1\n",
      "3    1\n",
      "4    1\n",
      "Name: Loan_Status, dtype: uint8\n"
     ]
    }
   ],
   "source": [
    "# Convert categorical variables into dummy/indicator variables through a process called \"one-hot\" encoding\n",
    "loan_data = pd.get_dummies(loan_data)\n",
    "\n",
    "# Drop unneeded features\n",
    "loan_data = loan_data.drop(['Gender_Female', \n",
    "                            'Married_No', \n",
    "                            'Education_Not Graduate',\n",
    "                            'Self_Employed_No', \n",
    "                            'Loan_Status_N'], axis = 1) # axis = 1 for column\n",
    "\n",
    "# Rename existing columns\n",
    "newColunmsNames = {'Gender_Male': 'Gender', \n",
    "                   'Married_Yes': 'Married', \n",
    "                   'Education_Graduate': 'Education', \n",
    "                   'Self_Employed_Yes': 'Self_Employed',\n",
    "                   'Loan_Status_Y': 'Loan_Status'}\n",
    "\n",
    "# Assigning new columns names\n",
    "loan_data.rename(columns=newColunmsNames, inplace=True)\n",
    "\n",
    "# Display the columns names and shape of the transformed dataset\n",
    "## Get the column names of the DataFrame\n",
    "column_names = loan_data.columns.tolist()\n",
    "\n",
    "## Print the column names in a readable format\n",
    "print(colored(\"Column Names:\", \"blue\",attrs=['reverse']))\n",
    "\n",
    "for col in column_names:\n",
    "    print(f\"- {col}\")\n",
    "    \n",
    "print(\"The shape =\",loan_data.shape)\n",
    "print(\"_______________________________________________\")\n",
    "\n",
    "# Display the first 5 rows of the transformed target variable\n",
    "print(colored(\"Transformed Target Variable (Y):\",\"blue\", attrs=['reverse']))\n",
    "print(loan_data['Loan_Status'].head())  # First 5 rows by default"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd0bb912",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">4. Handling Outliers</font>\n",
    "\n",
    "**Outliers** are data points that significantly differ from the rest of the observations in a dataset, often due to errors in data collection or genuine extreme values.\n",
    "\n",
    " **How to detect outliers?**\n",
    "- Visualization data (Numerical values)\n",
    "\n",
    " **Decision options:**\n",
    "1. Keep them (Not the best solution, I try it and not get high accuracy, \"There is better\")\n",
    "2. Reassign new values to them (Using specific techniques)\n",
    "    - Capping Outliers (N)\n",
    "        - Instead of removing outliers entirely, replace extreme values with more reasonable thresholds.\n",
    "    - Robust Scaling (Y)\n",
    "        - Use scaling methods less sensitive to outliers (e.g., IQR scaling, standardization with robust estimators).\n",
    "            - Robust Scaling Function\n",
    "            - IQR scaling\n",
    "    - Replace with mean (N)\n",
    "        - Replacing the outliers with the mean value\n",
    "3. Delete them (N)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80ed66db",
   "metadata": {},
   "source": [
    "Showing outliers in this process before making any handling, but in another graph than the previous graph I used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8fbfd9ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "# Only variables that have outliers\n",
    "outliersColumns = loan_data.get([\"ApplicantIncome\", \"CoapplicantIncome\", \"LoanAmount\", \"Loan_Amount_Term\"])\n",
    "\n",
    "# Add outliers to the plot\n",
    "sns.stripplot(data=outliersColumns, color=\"red\", jitter=0.3, size=5)\n",
    "\n",
    "# Set the axis labels and title\n",
    "plt.title(\"Outliers\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "682386f2",
   "metadata": {},
   "source": [
    "**The Process**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f38cde24",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(136, 17)\n",
      "\u001b[7m\u001b[32mThe task has been completed without any errors....\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Reassign new values to them\n",
    "\n",
    "'''\n",
    "# 1- Capping Outliers\n",
    "loan_data.loc[loan_data['ApplicantIncome'] > 20000, 'ApplicantIncome'] = 20000\n",
    "loan_data.loc[loan_data['CoapplicantIncome'] > 10000, 'CoapplicantIncome'] = 10000\n",
    "'''\n",
    "'''\n",
    "# 2- Robust Scaling\n",
    "## Method 1\n",
    "\n",
    "# import Robust Scaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "scaler = RobustScaler()\n",
    "loan_data[['ApplicantIncome', 'CoapplicantIncome']] = scaler.fit_transform(loan_data[['ApplicantIncome', 'CoapplicantIncome']])\n",
    "\n",
    "## Method 2\n",
    "\n",
    "# IQR Scaling\n",
    "Q1 = loan_data.quantile(0.25)\n",
    "Q3 = loan_data.quantile(0.75)\n",
    "\n",
    "# Calcuate the Interquartile Range (IQR)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Removing Qutliers\n",
    "loan_data = loan_data[~((loan_data < (Q1 - 1.5 * IQR)) |(loan_data > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
    "\n",
    "'''\n",
    "\n",
    "'''\n",
    "# 3- Replace with mean\n",
    "loan_data.loc[loan_data['ApplicantIncome'] > 20000, 'ApplicantIncome'] = loan_data['ApplicantIncome'].mean()\n",
    "loan_data.loc[loan_data['CoapplicantIncome'] > 10000, 'CoapplicantIncome'] = loan_data['CoapplicantIncome'].mean()\n",
    "'''\n",
    "'''\n",
    "# Delete the outliers\n",
    "# The data before deleting outliers \n",
    "print(\"Before Removing the outliers\", loan_data.shape)\n",
    "\n",
    "# Deleting outliers (Removing the number of observation where the applicant income is more than 20k)\n",
    "loan_data = loan_data[loan_data['ApplicantIncome']<20000]\n",
    "\n",
    "# Deleting outliers (Removing the number of observation where the co-applicant income is more than 10k)\n",
    "loan_data = loan_data[loan_data['CoapplicantIncome']<10000]\n",
    "\n",
    "#The data after deleting outliers\n",
    "print(\"After Removing the outliers\", loan_data.shape)\n",
    "'''\n",
    "## Method 2\n",
    "\n",
    "# IQR Scaling\n",
    "Q1 = loan_data.quantile(0.25)\n",
    "Q3 = loan_data.quantile(0.75)\n",
    "\n",
    "# Calcuate the Interquartile Range (IQR)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Removing Qutliers\n",
    "loan_data = loan_data[~((loan_data < (Q1 - 1.5 * IQR)) |(loan_data > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
    "\n",
    "# printing shape\n",
    "print(loan_data.shape)\n",
    "\n",
    "# Check and ensure running\n",
    "print(colored(\"The task has been completed without any errors....\", \"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9aabb46b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(136, 17)\n",
      "\u001b[7m\u001b[32mWe will see the distribution after Scaling\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Square Root Transformation - to normalized the distribution.\n",
    "loan_data.ApplicantIncome = np.sqrt(loan_data.ApplicantIncome)\n",
    "loan_data.CoapplicantIncome = np.sqrt(loan_data.CoapplicantIncome)\n",
    "loan_data.LoanAmount = np.sqrt(loan_data.LoanAmount)\n",
    "\n",
    "print(loan_data.shape)\n",
    "print(colored(\"We will see the distribution after Scaling\",\"green\", attrs=[\"reverse\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84e7b25f",
   "metadata": {},
   "source": [
    "Visualize data distribution after handling outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ddba4402",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "# Only variables that have outliers\n",
    "outliersColumns = loan_data.get([\"ApplicantIncome\", \"CoapplicantIncome\", \"LoanAmount\", \"Loan_Amount_Term\"])\n",
    "\n",
    "# Add outliers to the plot\n",
    "sns.stripplot(data=outliersColumns, color=\"red\", jitter=0.3, size=5)\n",
    "\n",
    "# Set the axis labels and title\n",
    "plt.title(\"Outliers\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a2af779",
   "metadata": {},
   "source": [
    "Another Graphe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aea6263d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram distribution for numerical values\n",
    "\n",
    "# Set the seaborn theme palette\n",
    "sns.set_theme(palette=\"flare\")\n",
    "\n",
    "def plot_distribution(column, title):\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    sns.histplot(data=loan_data, x=column, kde=True)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(column)\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.show()\n",
    "\n",
    "# Plot distribution for each numerical column\n",
    "plot_distribution(\"ApplicantIncome\", \"Applicant Income Distribution\")\n",
    "plot_distribution(\"CoapplicantIncome\", \"Coapplicant Income Distribution\")\n",
    "plot_distribution(\"LoanAmount\", \"Loan Amount Distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7fa6109",
   "metadata": {},
   "source": [
    "As can be seen, the distribution after using log transformation are much better compared to original distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d9ebf4",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">5. Handling Duplicates</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9f10ec52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[36mNumber of duplicate entries found in the ApplicantIncome column: 10\u001b[0m\n",
      "\u001b[1m\u001b[36mNumber of duplicate entries found in the CoapplicantIncome column: 45\u001b[0m\n",
      "\u001b[1m\u001b[36mNumber of duplicate entries found in the LoanAmount column: 57\u001b[0m\n",
      "\u001b[1m\u001b[36mNumber of duplicate entries found in the Loan_Amount_Term column: 135\u001b[0m\n",
      "\u001b[1m\u001b[36mNumber of duplicate entries found in the Credit_History column: 135\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# List of column names to check for duplicates (Numerical values)\n",
    "columns_to_check = ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term', 'Credit_History']\n",
    "\n",
    "# Iterate over each column name\n",
    "for column_name in columns_to_check:\n",
    "    # Checking for duplicate entries in the current column\n",
    "    duplicate_count = loan_data[column_name].duplicated().sum()\n",
    "    \n",
    "    # Output the result with a descriptive message\n",
    "    if duplicate_count == 0:\n",
    "        print(colored(f\"No duplicate entries found in the {column_name} column.\", \"green\", attrs=['reverse']))\n",
    "    else:\n",
    "        print(colored(f\"Number of duplicate entries found in the {column_name} column: {duplicate_count}\", \"cyan\", attrs=['bold']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8516eb71",
   "metadata": {},
   "source": [
    "**We don't need to handle duplicate values because it isn't affecting the accuracy or integrity of the analysis or model being performed.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d934eeb8",
   "metadata": {},
   "source": [
    "# <font color=#4863A0> Data Preprocessing 👨‍💻</font>\n",
    "It involves *preparing* and *transforming raw data* into a suitable format for analysis and **model training**. Effective data processing ensures that the machine learning algorithms can extract meaningful patterns and make accurate predictions, includes:\n",
    "- Splitting data into \"Features\" - \"Target\"\n",
    "- SMOTE Technique\n",
    "- Data Re-scaling and Normalizing the features\n",
    "- Splitting data into \"Training\" - \"Testing\" Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "488ded67",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">1. Splitting data into \"Features\" - \"Target\"</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a5dfdacd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X: (136, 16)\n",
      "Shape of Y: (136,)\n"
     ]
    }
   ],
   "source": [
    "# Features \"Inputs\" (X)\n",
    "X = loan_data.drop(columns=['Loan_Status'])\n",
    "\n",
    "# Target variable \"Outputs\" (Y)\n",
    "Y = loan_data['Loan_Status']\n",
    "\n",
    "# Print the shapes of X and Y to verify the splitting\n",
    "print(\"Shape of X:\", X.shape)\n",
    "print(\"Shape of Y:\", Y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0606832",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">2. SMOTE Technique</font>\n",
    "In previous exploration, it can be seen that the number between **accepted** and **rejected** loan is *imbalanced*. In this section, oversampling technique will be used to avoid overfitting. But recently I discovered that not using it will give higher accuracy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2c6ea7d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"# Import the SMOTE (Synthetic Minority Over-sampling Technique) from the imbalanced-learn library\\nfrom imblearn.over_sampling import SMOTE\\n\\n# Resample the dataset using SMOTE to handle class imbalance\\n# X: Features, Y: Target variable\\nX, Y = SMOTE().fit_resample(X, Y)\\n\\n# SMOTE().fit_resample(X, Y) fits the SMOTE algorithm to the dataset (X, Y) and generates synthetic samples to balance the classes.\\n# X_resampled: Resampled features, Y_resampled: Resampled target variable\\n\\n# You can also specify parameters for SMOTE such as sampling_strategy, k_neighbors, etc. if needed, but I don't need.\\n\\nprint(X.shape)\\nprint(Y.shape)\\n\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''# Import the SMOTE (Synthetic Minority Over-sampling Technique) from the imbalanced-learn library\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Resample the dataset using SMOTE to handle class imbalance\n",
    "# X: Features, Y: Target variable\n",
    "X, Y = SMOTE().fit_resample(X, Y)\n",
    "\n",
    "# SMOTE().fit_resample(X, Y) fits the SMOTE algorithm to the dataset (X, Y) and generates synthetic samples to balance the classes.\n",
    "# X_resampled: Resampled features, Y_resampled: Resampled target variable\n",
    "\n",
    "# You can also specify parameters for SMOTE such as sampling_strategy, k_neighbors, etc. if needed, but I don't need.\n",
    "\n",
    "print(X.shape)\n",
    "print(Y.shape)\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9ac5fc6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a count plot to visualize the distribution of loan status (Check the imbalanced)\n",
    "sns.countplot(x=Y, data=loan_data, palette=\"flare\")\n",
    "\n",
    "# Label the y-axis as \"Loan Status\"\n",
    "plt.ylabel('Total')\n",
    "\n",
    "# Label the x-axis as \"Total\"\n",
    "plt.xlabel('Loan Status')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1990fc75",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">3. Data Re-scaling and Normalizing the features</font>\n",
    "***Min-Max Scaling:*** transforms the features so that they fall within a specified range, typically between 0 and 1.\n",
    "\n",
    "***Standardization:*** the features so that they have a mean of 0 , typically between -1 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "66eb7e34",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rescale and normalize the features\n",
    "'''\n",
    "# Standardization (Normalization)\n",
    "standard_scaler = StandardScaler()\n",
    "X = standard_scaler.fit_transform(X)\n",
    "'''\n",
    "\n",
    "# Min-Max Scaling (Rescaling)\n",
    "min_max_scaler = MinMaxScaler()\n",
    "X = min_max_scaler.fit_transform(X)\n",
    "\n",
    "#I will choose one of them in the future part \"model selection\" based on the highest accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da770737",
   "metadata": {},
   "source": [
    "**Why we use fit_transform() on training data but transform() on the test data?**\n",
    "    \n",
    "The fit_transform() method is used on training data to calculate scaling parameters like **mean** and **standard deviation**, then applies scaling. For test data, we apply the same scaling transformation without recalculating parameters, ensuring consistency for fair comparison and accurate evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13449e21",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">4. Splitting data into \"Training\" - \"Testing\" Data</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9002279f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train: (108, 16)\n",
      "Shape of X_test: (28, 16)\n",
      "Shape of Y_train: (108,)\n",
      "Shape of Y_test: (28,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into training and testing sets\n",
    "# X: Features, Y: Target variable\n",
    "# test_size=0.2 specifies that 20% of the data will be used for testing and 80% for training\n",
    "# random_state=0 sets the random seed for reproducibility\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
    "\n",
    "# Print the shapes of the training and testing sets to verify the splitting\n",
    "print(\"Shape of X_train:\", X_train.shape)\n",
    "print(\"Shape of X_test:\", X_test.shape)\n",
    "print(\"Shape of Y_train:\", Y_train.shape)\n",
    "print(\"Shape of Y_test:\", Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2295a2",
   "metadata": {},
   "source": [
    "# <font color=#4863A0> Model Selection 🧩</font>\n",
    "Model selection is an essential step in machine learning that involves choosing **the most appropriate model for a given problem**. The goal of model selection is to find a model that generalizes well to unseen data and provides the best performance on the task at hand.\n",
    "- Importing models for selection process\n",
    "- Training our models.\n",
    "    1. Decision Tree Classifier\n",
    "    2. Random Forest Classifier (Highest Accuracy) \n",
    "    3. Naives Bayes\n",
    "        - GaussianNB (Suitable for continuous data)\n",
    "        - BernoulliNB (Suitable for binary value)\n",
    "    4. Logistic Regression \n",
    "    5. Ridge Classifier CV\n",
    "    6. K-Nearest Neighbors (KNN)\n",
    "- For future searching\n",
    "    7. Support Vector Classifier\n",
    "    8. Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbcc8b34",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">1. Importing models for selection process</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "54c4578a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[7m\u001b[32mTHE REQUIRED LIBRARIES WERE SUCCESSFULLY IMPORTED...\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier  # For Decision Tree Classifier Model\n",
    "from sklearn.ensemble import RandomForestClassifier # For Random Forest Classifier Model\n",
    "from sklearn.neighbors import KNeighborsClassifier # For K-Nearest Neighbors Model\n",
    "from sklearn.naive_bayes import GaussianNB,BernoulliNB  # For Gaussian,Bernoulli Naive Bayes Classifier Model\n",
    "from sklearn.linear_model import RidgeClassifierCV, LogisticRegression # For Ridge Classifier Cross-validated and Logistic Regression Models\n",
    "from sklearn.svm import SVC  # For Support Vector Classifier Model\n",
    "from sklearn.ensemble import GradientBoostingClassifier  # For Gradient Boosting Classifier Model\n",
    "\n",
    "# Printing a success message in green color\n",
    "print(colored(\"THE REQUIRED LIBRARIES WERE SUCCESSFULLY IMPORTED...\", \"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4303602e",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">2. Training the Machine Learning Algorithms</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "587a96e7",
   "metadata": {},
   "source": [
    "- Decision Tree Classifier"
   ]
  },
  {
   "attachments": {
    "Image%205-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "c4f9eb7b",
   "metadata": {},
   "source": [
    "![Image%205-2.png](attachment:Image%205-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dc1dc700",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree best accuracy (Training): 100.00%\n",
      "Decision Tree best accuracy (Testing): 96.43%\n",
      "\u001b[7m\u001b[32mThe Decision Tree model has been trained successfully\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree Classifier Parameters\n",
    "'''\n",
    "sklearn.tree.DecisionTreeClassifier(*, criterion='gini', \n",
    "                                    splitter='best', max_depth=None, \n",
    "                                    min_samples_split=2, min_samples_leaf=1, \n",
    "                                    min_weight_fraction_leaf=0.0, max_features=None, \n",
    "                                    random_state=None, max_leaf_nodes=None, \n",
    "                                    min_impurity_decrease=0.0, class_weight=None, \n",
    "                                    ccp_alpha=0.0, monotonic_cst=None)\n",
    "'''\n",
    "\n",
    "# max_depth (The maximum depth of the tree \"هو عدد الطبقات لاطول فرع من فروع الشجرة\")\n",
    "# min_samples_split (The minimum number of samples required to split an internal node \"2 by default\")\n",
    "# min_samples_leaf (The minimum number of samples required to be at a leaf node)\n",
    "# max_features (The number of features to consider when looking for the best split)\n",
    "\n",
    "# Initialize lists to store training and testing accuracies\n",
    "scoreListDT_Train = []\n",
    "scoreListDT_Test = []\n",
    "\n",
    "# Iterate over different values of max_depth\n",
    "for i in range(1, 20):\n",
    "    # Iterate over different values of min_samples_leaf\n",
    "    for j in range(1, 5):\n",
    "        # Create a Decision Tree model with the different values of max_depth, min_samples_leaf, and fixed max_features\n",
    "        Model1 = DecisionTreeClassifier(max_depth=i, min_samples_leaf=j, max_features=2)\n",
    "\n",
    "        # Fit the model on the training data\n",
    "        Model1.fit(X_train, Y_train)\n",
    "\n",
    "        # Calculate and store the training accuracy\n",
    "        scoreListDT_Train.append(Model1.score(X_train, Y_train))\n",
    "\n",
    "        # Calculate and store the testing accuracy\n",
    "        scoreListDT_Test.append(Model1.score(X_test, Y_test))\n",
    "\n",
    "# Find the maximum accuracy for both training and testing\n",
    "DT_Accuracy_Train = max(scoreListDT_Train) \n",
    "DT_Accuracy_Test = max(scoreListDT_Test)\n",
    "\n",
    "# Print the best accuracies achieved\n",
    "print(f\"Decision Tree best accuracy (Training): {DT_Accuracy_Train*100:.2f}%\")\n",
    "print(f\"Decision Tree best accuracy (Testing): {DT_Accuracy_Test*100:.2f}%\")\n",
    "\n",
    "# Print a success message indicating that the model has been trained successfully\n",
    "print(colored(\"The Decision Tree model has been trained successfully\",\"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e2d8975",
   "metadata": {},
   "source": [
    "- Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f212d7eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest best accuracy (Training): 100.00%\n",
      "Random Forest best accuracy (Testing): 96.43%\n",
      "\u001b[7m\u001b[32mThe Random Forest model has been trained successfully\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classifier Parameters\n",
    "'''\n",
    "sklearn.ensemble.RandomForestClassifier(n_estimators=100, *, criterion='gini', \n",
    "                                        max_depth=None, min_samples_split=2, min_samples_leaf=1, \n",
    "                                        min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None, \n",
    "                                        min_impurity_decrease=0.0, bootstrap=True, oob_score=False, \n",
    "                                        n_jobs=None, random_state=None, verbose=0, \n",
    "                                        warm_start=False, class_weight=None, ccp_alpha=0.0, \n",
    "                                        max_samples=None, monotonic_cst=None)\n",
    "'''\n",
    "\n",
    "# n_estimators (The number of trees in the forest\"عدد الاشجار في الغابة\")\n",
    "# max_depth (The maximum depth of the tree.)\n",
    "# random_state (Controls both the randomness of the bootstrapping of the samples used when building trees)\n",
    "\n",
    "# Initialize lists to store training and testing accuracies\n",
    "scoreListRF_Train = []\n",
    "scoreListRF_Test = []\n",
    "\n",
    "'''\n",
    "max_dep      ----------> (1, 5),(1, 10) \n",
    "rand_state   ----------> (1, 35),(1, 50)\n",
    "n_est        ----------> (1, 30),(1, 30)\n",
    "'''\n",
    "\n",
    "# Iterate over different values of max_depth\n",
    "for max_dep in range(1, 10):\n",
    "    # Iterate over different values of random_state\n",
    "    for rand_state in range(1, 50):\n",
    "        # Iterate over different values of n_estimators\n",
    "        for n_est in range(1, 30):\n",
    "            # Create a Random Forest model with the different values of max_depth, random_state, and n_estimators\n",
    "            Model2 = RandomForestClassifier(n_estimators=n_est, random_state=rand_state, max_depth=max_dep)            \n",
    "            \n",
    "            # Fit the model on the training data\n",
    "            Model2.fit(X_train, Y_train)\n",
    "            \n",
    "            # Calculate and store the training accuracy\n",
    "            scoreListRF_Train.append(Model2.score(X_train, Y_train))\n",
    "            \n",
    "            # Calculate and store the testing accuracy\n",
    "            scoreListRF_Test.append(Model2.score(X_test, Y_test))\n",
    "\n",
    "# Find the maximum accuracy for both training and testing\n",
    "RF_Accuracy_Train = max(scoreListRF_Train) \n",
    "RF_Accuracy_Test = max(scoreListRF_Test)\n",
    "\n",
    "# Print the best accuracies achieved\n",
    "print(f\"Random Forest best accuracy (Training): {RF_Accuracy_Train*100:.2f}%\")\n",
    "print(f\"Random Forest best accuracy (Testing): {RF_Accuracy_Test*100:.2f}%\")\n",
    "\n",
    "# Print a success message indicating that the model has been trained successfully\n",
    "print(colored(\"The Random Forest model has been trained successfully\",\"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df06b2f",
   "metadata": {},
   "source": [
    "- Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b0e0465b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes best accuracy (Training): 79.63%\n",
      "Gaussian Naive Bayes best accuracy (Testing): 89.29%\n",
      "\u001b[7m\u001b[32mThe Gaussian Naive Bayes model has been trained successfully\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Gaussian Naive Bayes Parameters\n",
    "'''\n",
    "sklearn.naive_bayes.GaussianNB(*, priors=None, var_smoothing=1e-09)\n",
    "'''\n",
    "\n",
    "# var_smoothing (Portion of the largest variance of all features that is added to variances for calculation stability.)\n",
    "\n",
    "# Initialize lists to store training and testing accuracies\n",
    "scoreListGNB_Train = []\n",
    "scoreListGNB_Test = []\n",
    "\n",
    "# Iterate over different values of var_smoothing\n",
    "for i in range(1, 9):\n",
    "    # Create a Gaussion Naive Bayes Model with the different values of var_smoothing\n",
    "    Model3_1 = GaussianNB(var_smoothing=10**(-i))\n",
    "\n",
    "    # Fit the model on the training data\n",
    "    Model3_1.fit(X_train, Y_train)\n",
    "    \n",
    "    # Calculate and store the training accuracy\n",
    "    scoreListGNB_Train.append(Model3_1.score(X_train, Y_train))\n",
    "    \n",
    "    # Calculate and store the testing accuracy\n",
    "    scoreListGNB_Test.append(Model3_1.score(X_test, Y_test))\n",
    "\n",
    "# Find the maximum accuracy for both training and testing\n",
    "GNB_Accuracy_Train = max(scoreListGNB_Train) \n",
    "GNB_Accuracy_Test = max(scoreListGNB_Test)\n",
    "\n",
    "# Print the best accuracies achieved\n",
    "print(f\"Gaussian Naive Bayes best accuracy (Training): {GNB_Accuracy_Train*100:.2f}%\")\n",
    "print(f\"Gaussian Naive Bayes best accuracy (Testing): {GNB_Accuracy_Test*100:.2f}%\")\n",
    "\n",
    "# Print a success message indicating that the model has been trained successfully\n",
    "print(colored(\"The Gaussian Naive Bayes model has been trained successfully\",\"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27b9dcd0",
   "metadata": {},
   "source": [
    "- Bernoulli Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6faedce1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bernoulli Naive Bayes best accuracy (Training): 80.56%\n",
      "Bernoulli Naive Bayes best accuracy (Testing): 92.86%\n",
      "\u001b[7m\u001b[32mThe Bernoulli Naive Bayes model has been trained successfully\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Bernoulli Naive Bayes Parameters\n",
    "'''\n",
    "sklearn.naive_bayes.BernoulliNB(*, alpha=1.0, force_alpha=True, binarize=0.0, fit_prior=True, class_prior=None)\n",
    "'''\n",
    "\n",
    "# alpha (Additive (Laplace/Lidstone) smoothing parameter)\n",
    "# force_alpha (If False and alpha is close to zero, it's adjusted to 1e-10 to prevent numerical errors; otherwise, alpha stays unchanged.)\n",
    "# binarize (Sets the threshold for converting sample features to binary values; if None, assumes the input is already binary.)\n",
    "\n",
    "# Create a Bernoulli Naive Bayes Model\n",
    "Model3_2= BernoulliNB()\n",
    "\n",
    "# Fit the model on the training data\n",
    "Model3_2.fit(X_train, Y_train)\n",
    "    \n",
    "# Calculate and store the training accuracy\n",
    "BNB_Accuracy_Train = Model3_2.score(X_train, Y_train)\n",
    "    \n",
    "# Calculate and store the testing accuracy\n",
    "BNB_Accuracy_Test = Model3_2.score(X_test, Y_test)\n",
    "\n",
    "# Print the best accuracies achieved\n",
    "print(f\"Bernoulli Naive Bayes best accuracy (Training): {BNB_Accuracy_Train*100:.2f}%\")\n",
    "print(f\"Bernoulli Naive Bayes best accuracy (Testing): {BNB_Accuracy_Test*100:.2f}%\")\n",
    "\n",
    "# Print a success message indicating that the model has been trained successfully\n",
    "print(colored(\"The Bernoulli Naive Bayes model has been trained successfully\",\"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe03c0c5",
   "metadata": {},
   "source": [
    "- Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f1df90cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression best accuracy (Training): 80.56%\n",
      "Logistic Regression best accuracy (Testing): 96.43%\n",
      "\u001b[7m\u001b[32mThe Logistic Regression model has been trained successfully\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression Parameters\n",
    "'''\n",
    "sklearn.linear_model.LogisticRegression(penalty='l2', *, \n",
    "                                        dual=False, tol=0.0001, \n",
    "                                        C=1.0, fit_intercept=True, \n",
    "                                        intercept_scaling=1, class_weight=None, \n",
    "                                        random_state=None, solver='lbfgs', \n",
    "                                        max_iter=100, multi_class='auto', \n",
    "                                        verbose=0, warm_start=False, \n",
    "                                        n_jobs=None, l1_ratio=None)\n",
    "'''\n",
    "\n",
    "# tol (\"مقدار السماحية المسموح بها في الخطأ\")\n",
    "# C (\"مقلوب قيمة التنعيم مثل الموجود في نظام الدعم الآلي,كلما قلت كلما زاد التنعيم\")\n",
    "# solver (\"معادلة التصنيف\")\n",
    "# max_iter (\"العدد الاقصي للمحاولات\")\n",
    "# random_state (\"الهيكلية المسخدمة في عشوائية البيانات\")\n",
    "# n_jobs (\"لتحديد مدي سرعة العملية,لو نان او 1 ستكون عادية,وكلما زادت زادت السرعة وقيمة سالب واحد الاقصي\")\n",
    "\n",
    "'''\n",
    "solver:\n",
    "liblinear : small data\n",
    "sag       : big data\n",
    "sage      : big data\n",
    "\n",
    "'''\n",
    "\n",
    "# Initialize lists to store training and testing accuracies\n",
    "scoreListLR_Train = []\n",
    "scoreListLR_Test = []\n",
    "\n",
    "# Iterate over different values of random_state\n",
    "for i in range(1, 150):\n",
    "    # Iterate over different values of max_iter\n",
    "    for j in range(1, 10):\n",
    "        # Create a Logistic Regression Model with the different values of random_state and max_iter\n",
    "        Model4= LogisticRegression(random_state=i, solver='saga', max_iter=j)\n",
    "\n",
    "        # Fit the model on the training data\n",
    "        Model4.fit(X_train,Y_train)\n",
    "\n",
    "        # Calculate and store the training accuracy\n",
    "        scoreListLR_Train.append(Model4.score(X_train, Y_train))\n",
    "\n",
    "        # Calculate and store the testing accuracy\n",
    "        scoreListLR_Test.append(Model4.score(X_test, Y_test))\n",
    "\n",
    "# Find the maximum accuracy for both training and testing\n",
    "LR_Accuracy_Train = max(scoreListLR_Train) \n",
    "LR_Accuracy_Test = max(scoreListLR_Test)\n",
    "\n",
    "# Print the best accuracies achieved\n",
    "print(f\"Logistic Regression best accuracy (Training): {LR_Accuracy_Train*100:.2f}%\")\n",
    "print(f\"Logistic Regression best accuracy (Testing): {LR_Accuracy_Test*100:.2f}%\")\n",
    "\n",
    "# Print a success message indicating that the model has been trained successfully\n",
    "print(colored(\"The Logistic Regression model has been trained successfully\",\"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5552b3dd",
   "metadata": {},
   "source": [
    "- Ridge Classifier CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "84fd21a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Classifier CV best accuracy (Training): 79.63%\n",
      "Ridge Classifier CV best accuracy (Testing): 92.86%\n",
      "\u001b[7m\u001b[32mThe Ridge Classifier CV model has been trained successfully\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Ridge Classifier CV Parameters\n",
    "'''\n",
    "sklearn.linear_model.RidgeClassifierCV(alphas=(0.1, 1.0, 10.0), *, \n",
    "                                       fit_intercept=True, scoring=None, \n",
    "                                       cv=None, class_weight=None, \n",
    "                                       store_cv_values=False)\n",
    "'''\n",
    "# alphas (Array of alpha values to try. Regularization strength)\n",
    "# cv (Determines the cross-validation splitting strategy.)\n",
    "\n",
    "# Create a Ridge Classifier Model\n",
    "Model5= RidgeClassifierCV()\n",
    "\n",
    "# Fit the model on the training data\n",
    "Model5.fit(X_train,Y_train)\n",
    "\n",
    "# Calculate and store the training accuracy\n",
    "RCCV_Accuracy_Train = Model5.score(X_train, Y_train)\n",
    "\n",
    "# Calculate and store the testing accuracy\n",
    "RCCV_Accuracy_Test = Model5.score(X_test, Y_test)\n",
    "\n",
    "# Print the best accuracies achieved\n",
    "print(f\"Ridge Classifier CV best accuracy (Training): {RCCV_Accuracy_Train*100:.2f}%\")\n",
    "print(f\"Ridge Classifier CV best accuracy (Testing): {RCCV_Accuracy_Test*100:.2f}%\")\n",
    "\n",
    "# Print a success message indicating that the model has been trained successfully\n",
    "print(colored(\"The Ridge Classifier CV model has been trained successfully\",\"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca133d10",
   "metadata": {},
   "source": [
    "- K-Nearest Neighbors (KNN) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c6062125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e9se6YsWKWrhwodLT0zVgwAC9//77Cg4O1pw5czKViuDgYBUoUEDNmzeXp6fnfT2+L7zwglxcXNSwYUPly5fPPl64cGHNnTtXPj4+Gjp0qHr06KH9+/dr6tSpqlWr1n3t+69q1Kih7t27a9++ffZF49nNVKpUKU2cOFHnz59Xr169NHz4cEnSwoULZbFY7vi92aNHD7388suaP3++evXqpfPnz2vMmDGZ5gQGBmrRokWyWCwaPHiw+vXrp/j4eE2fPj3b5Q/IDovtYVa7ATCFLVu26LHHHsv0FPHLly+rbt26Gjx4sP3p7pD27Nmjl19+WcuWLVPlypWdHQfAfeAyGYB72r9/v6ZMmaKBAweqUqVKunTpkubPny8fH5+7nv0xk9jYWMXGxmrFihWqVasWRQh4hFCGANxTxtqQzz//XGfPnlXevHkVGhqqDz/8kKc8/59Lly5pwYIFKlu27D2fvg7AWLhMBgAATI0F1AAAwNQoQwAAwNQoQwAAwNQoQwAAwNR4Ntl9stlsSk/P+bXmLi4Wh+z3YRkxlxEzScbMZcRMErmyw4iZJGPmMmImyZi5jJhJclwuF5fbv0r+31GG7lN6uk0JCVdzdJ9ubi4qUMBLSUkpunkzd99r6m6MmMuImSRj5jJiJolc2WHETJIxcxkxk2TMXEbMJDk2V8GCXnJ1vXcZ4jIZAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNcoQAAAwNUOVoRkzZigsLOyucy5duqRBgwapZs2aqlmzpoYNG6aUlJTbzk1LS1PLli0VGRnpiLgAAOAfwDBlKDo6WlOmTLnnvH79+un06dP2+T/99JNGjhx527kfffSRDh8+nNNRAQDAP4ibswOcP39eQ4cO1fbt2+Xv73/XuTt37tS2bdsUExOjMmXKSJLef/99devWTQMHDlSRIkXsczdt2qQ1a9aoXLlyDs0PAAAebU4/M7R//37ly5dPK1euVFBQ0F3nxsXFyc/Pz16EJCk0NFQWi0Xbt2+3jyUkJOjtt9/WqFGjVKBAAYdlBwAAjz6nnxlq3LixGjdufF9zz58/r2LFimUac3d3V/78+XX27Fn72NChQ9WoUSM1btxYCxYsyLGsbm452x1dXV0y/WsURsxlxEySMXMZMZNEruwwYibJmLmMmEkyZi4jZpKMkcvpZSg7rl27Jnd39yzjHh4eSk1NlSQtXrxYR48e1cSJE3P0vl1cLCpQwCtH95nB1zePQ/b7sIyYy4iZJGPmMmImiVzZYcRMkjFzGTGTZMxcRswkOTfXI1WGPD09lZaWlmU8NTVVefPm1bFjxzR+/HjNmzdPefPmzdH7Tk+3KSnp9s9ae1Curi7y9c2jpKRrslrTc3TfD8OIuYyYSTJmLiNmksiVHUbMJBkzlxEzScbMZcRMkmNz+frmua8zTo9UGSpatKi+++67TGNpaWlKTExUkSJFFBMTo6tXr+q1116z3379+nXt2LFDa9eu1erVq/X4448/8P3fvOmYbx6rNd1h+34YRsxlxEySMXMZMZNEruwwYibJmLmMmEkyZi4jZpKcm+uRKkM1a9bUhAkTdPLkSZUqVUqSFBsbK0kKCQlR7dq11bJly0zbvPnmmypatKjefPNNFS5cONczAwAAYzN0GbJarUpISJCPj488PT0VFBSkkJAQDRgwQCNGjFBKSoree+89tWnTxv60+vz582fah6enp7y8vOzlCQAA4K+MtaT8b86ePat69eopJiZGkmSxWDRt2jSVKFFC4eHh6t+/v+rXr68RI0Y4NygAAHhkGerM0AcffJDp8xIlSujQoUOZxgoVKnRfr1SdYeHChTmSDQAA/DMZ+swQAACAo1GGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqVGGAACAqRmqDM2YMUNhYWF3nXPp0iUNGjRINWvWVM2aNTVs2DClpKTYb09PT9fcuXPVrFkzBQcHq3nz5vryyy8dHR0AADyiDFOGoqOjNWXKlHvO69evn06fPm2f/9NPP2nkyJH222fNmqXZs2erf//+WrlypcLDwzVy5Eh99dVXjowPAAAeUW7ODnD+/HkNHTpU27dvl7+//13n7ty5U9u2bVNMTIzKlCkjSXr//ffVrVs3DRw4UEWKFNHixYvVtWtXPf/885KkkiVLavfu3Vq6dKlefPFFhx8PAAB4tDj9zND+/fuVL18+rVy5UkFBQXedGxcXJz8/P3sRkqTQ0FBZLBZt375d6enp+uCDD9SmTZss216+fDmnowMAgH8Ap58Zaty4sRo3bnxfc8+fP69ixYplGnN3d1f+/Pl19uxZubi4qHbt2plu//3337V69Wp16NDhobO6ueVsd3R1dcn0r1EYMZcRM0nGzGXETBK5ssOImSRj5jJiJsmYuYyYSTJGLqeXoey4du2a3N3ds4x7eHgoNTU1y3h8fLwiIiJUqFAh9erV66Hu28XFogIFvB5qH3fi65vHIft9WEbMZcRMkjFzGTGTRK7sMGImyZi5jJhJMmYuI2aSnJvrkSpDnp6eSktLyzKempqqvHnzZho7duyYIiIidOPGDS1cuFD58uV7qPtOT7cpKSnl3hOzwdXVRb6+eZSUdE1Wa3qO7vthGDGXETNJxsxlxEwSubLDiJkkY+YyYibJmLmMmElybC5f3zz3dcbpkSpDRYsW1XfffZdpLC0tTYmJiSpSpIh9bPv27erVq5f8/Py0cOHCLJfWHtTNm4755rFa0x2274dhxFxGzCQZM5cRM0nkyg4jZpKMmcuImSRj5jJiJsm5uYx14fAeatasqXPnzunkyZP2sdjYWElSSEiIJGnPnj3q1q2bypUrp//+9785VoQAAMA/k6HLkNVqVXx8vK5fvy5JCgoKUkhIiAYMGKA9e/Zo69ateu+999SmTRsVKVJEN2/e1JtvvqlChQrpgw8+UFpamuLj4xUfH6+EhAQnHw0AADAiQ18mO3v2rJo0aaJx48apbdu2slgsmjZtmkaOHKnw8HB5eHjoueee09tvvy3p1lmhjLNGTZs2zbSv4sWLa/369bl+DAAAwNgMVYY++OCDTJ+XKFFChw4dyjRWqFChO75SdUhISJb5AAAAd2Poy2QAAACORhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmRhkCAACmlu0ydObMGUfkAAAAcIpsl6EmTZrotdde0zfffKPU1FRHZAIAAMg12S5DEyZMkJubmyIjI1W3bl0NHz5cu3btckA0AAAAx3PL7gbNmzdX8+bNFR8frxUrVujrr7/WF198oSeffFJt27ZV69atVaRIEUdkBQAAyHEPvIDaz89P3bt316pVq/TVV1+pcOHCmjRpkho3bqxevXpp+/btOZkTAADAIR7q2WRxcXEaNmyYunTpori4ONWtW1fvvPOObt68qU6dOmnBggU5lRMAAMAhsn2Z7OTJk/r666+1cuVK/fHHHypevLg6d+6sdu3aqWjRopKkjh076s0339TMmTP12muv5XhoAACAnJLtMtSsWTN5eHioadOmGjVqlGrXrn3beaVLl9aJEyceNh8AAIBDZbsMDRs2TK1atZKPj89d5/Xu3Vu9e/d+4GAAAAC5Idtrhjp27KgffvhBQ4cOtY/FxcXpxRdf1Lp163I0HAAAgKNluwwtX75cgwcP1rVr1+xjhQoVUokSJfTGG29QiAAAwCMl22Vo/vz56tatmz7++GP7mL+/v6ZOnarXXntNM2bMyNGAAAAAjpTtMnT69GnVq1fvtrfVq1dPx48ff+hQAAAAuSXbZahw4cLas2fPbW87cOCAChQo8NChAAAAcku2n03Wpk0bzZw5U15eXmratKkKFiyohIQEfffdd5o2bZo6d+7siJwAAAAOke0y1KNHDx09elSjRo3S6NGj7eM2m03PPfec+vbtm6MBAQAAHCnbZcjNzU0ff/yxevXqpbi4OF2+fFk+Pj6qXr26AgMDHZERAADAYbJdhjKUK1dO5cqVyzJ+5cqVe74gIwAAgFFkuwylpaUpOjpa27Zt040bN2Sz2STdukyWkpKiI0eOaPfu3TkeFAAAwBGyXYY++ugjffbZZypfvrwSEhLk4eGhggUL6vDhw7px44b+/e9/OyInAACAQ2T7qfXffvutunTpopUrVyosLEyVK1fWl19+qW+//VbFixdXenq6I3ICAAA4RLbLUEJCgho0aCBJCggI0N69eyVJRYoUUUREhGJiYnI2IQAAgANluwz5+PgoLS1NkvTkk0/q7NmzSk5OzvQ5AADAoyLbZahGjRpauHChUlJSVKJECeXJk8f+5qw7d+6Ut7d3jocEAABwlGyXoT59+mjXrl3q0aOH3Nzc9Oqrr2r48OFq27atJk+erGbNmjkiJwAAgENk+9lkgYGBWrNmjQ4fPixJGjRokLy9vbVjxw41btxYEREROR4SAADAUbJdhkaMGKHWrVurbt26kiSLxaKePXvmeDAAAIDckO3LZN98842uX7/uiCwAAAC5LttlqEqVKtq4caMjsgAAAOS6bF8mCwgI0MKFC7V27VqVLVtWhQoVynS7xWLR2LFjHyjMjBkztGXLFi1cuPCOcy5duqTRo0fbC9lzzz2nt99+W3nz5rXPWbNmjaZOnarTp0/rySef1FtvvaX69es/UCYAAPDPlu0ytG7dOhUuXFiSdOTIER05ciTT7RaL5YGCREdHa8qUKapZs+Zd5/Xr10+pqamKjo5WUlKShg4dqpEjR+rDDz+UJG3dulVvvfWWIiMjVbt2bS1dulR9+vTRihUrVKZMmQfKBgAA/rmyXYbWr1+fowHOnz+voUOHavv27fL397/r3J07d2rbtm2KiYmxF5v3339f3bp108CBA1WkSBHNmTNHzzzzjDp16iRJGjJkiHbu3KlPP/1U77//fo5mf1g2m022G6my3UiT7aZx3sbEZnOR7YaboXIZMZNkzFxGzCSRKzuMmEkyZi4jZpKMmcuImaT/y2XLe++JDpTtMpTT9u/fr3z58mnlypWaPn26/vjjjzvOjYuLk5+fX6YzPKGhobJYLNq+fbuee+457dixQ5GRkZm2e+qpp+wvDPkw3NyyvcTqjmw2m6wxMxV/4WSO7TOn3JQU7+wQf2PETJIxcxkxk0Su7DBiJsmYuYyYSTJmLiNmkm7lSny8tFye75mjv2ezI9tlqHPnzvec85///Oe+99e4cWM1btz4vuaeP39exYoVyzTm7u6u/Pnz6+zZs0pKSlJKSoqKFi2aaU7hwoUf+m1CXFwsKlDA66H28Vc2m02Jbq66kWN7BADg0eXrm+eBl9o8rGyXIZvNlmUsJSVFR48eVd68efXss8/mSLDbuXbtmtzd3bOMe3h4KDU11f6U/7/Pybj9YaSn25SUlPJQ+/g7l+d7yi+vq5KSrstqNc4pS1dXF/n6ehoqlxEzScbMZcRMErmyw4iZJGPmMmImyZi5jJhJ+r9cBX115UrO5/L1zSNX13ufbcp2GbrTM70uX76sHj16qHTp0tnd5X3z9PS0v0nsX6Wmpipv3rzy8PCQpCxzUlNTlSdPnoe+/5s5fI3Vzc1Flsc8lO5yU9Z043xjWlyMl8uImSRj5jJiJolc2WHETJIxcxkxk2TMXEbMJP1fLotFVmt6jv+evV85dnEuX7586t69u6Kjo3Nql1kULVpUFy5cyDSWlpamxMREFSlSRPnz51fevHmzzLlw4UKWS2cAAABSDpYh6dYltIsXL+bkLjOpWbOmzp07p5Mn//+i49jYWElSSEiILBaLQkJCtG3btkzbxcbGqnr16g7LBQAAHl3Zvkz2yy+/ZBmzWq06d+6cpk2bpkqVKuVIsIz9JiQkyMfHR56engoKClJISIgGDBigESNGKCUlRe+9957atGmjIkWKSJJee+01RUREqGLFiqpfv76WLVumX3/9VWPGjMmxXAAA4J8j22UoLCxMFotFNpvNvuo7Y1F1sWLF9M477+RYuLNnz6pJkyYaN26c2rZtK4vFomnTpmnkyJEKDw+Xh4eH/RWoM9SrV09jx47VjBkzNGnSJJUtW1ZRUVG84CIAALgti+12Tw+7i79fgpJuveq0t7e3AgIC5OLinNcIcDSrNV0JCVdzdJ9ubi4qUMBLly5dddqisdsxYi4jZpKMmcuImSRyZYcRM0nGzGXETJIxcxkxk+TYXAULejnm2WShoaGyWq06dOiQKlasKOnWAuW9e/eqbNmy/9gyBAAA/pmy3VzOnTunVq1aqV+/fvaxgwcPqk+fPnr11VeVkJCQowEBAAAcKdtl6KOPPpLVatWkSZPsY/Xr19fXX3+tq1evauLEiTkaEAAAwJGyXYa2bNmiN998U1WqVMk0HhAQoH79+mnDhg05Fg4AAMDRsl2Gbty4ccf3DvHw8NDVqzm7yBgAAMCRsl2GgoODFR0drRs3Mr/F6I0bN/Tpp5+qatWqORYOAADA0bL9bLL+/fvr1VdfVZMmTVS/fn0VKlRICQkJ2rRpky5dunTH9y4DAAAwomyXocqVK+uLL77QjBkz9OOPPyoxMVE+Pj6qUaOGevfurQoVKjgiJwAAgENkuwxJUmBgoCZOnKjHHntMkpSSkqK0tDTlz58/J7MBAAA4XLbXDKWlpendd9/Vv/71L/vYrl27VK9ePY0ZM0ZWqzVHAwIAADhStsvQlClTFBMTozZt2tjHKlWqpCFDhuirr77SnDlzcjIfAACAQ2X7Mtnq1as1ZMgQtW/f3j6WL18+hYWFycXFRdHR0erZs2eOhgQAAHCUbJ8ZunTpkkqUKHHb2/z9/XX+/PmHDgUAAJBbsl2GypQpo7Vr1972tnXr1qlUqVIPHQoAACC3ZPsyWdeuXTVo0CAlJiaqadOm9tcZ+u677/Ttt99q3LhxjsgJAADgENkuQ82bN9eVK1c0bdo0ffvtt/bxAgUKaPjw4WrRokWOBgQAAHCkB3qdoQ4dOqh9+/Y6fvy4EhMT5evrKw8PD3355Zdq2LChNm/enNM5AQAAHOKBypAkWSwW+fv764cfflBUVJR++uknWa1WlS5dOifzAQAAONQDlaELFy7oyy+/1NKlS3Xu3Dn5+vqqffv2atOmDW/UCgAAHinZKkM//fSTFi9erB9++EE2m01PPfWUzp07p2nTpqlmzZqOyggAAOAw91WG5s6dqy+++EKnTp2Sv7+/+vXrpxdffFEeHh4KDQ11dEYAAACHua8yNGHCBAUEBGjhwoWZzgBduXLFYcEAAAByw3296GKrVq106tQpdevWTT169NCaNWuUlpbm6GwAAAAOd19nhj766CNdvXpVq1at0vLlyzVgwADly5dPTZo0kcVikcVicXROAAAAh7jvt+Pw8vJS+/bttWTJEq1evVpt27bVxo0bZbPZNGTIEE2aNEmHDx92ZFYAAIAcl+33JpNuvT/ZkCFDtGHDBk2bNk3lypXTvHnz1Lp1a7Vq1SqnMwIAADjMA7/ooiS5urqqadOmatq0qS5evKjly5drxYoVORQNAADA8R7ozNDtFCpUSN27d9fq1atzapcAAAAOl2NlCAAA4FFEGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKZGGQIAAKbm9DKUnp6uKVOm6Omnn1ZQUJC6du2qkydP3nH+6dOn1bNnT4WGhqpu3boaPXq0rl27lmnON998o+bNmysoKEgvvPCCli1b5ujDAAAAjyinl6EZM2Zo8eLFGj16tJYsWSKLxaLu3bsrLS0ty9wrV67olVde0eXLlzV37lxFRUVp37596tOnj33Oli1bFBkZqbCwMK1atUodO3bUu+++qx9++CE3DwsAADwinFqG0tLSNH/+fPXt21cNGjRQYGCgJk2apPPnz2vdunVZ5n/11VdKTk7W9OnTVbVqVVWpUkWTJk3Szz//rLi4OEnS+vXrFRAQoA4dOuiJJ55Qx44dFRgYqM2bN+f24QEAgEeAU8vQwYMHdfXqVdWqVcs+5uvrq4oVK+qXX37JMv/48eMqXbq0ChYsaB8rVqyYChQooG3btkmS8ufPryNHjmjr1q2y2WyKjY3V0aNHFRQU5PgDAgAAjxw3Z975uXPnJN0qNH9VuHBhnT17Nst8Pz8/xcfHy2q1ytXVVZKUnJysy5cv6+LFi5Kkzp07a+/evQoPD5erq6usVqu6d++uVq1aPXReN7ec7Y6uri6Z/jUKI+YyYibJmLmMmEkiV3YYMZNkzFxGzCQZM5cRM0nGyOXUMpSx8Nnd3T3TuIeHhy5fvpxlfvPmzRUVFaWxY8dq4MCBslqtGjlypCwWi32N0dmzZ5WYmKjhw4crJCREW7du1aRJk1S6dGm1bdv2gbO6uFhUoIDXA29/N76+eRyy34dlxFxGzCQZM5cRM0nkyg4jZpKMmcuImSRj5jJiJsm5uZxahjw9PSXdWjuU8d+SlJqaqjx5sj4opUqV0tSpUzV8+HAtWrRInp6eCgsLU+XKleXt7S1J6tevn1q2bKmOHTtKkipUqKDLly/rww8/VJs2beTi8mDNMz3dpqSklAfa9k5cXV3k65tHSUnXZLWm5+i+H4YRcxkxk2TMXEbMJJErO4yYSTJmLiNmkoyZy4iZJMfm8vXNc19nnJxahjIuj124cEElS5a0j1+4cEGBgYG33aZBgwbasGGD4uPj5ePjI09PT9WpU0dt27ZVQkKCjh8/ripVqmTaJjg4WDNnzlRiYmKm9UbZdfOmY755rNZ0h+37YRgxlxEzScbMZcRMErmyw4iZJGPmMmImyZi5jJhJcm4up144DAwMlLe3t2JjY+1jSUlJOnDggGrUqJFl/vbt29WpUyelpaXJz89Pnp6e2rZtmy5duqQ6deoof/78ypMnjw4dOpRpu8OHD8vX1/ehihAAAPhncuqZIXd3d3Xq1EkTJkxQwYIFVbx4cY0fP15FixbVM888I6vVqoSEBPsZoDJlyui3337T2LFj9frrr+v06dMaPHiw/Wn0khQeHq6ZM2fKz89P1atX1/bt2xUVFaXevXs781ABAIBBObUMSbfW+Ny8eVPvvvuurl+/rpo1a2revHlyd3fX77//riZNmmjcuHFq27at8ufPr9mzZ2vcuHFq2bKlChQooA4dOqhXr16Z9pc/f37NmjVLZ8+eVYkSJfTWW2+pQ4cOTjxKAABgVBabzWZzdohHgdWaroSEqzm6Tzc3FxUo4KVLl64a6vqtEXMZMZNkzFxGzCSRKzuMmEkyZi4jZpKMmcuImSTH5ipY0Ou+FlAb68UGAAAAchllCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmBplCAAAmJrTy1B6erqmTJmip59+WkFBQeratatOnjx5x/mnT59Wz549FRoaqrp162r06NG6du1apjl79uxRx44dVbVqVTVo0EBTpkxRenq6ow8FAAA8gpxehmbMmKHFixdr9OjRWrJkiSwWi7p37660tLQsc69cuaJXXnlFly9f1ty5cxUVFaV9+/apT58+9jnHjx9X586dVbJkSX399deKjIzUggULNG/evNw8LAAA8Ihwc+adp6Wlaf78+XrrrbfUoEEDSdKkSZP09NNPa926dWrevHmm+V999ZWSk5M1ffp0FSxY0D6/UaNGiouLU40aNTRr1iyVLVtWY8eOlcVikb+/v3777Tft2LEj148PAAAYn1PPDB08eFBXr15VrVq17GO+vr6qWLGifvnllyzzjx8/rtKlS9uLkCQVK1ZMBQoU0LZt2yRJmzZtUosWLWSxWOxz+vXrp5kzZzrwSAAAwKPKqWeGzp07J+lWofmrwoUL6+zZs1nm+/n5KT4+XlarVa6urpKk5ORkXb58WRcvXlRycrL+/PNP+fj46J133tHGjRvl6+urNm3a6PXXX7dv86Dc3HK2O7q6umT61yiMmMuImSRj5jJiJolc2WHETJIxcxkxk2TMXEbMJBkjl1PLUMbCZ3d390zjHh4eunz5cpb5zZs3V1RUlMaOHauBAwfKarVq5MiRslgsSktLU3JysiTpww8/VOfOnTVnzhz9+uuvGjNmjK5du6Y33njjgbO6uFhUoIDXA29/N76+eRyy34dlxFxGzCQZM5cRM0nkyg4jZpKMmcuImSRj5jJiJsm5uZxahjw9PSXdWjuU8d+SlJqaqjx5sj4opUqV0tSpUzV8+HAtWrRInp6eCgsLU+XKleXt7a3HHntMklSnTh39+9//liRVqFBBCQkJmj59uvr165fp8ll2pKfblJSU8kDb3omrq4t8ffMoKemarFbjPNvNiLmMmEkyZi4jZpLIlR1GzCQZM5cRM0nGzGXETJJjc/n65rmvM05OLUMZl8cuXLigkiVL2scvXLigwMDA227ToEEDbdiwQfHx8fLx8ZGnp6fq1Kmjtm3bKn/+/PLw8FD58uUzbVOuXDmlpKQoISFBhQoVeuC8N2865pvHak132L4fhhFzGTGTZMxcRswkkSs7jJhJMmYuI2aSjJnLiJkk5+Zy6oXDwMBAeXt7KzY21j6WlJSkAwcOqEaNGlnmb9++XZ06dVJaWpr8/Pzk6empbdu26dKlS6pTp45cXV0VEhKi3bt3Z9ru0KFD8vX1Vf78+R19SAAA4BHj1DLk7u6uTp06acKECfr+++918OBBDRgwQEWLFtUzzzwjq9Wq+Ph4Xb9+XZJUpkwZ/fbbbxo7dqxOnz6tn3/+WQMHDlSHDh30xBNPSJJ69eqlTZs2aerUqTp16pTWrFmj2bNnKzw8/KEXUAMAgH8ep14mk2497f3mzZt69913df36ddWsWVPz5s2Tu7u7fv/9dzVp0kTjxo2zXwabPXu2xo0bp5YtW6pAgQLq0KGDevXqZd/fU089pVmzZmnSpEmaNWuW/Pz8FBERoW7dujnxKAEAgFFZbDabzdkhHgVWa7oSEq7m6D7d3FxUoICXLl26aqjrt0bMZcRMkjFzGTGTRK7sMGImyZi5jJhJMmYuI2aSHJurYEGv+1pAbawXGwAAAMhllCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqlCEAAGBqFpvNZnN2iEeBzWZTenrOP1Suri6yWtNzfL8Py4i5jJhJMmYuI2aSyJUdRswkGTOXETNJxsxlxEyS43K5uFhksVjuOY8yBAAATI3LZAAAwNQoQwAAwNQoQwAAwNQoQwAAwNQoQwAAwNQoQwAAwNQoQwAAwNQoQwAAwNQoQwAAwNQoQwAAwNQoQwAAwNQoQwAAwNQoQwAAwNQoQ05w8eJFvfXWW6pVq5aqVaumiIgIHTlyxNmxMjl+/LiqVaum5cuXOzuK/vjjDwUEBGT5+PLLL52aa8WKFXrhhRdUpUoVNW/eXGvWrHFqntjY2Ns+TgEBAWrSpInTct24cUOTJk1Sw4YNVa1aNb366qvasWOH0/JkuHr1qkaNGqUGDRqoevXq6t27t06dOuW0PDNmzFBYWFimsV9//VWdOnVScHCwGjZsqHnz5hkil3TrZ0RwcLB+//13Q2Rav3692rVrp2rVqqlx48b68MMPdf36dafnWr16tVq2bKmqVauqadOmmj17tmw2m9Nz/dW7776rxo0b52Ki22d6++23s/zsql+/fu4EsiHXvfzyy7b27dvb9uzZYzty5Iitb9++trp169pSUlKcHc1ms9lsaWlptrZt29rKly9vW7ZsmbPj2L7//ntblSpVbOfPn7dduHDB/nHt2jWnZVqxYoWtQoUKtujoaNuJEyds06ZNswUGBtp27NjhtEypqamZHp8LFy7YNm/ebKtYsaLtiy++cFquyZMn2+rWrWvbtGmT7cSJE7ahQ4faQkJCbOfOnXNaJpvNZuvWrZvt6aeftq1fv9525MgR27vvvmurU6eOLSEhIdezLFiwwBYQEGDr1KmTfSwhIcH21FNP2YYOHWo7cuSIbenSpbYqVarYli5d6tRcNpvNdvDgQVuDBg1s5cuXt50+fTrX8twp0y+//GKrUKGCbdasWbYTJ07YNmzYYGvQoIEtMjLSqbl+/PFHW8WKFW2fffaZ7dSpU7a1a9fagoODbQsWLHBqrr9at26drXz58rZGjRo5PdOLL75o+/jjjzP9DLt48WKuZOLMUC67dOmSSpQooVGjRqlKlSoqU6aMevfurfj4eP3222/OjidJmjp1qry8vJwdw+7w4cPy9/dX4cKF5efnZ//w9PR0Sh6bzabJkycrPDxc4eHhKlWqlPr06aM6depo27ZtTskkSe7u7pken/z582vcuHF69tln9fLLLzst1/fff68WLVqoXr16KlWqlCIjI5WcnKxdu3Y5LdPBgwe1ceNGjRo1So0aNVKZMmU0cuRIeXt767///W+u5Th//ry6deumyZMny9/fP9NtX3zxhdzd3TVixAiVKVNG7dq1U5cuXTRnzhyn5po5c6b+9a9/qWDBgg7Pcb+ZFi9erFq1aikiIkKlSpVS/fr1NWDAAK1cuVJpaWlOyxUfH6/u3burY8eOeuKJJ/Tss8+qTp06+vnnnx2a6V65Mly4cEHDhg1TaGiow/PcK5PVatWRI0dUpUqVTD/Hcuv7jDKUywoUKKCPP/5Y5cqVkyT9+eefmjdvnooWLaqyZcs6OZ30yy+/aMmSJfrwww+dHcXu0KFDhnhsMhw7dkx//PGHWrZsmWl83rx56tGjh5NSZbVo0SKdPXtWb7/9tlNz5M+fXz/88IN+//13Wa1WLVmyRO7u7qpQoYLTMh0/flySVKNGDfuYi4uLAgMD9csvv+Rajv379ytfvnxauXKlgoKCMt0WFxenmjVrys3NzT5Wq1YtHT9+XBcvXnRark2bNmn8+PEaMmSIQzNkJ1PXrl01ePDgLNvcvHlTycnJTsv10ksvqX///pJu/bLfuHGjtm3bprp16zo0071ySbf+qIuMjFTr1q1zrQzdLdOJEyeUmpqqMmXK5EqWv3O79xQ4yrBhw+x//c2cOVN58+Z1ap6kpCQNHjxY7777rooVK+bULH91+PBh+fn56dVXX9WJEydUqlQp9e7dW08//bRT8pw4cUKSlJKSotdff10HDhxQiRIl1KtXr1y/7n4nqampioqKUnh4uAoXLuzULEOHDtWAAQPUpEkTubq6ysXFRZMnT1bJkiWdlsnPz0+SdO7cuUw/fP/44w+lpqbmWo7GjRvf8Xvm3LlzKl++fKaxjK/lmTNnVKhQIafkyjhzFhsb67D7v527ZapYsWKmz9PS0rRgwQJVqlTJ4WcW7pYrw5kzZ9S0aVNZrVbVq1dPr7zyikMz3U+u6OhoxcfHKyoqSrNmzXJ4nntlOnz4sCwWiz799FNt3LhRLi4uatCggfr37y8fHx+HZ+PMkBOFh4dr2bJlatWqlfr06aP9+/c7Nc+IESMUHByc5YyHM6WlpenEiRNKTk5W//79NXv2bFWpUkXdu3fXli1bnJIp4y/NIUOGqEWLFpo/f77q1q2r3r17Oy3T33399ddKTU2966LJ3HL06FH5+vpq+vTpWrJkidq2bashQ4bo4MGDTssUFBSkMmXK6L333tPZs2eVlpam6Oho/frrrw6/rHK/rl+/Lnd390xjHh4ekpSrhe1Rc/PmTQ0ePFhHjhzRe++95+w4kiRfX18tXbpUkydP1qFDh257Fis3HTx4UNOmTdP48eOzfI85y2+//SYXFxcVL15cUVFRGjJkiDZs2KDevXsrPT3d4ffPmSEnyrj0M2rUKO3atUufffaZxo0b55QsK1asUFxcnL755hun3P+duLu765dffpGbm5v9f9rKlSvr6NGjmjdvnmrXrp3rmR577DFJ0uuvv64XX3xRklShQgUdOHBACxYscEqmv1uxYoWeffZZFShQwKk5/vjjD7311luKjo62X5KqUqWKjhw5oqlTp2r69OlOyfXYY49p+vTpioyMVMOGDeXm5qaGDRvqpZde0r59+5yS6e88PT2zFLOMEuTss8hGlfFHU2xsrKZMmXLby0PO4O3trYoVK6pixYpKT0/XgAED9NZbb6l48eK5niU1NVVvvvmmevXqpcDAwFy//zvp27evunTpIl9fX0lS+fLl5efnp/bt22vv3r0O/1pyZiiXXbx4UatWrZLVarWPubi4qEyZMrpw4YLTci1btkwXL160P/25WrVqkqT33ntPzZs3d1ou6dYP/r//9VK+fHmdP3/eKXmKFi1qz/BXZcuWdcrTjP8uISFBO3fu1AsvvODsKNqzZ49u3LihKlWqZBoPCgqyX250Fn9/fy1ZskTbtm3Tli1bNH36dCUmJurJJ590aq4MRYsWzfIzIePzIkWKOCOSoV24cEEdO3bUzp07NWfOHENcso6Li9PevXszjWWsF3XWz/vdu3frt99+07Rp0+w/62fNmqUzZ86oWrVqWrlypVNyWSwWexHKkPEz9ty5cw6/f84M5bILFy5o0KBBKlSokP0Mwo0bN3TgwAGn/s87YcKELK/J8eyzz6pfv35O/aV68OBBvfLKK5ozZ06mxa779u1z2qLqihUrysvLS7t3786U6fDhw05dB5Nhx44dslgsubYo8m4y1p4dOnRIVatWtY8fPnxYpUqVclYsJScnq2fPnoqMjFTlypUlSVeuXNHPP/+soUOHOi3XX9WsWVOLFy+W1WqVq6urJGnLli3y9/d36HqhR9Hly5cVHh6u5ORk/fe//1VAQICzI0mS5s+fr8TExEzPUNy9e7fc3NycVrqrVq2qb7/9NtPYwoUL9e2332rhwoVO+94aNGiQEhMTM72WVkaRzI2f9ZwZymWBgYGqV6+eRo4cqbi4OB0+fFhDhgxRUlKSunTp4rRcRYoUUalSpTJ9SFKhQoWccio3Q/ny5VWuXDn743X06FGNGzdOu3btUs+ePZ2SydPTU926ddP06dO1atUqnTp1SjNnztRPP/2k1157zSmZ/urgwYN64oknlCdPHmdHUdWqVVWjRg0NGTJEW7du1YkTJ/TJJ59oy5YtioiIcFoub29vWSwWjR07VocOHdLBgwfVs2dPPf7442rRooXTcv1Vu3btlJycrKFDh+rIkSNavny5Pv30U0M9Y9Eoxo0bp9OnT2v8+PEqWLCg4uPj7R9/PQuf27p27apdu3ZpypQpOnnypGJiYjR+/Hh17tzZaZewPT09s/ysz5cvn9zc3FSqVCl5e3s7JVeLFi30008/aebMmTp16pQ2bNigd955Ry1atMiVZ5hxZiiXWSwWffLJJ5o4caL69++vK1euqEaNGlq0aJEef/xxZ8czHBcXF0VFRWnChAnq37+/kpKSVLFiRS1YsMCpf/317t1befLk0aRJk3T+/HmVKVNGU6dO1VNPPeW0TBn+/PNP5c+f39kxJN36+s2YMUOffPKJ3n77bV2+fFnly5dXdHS0goODnZpt4sSJGj16tDp16iQXFxc1atRIgwcPzvRUdmcqVKiQ5s6dqzFjxujFF1+Un5+fBg8ebF+nhlvS09MVExOjGzduKDw8PMvt33//vUqUKOGEZLdeumHWrFn65JNPNG/ePBUsWFBdu3ZV9+7dnZLHyBo1aqTJkycrKipKUVFR8vHxUcuWLe0vTeBoFpstl18XHAAAwEC4TAYAAEyNMgQAAEyNMgQAAEyNMgQAAEyNMgQAAEyNMgQAAEyNMgQAAEyNMgQAAEyNMgTAkMLCwhQWFpZl/OrVq3r11VdVuXJl/e9//3NohqlTpxrmfa4AOI4xXnceAO7D1atXFRERoT179mjy5Mlq0qSJsyMB+AegDAF4JKSkpCgiIkJ79+7VjBkzVL9+fWdHAvAPwWUyAIaXUYQOHDigWbNm3bMINWvWTH369Mky/vLLLysiIkKSZLVaNXv2bLVo0UJVq1ZVcHCwOnTooC1bttxxv7e7dBcbG6uAgADFxsbax86cOaOBAwcqNDRUQUFBCg8P14EDB7JzyAByEWUIgKH99YzQnDlzVLt27Xtu07p1a23cuFHJycn2sVOnTmnPnj1q3bq1JGnChAmaPn262rdvr7lz5+r999/XpUuX9MYbbyglJeWB8yYkJKhDhw7av3+/hg0bpokTJyo9PV0dO3bU0aNHH3i/AByHMgTAsDKKUFxcnFJTU3X16tX72q5Vq1a6ceOG1q1bZx/75ptv5OXlZV9ndOHCBQ0YMEBhYWEKDQ1Vq1atNGjQIF2+fFmHDh164MyffvqpEhMTNX/+fLVs2VJNmzbVvHnzVKhQIU2ePPmB9wvAcShDAAxr3759+u233/TZZ5+pVKlSioyM1IULF+65XYkSJVS9enWtXr3aPrZ69Wo1a9ZMnp6ekqSJEyeqS5cuSkhI0M6dO7V8+XKtXLlSknTjxo0HzrxlyxZVqFBBRYoU0c2bN3Xz5k25uLiofv36+vnnnx94vwAchwXUAAwrX758+vTTTxUYGKjx48frlVde0eDBgzV//ny5uNz9b7k2bdpoxIgRunTpks6dO6ejR49q+PDh9tv37t2rkSNHau/evfL09FTZsmVVvHhxSZLNZnvgzImJiTp58qQqVap029uvXbumPHnyPPD+AeQ8yhAAwwoICFBgYKAkqWrVqurVq5emTp2qWbNmqVevXnfd9rnnntOoUaO0bt06nTx5UsWKFVNoaKgkKTk5Wd26dVNAQIBWrVqlMmXKyMXFRRs2bNDatWvvul+r1Zrp87+vL/Lx8VFoaKgGDx582+3d3d3vun8AuY/LZAAeGb169VJQUJCmTZumHTt23HWuj4+PGjVqpO+//17/+9//1LJlS/vZpGPHjikxMVGdO3dWuXLl7OMbN26UJKWnp992n97e3jp37lymsb/nCA0N1fHjx+Xv768qVarYP1auXKkvv/xSrq6uD3TsAByHMgTgkeHq6qrx48fL3d1db775pi5fvnzX+W3atNGmTZv0+++/q1WrVvZxf39/eXt7KyoqSj/++KM2b96sYcOG6b///a+kW5eybqdRo0b6448/NGbMGMXGxmrGjBlasWJFpjldunRRenq6unTpopiYGG3ZskXDhg3Tf/7zH5UuXfrhHgAADkEZAvBIyVhI/ccff+jdd9+969ynn35a+fLlU8WKFVWuXDn7uI+Pj2bMmCGbzaY33nhDgwcP1pkzZ/TZZ5/Jy8tLcXFxt91fu3bt1L17d8XExKh79+7asWNHlmeIFSlSRIsXL1bx4sU1YsQI9ezZU3v27NGYMWPUpUuXhz5+ADnPYnuYlYIAAACPOM4MAQAAU6MMAQAAU6MMAQAAU6MMAQAAU6MMAQAAU6MMAQAAU6MMAQAAU6MMAQAAU6MMAQAAU6MMAQAAU6MMAQAAU/t/xAillIVwWLsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN best accuracy (Training): 100.00%\n",
      "KNN best accuracy (Testing): 92.86%\n",
      "\u001b[7m\u001b[32mThe K-Nearest Neighbors (KNN) model has been trained successfully\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# K-Nearest Neighbors (KNN) Parameters\n",
    "'''\n",
    "sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, *, \n",
    "                                       weights='uniform', algorithm='auto', \n",
    "                                       leaf_size=30, p=2, \n",
    "                                       metric='minkowski', \n",
    "                                       metric_params=None, n_jobs=None)\n",
    "'''\n",
    "\n",
    "# n_neighbors (Number of neighbors to use by default for kneighbors queries.)\n",
    "# metric (Metric to use for distance computation.)\n",
    "# n_jobs (The number of parallel jobs to run for neighbors search.)\n",
    "\n",
    "# Initialize lists to store training and testing accuracies\n",
    "scoreListknn_Train = []\n",
    "scoreListknn_Test = []\n",
    "\n",
    "# Iterate over different values of n_neighbors\n",
    "for i in range(3, 16):\n",
    "    # Create a KNN model with the different value of n_neighbors\n",
    "    Model6 = KNeighborsClassifier(n_neighbors=i, weights='distance')\n",
    "    \n",
    "    # Fit the model on the training data\n",
    "    Model6.fit(X_train, Y_train)\n",
    "    \n",
    "    # Calculate and store the training accuracy\n",
    "    scoreListknn_Train.append(Model6.score(X_train, Y_train))\n",
    "    \n",
    "    # Calculate and store the testing accuracy\n",
    "    scoreListknn_Test.append(Model6.score(X_test, Y_test))\n",
    "\n",
    "# Plot the training accuracy for different values of n_neighbors\n",
    "plt.plot(range(3, 16), scoreListknn_Train)\n",
    "plt.xticks(np.arange(3, 16, 1))\n",
    "plt.title(\"Training Accuracy vs K value\")\n",
    "plt.xlabel(\"K value\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()\n",
    "\n",
    "# Plot the testing accuracy for different values of n_neighbors\n",
    "plt.plot(range(3, 16), scoreListknn_Test)\n",
    "plt.xticks(np.arange(3, 16, 1))\n",
    "plt.title(\"Testing Accuracy vs K value\")\n",
    "plt.xlabel(\"K value\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()\n",
    "\n",
    "# Find the maximum accuracy for both training and testing\n",
    "KNN_Accuracy_Train = max(scoreListknn_Train) \n",
    "KNN_Accuracy_Test = max(scoreListknn_Test)\n",
    "\n",
    "# Print the best accuracies achieved\n",
    "print(f\"KNN best accuracy (Training): {KNN_Accuracy_Train*100:.2f}%\")\n",
    "print(f\"KNN best accuracy (Testing): {KNN_Accuracy_Test*100:.2f}%\")\n",
    "\n",
    "# Print a success message indicating that the model has been trained successfully\n",
    "print(colored(\"The K-Nearest Neighbors (KNN) model has been trained successfully\",\"green\", attrs=['reverse']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1ab1262",
   "metadata": {},
   "source": [
    "# <font color=#4863A0> Model Evaluation 📝</font>\n",
    "- Training Score\n",
    "- Testing Score.\n",
    "- Choosing the better Model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07a2557b",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">1. Training Score</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c0fbd256",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mModel 1-Decision Tree Classifier                   Training Score: 100.0\u001b[0m\n",
      "\u001b[32mModel 2-Random Forest Classifier                   Training Score: 100.0\u001b[0m\n",
      "\u001b[32mModel 3-GaussianNB                                 Training Score: 79.62962962962963\u001b[0m\n",
      "\u001b[32mModel 3-BernoulliNB                                Training Score: 80.55555555555556\u001b[0m\n",
      "\u001b[32mModel 4-Logistic Regression                        Training Score: 80.55555555555556\u001b[0m\n",
      "\u001b[32mModel 5-Ridge Classifier CV                        Training Score: 79.62962962962963\u001b[0m\n",
      "\u001b[32mModel 6-K-Nearest Neighbors (KNN)                  Training Score: 100.0\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Dictionary to store model names and their respective training scores (key:value)\n",
    "model_train_scores = {\n",
    "    \"Model 1-Decision Tree Classifier\": DT_Accuracy_Train,\n",
    "    \"Model 2-Random Forest Classifier\": RF_Accuracy_Train,\n",
    "    \"Model 3-GaussianNB\": GNB_Accuracy_Train,\n",
    "    \"Model 3-BernoulliNB\": BNB_Accuracy_Train,\n",
    "    \"Model 4-Logistic Regression\": LR_Accuracy_Train,\n",
    "    \"Model 5-Ridge Classifier CV\": RCCV_Accuracy_Train,\n",
    "    \"Model 6-K-Nearest Neighbors (KNN)\": KNN_Accuracy_Train\n",
    "}\n",
    "\n",
    "# Loop through each model and print the training score\n",
    "for model_name, accuracy in model_train_scores.items():\n",
    "    print(colored(f\"{model_name:<50} Training Score: {accuracy*100}\", \"green\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd9fe68d",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">2. Testing Score</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4083df15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mModel 1-Decision Tree Classifier                   Testing Score: 96.42857142857143\u001b[0m\n",
      "\u001b[32mModel 2-Random Forest Classifier                   Testing Score: 96.42857142857143\u001b[0m\n",
      "\u001b[32mModel 3-GaussianNB                                 Testing Score: 89.28571428571429\u001b[0m\n",
      "\u001b[32mModel 3-BernoulliNB                                Testing Score: 92.85714285714286\u001b[0m\n",
      "\u001b[32mModel 4-Logistic Regression                        Testing Score: 96.42857142857143\u001b[0m\n",
      "\u001b[32mModel 5-Ridge Classifier CV                        Testing Score: 92.85714285714286\u001b[0m\n",
      "\u001b[32mModel 6-K-Nearest Neighbors (KNN)                  Testing Score: 92.85714285714286\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Dictionary to store model names and their respective testing scores\n",
    "model_test_scores = {\n",
    "    \"Model 1-Decision Tree Classifier\": DT_Accuracy_Test,\n",
    "    \"Model 2-Random Forest Classifier\": RF_Accuracy_Test,\n",
    "    \"Model 3-GaussianNB\": GNB_Accuracy_Test,\n",
    "    \"Model 3-BernoulliNB\": BNB_Accuracy_Test,\n",
    "    \"Model 4-Logistic Regression\": LR_Accuracy_Test,\n",
    "    \"Model 5-Ridge Classifier CV\": RCCV_Accuracy_Test,\n",
    "    \"Model 6-K-Nearest Neighbors (KNN)\": KNN_Accuracy_Test\n",
    "}\n",
    "\n",
    "# Loop through each model and print the testing score\n",
    "for model_name, accuracy in model_test_scores.items():\n",
    "    print(colored(f\"{model_name:<50} Testing Score: {accuracy*100}\", \"green\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0c58c7b",
   "metadata": {},
   "source": [
    "## <font size=\"4\" face=\"WildWest\">3. Choosing the better Model.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853ae680",
   "metadata": {},
   "source": [
    "The best models in accuracy are models 1 & 2 & 4 : **Decision Tree Classifier** and **Random Forest Classifier** and **Logistic Regression** with accuracy **96.43%**. Which we can choose any one of them for our *deployment*. I have chosen **Random Forest Classifier** model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a28f7e5",
   "metadata": {},
   "source": [
    "# <font color=#4863A0> Model Deployment 🛠️</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "994db53b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# File mode explanation:\n",
    "# 'r'  - open for reading (default)\n",
    "# 'w'  - open for writing, truncating the file first\n",
    "# 'x'  - create a new file and open it for writing\n",
    "# 'a'  - open for writing, appending to the end of the file if it exists\n",
    "# 'b'  - binary mode\n",
    "# 't'  - text mode (default)\n",
    "# '+'  - open a disk file for updating (reading and writing)\n",
    "# 'U'  - universal newline mode (deprecated)\n",
    "\n",
    "# Define the filename for the pickle file\n",
    "filename = 'model.pkl'\n",
    "\n",
    "# Save (serialize) the model to the file using pickle\n",
    "# 'wb' mode opens the file in binary format for writing\n",
    "pickle.dump(Model2, open(filename, 'wb'))"
   ]
  }
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