This project is a Machine Learning-based Loan Approval Prediction System that helps financial institutions determine whether a loan application should be approved or rejected based on applicant details. The system uses various machine learning models to predict loan approval status with high accuracy.
The main objective of this project is to develop a machine learning model that can accurately predict loan approval based on applicant details. By leveraging data-driven insights, the system aims to: Reduce manual loan processing time. Minimize loan default risk. Improve decision-making for financial institutions.
The dataset consists of multiple features related to loan applications, including: No. of Dependents Education Level Self-Employment Status Annual Income Loan Amount Requested Loan Term CIBIL Score Asset Values (Residential, Commercial, Luxury, Bank Assets) Loan Status (Target Variable)
Several models were trained and compared to determine the best-performing one: Linear Regression Random Forest Classifier ✅ (Best Performing Model) K-Nearest Neighbors (KNN) Decision Tree Classifier Support Vector Machine (SVM) The best-performing model was saved using joblib for real-time predictions.
To evaluate model performance, the following metrics were used: Accuracy: Measures the overall correctness of the model. Precision: Measures how many of the predicted positive instances were actually positive. Recall: Measures how many of the actual positive instances were correctly identified. F1 Score: Harmonic mean of precision and recall. Confusion Matrix: Provides a detailed breakdown of true/false positives and negatives.
To set up and run the project on your local machine: Clone the repository:git clone https://github.com/your-username/Loan-Approval-Prediction.git cd Loan-Approval-Prediction Install dependencies:pip install -r requirements.txt
Handling categorical variables using Label Encoding. Feature scaling for numerical values. Addressing imbalanced data for better predictions. Optimizing hyperparameters to improve accuracy.
Feature engineering plays a key role in improving model performance. Random Forest is effective for handling complex datasets. Streamlit is a great tool for deploying ML models interactively.