Project Title:Predicting Loan Default Using Machine Learning

Objective:

To build a machine learning model that predicts whether a loan applicant is likely to default on a loan, based on financial and personal data.

 Project Summary:

Loan default prediction is a classification problem where the goal is to identify high-risk borrowers who are likely to fail in repaying their loans. This project involves analyzing historical loan data, which includes borrower details like income, credit score, loan amount, employment status, and repayment history. The machine learning model learns patterns from past defaults and non-defaults to make accurate predictions on new applicants. This helps financial institutions minimize risk and improve decision-making in the loan approval process.

Key Components:

Dataset: Includes features such as credit score, income, employment status, loan amount, loan purpose, and default status

Algorithms: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting (e.g., XGBoost), SVM

Tools & Libraries: Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn

Technologies: Machine Learning, Classification, Financial Analytics

Features:

Data cleaning and preprocessing (handling missing values, encoding categorical data)

Exploratory Data Analysis (EDA) to find correlations and trends

Model training, hyperparameter tuning, and evaluation

Use of metrics like accuracy, precision, recall, F1-score, and ROC-AUC

Feature importance analysis to understand key factors affecting default risk

Applications:

Banks and lending institutions for risk assessment

Fintech apps offering personal or business loans

Credit rating systems and financial planning tools

Insurance underwriting and premium prediction

Outcome:

The project demonstrates how ML can improve financial decision-making by accurately identifying risky borrowers. It gives students hands-on experience in building classification models, handling financial data, and solving real-world business problems.