Loan Approval Prediction

This project predicts whether a loan application will be approved or rejected based on applicant details. It uses Machine Learning classification models like Logistic Regression and Decision Tree.

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📌 Project Overview

This project predicts whether a loan application will be Approved (1) or Rejected (0) using machine learning models. We explore and preprocess the dataset, apply classification models, and compare their performance with evaluation metrics.

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📂 Dataset

Source: Kaggle - Loan Approval Prediction Dataset

Number of Rows: 4269
Number of Columns: 13

Features:

• loan_id → Unique ID of the loan application
• no_of_dependents → Number of dependents of applicant
• education → Graduate / Not Graduate
• self_employed → Yes / No
• income_annum → Annual income of the applicant
• loan_amount → Loan amount requested
• loan_term → Loan repayment term in months
• cibil_score → Credit score of the applicant
• residential_assets_value → Value of residential assets
• commercial_assets_value → Value of commercial assets
• luxury_assets_value → Value of luxury assets
• bank_asset_value → Value of bank assets
• loan_status → Target variable (Approved=1, Rejected=0)

Preprocessing Steps:

• Removed duplicates
• Handled missing values
• Dropped loan_id (irrelevant for prediction)
• Encoded categorical variables into numeric form

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⚙️ Models Used

1. Logistic Regression

   • Best for binary classification.
   • Outputs probabilities between 0 and 1.
   • Helps in understanding the relationship between features and target.

2. Decision Tree Classifier

   • Splits data into branches using conditions.
   • Easy to visualize and interpret.
   • Can overfit if not tuned properly.

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📊 Evaluation Metrics

It used the following metrics to evaluate model performance:

• Accuracy → Correct predictions / Total predictions
• Precision → Out of predicted "Approved", how many were actually "Approved"
• Recall (Sensitivity) → Out of actual "Approved", how many were correctly predicted
• F1 Score → Harmonic mean of Precision & Recall
• Confusion Matrix → Table showing True/False Positives and Negatives

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📊 Results

• Logistic Regression

Training Accuracy: 79.5% Testing Accuracy: 79.8% Balanced performance, slightly lower accuracy.

• Decision Tree (max_depth=5)

Training Accuracy: 97.5% Testing Accuracy: 96.8% Higher accuracy, well-controlled overfitting with depth limit.

✅ Decision Tree performed better on this dataset.

🛠️ Technologies Used

• Python 🐍
• Pandas, NumPy → Data handling
• Matplotlib, Seaborn → Visualization
• Scikit-learn → Machine Learning models & metrics

🛠️ Installation

1. Clone the repository (or download the files):

   git clone https://github.com/Adeeba-Shahzadi/LoanApprovalPrediction-BinaryClassificationModel.git
   cd LoanApprovalPrediction-BinaryClassification

2. Install required dependencies:

    pip install -r requirements.txt

📂 Files

• LoanApprovalPrediction.ipynb → Jupyter Notebook with step-by-step implementation.
• loan_approval_dataset.csv → Dataset for training
• loanapprovalprediction.py → Python script version
• requirements.txt → Required libraries.
• README.md → Project documentation.

▶️ How to Run

• Run with Jupyter Notebook:

    jupyter notebook LoanApprovalPrediction.ipynb

• Run with Python:

    python loanapprovalprediction.py