Loan Prediction is a machine learning-based solution designed to predict whether a loan application will be approved based on applicant financial and demographic features. The project integrates data preprocessing, model training, evaluation, and deployment, and demonstrates an end-to-end approach from raw data to a working prediction system.
🔍 Project Overview
This solution tackles a common real-world problem in banking and finance — automating the loan approval process using historical loan application data. The main goal is to build predictive models that can learn from past loan records and accurately identify which applicants are more likely to receive loan approval.
🧠 Key Features
✔ Data Pipeline – Includes data ingestion, cleaning, and feature engineering to prepare inputs for model training. ✔ Multiple ML Models – Implements Random Forest and XGBoost models to explore different machine learning approaches and compare performance. ✔ Model Evaluation – Model accuracy and performance are evaluated on test data to select the best performer. ✔ Web Application (Web_APP) – A user-facing interface that allows users to input applicant details and obtain live loan predictions based on the trained model. ✔ Reusable Scripts and Setup – Includes a setup script and utility code to streamline environment setup and integration with data platforms (e.g., HBase ingestion).
🛠 Tech Stack
Python & Jupyter Notebooks – For data analytics, exploration, and training.
scikit-learn / XGBoost – For building and tuning classification models.
Web Technologies (HTML, CSS, possibly Flask/JS) – For the interactive web application layer.
Docker / Shell scripts – For environment setup and reproducibility.
📈 Outcomes
Models capable of predicting loan approval using historical features with competitive performance.
Demonstrated workflow from data ingestion to real-time prediction through a web interface.
🎯 Project Impact
This project showcases a complete machine learning lifecycle suitable for production readiness in financial applications, helping reduce manual workload and improve consistency in loan decisions