Loan Default Risk Prediction

Project Overview

This project builds a machine learning model to predict whether a customer is likely to default on a loan. It covers the complete ML pipeline from data preprocessing to model evaluation and hyperparameter tuning.

The objective is to help financial institutions identify high-risk customers and make better lending decisions.

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Dataset

The dataset contains customer financial and demographic details such as:

• Age, Income, Savings
• Monthly Expenses
• Credit Score
• Loan Amount & Loan Term
• Employment Years
• Home Ownership
• Education
• Marital Status
• Region
• Recent Default History

Target Variable

target_default_risk

• 0 → No Default
• 1 → Default

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Project Workflow

1. Data Cleaning

• Handled missing values using median imputation
• Fixed categorical inconsistencies (e.g., spelling issues)
• Removed irrelevant columns like customer_id

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2. Exploratory Data Analysis (EDA)

• Value counts and countplots for categorical features
• Distribution analysis of numerical features
• Correlation heatmap
• Outlier detection using boxplots

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3. Feature Engineering

Created new meaningful features:

• income_per_dependent
• savings_to_income_ratio

These features help capture financial pressure and stability.

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4. Data Preprocessing

• Train-test split (80-20)
• Ordinal Encoding for education
• One-Hot Encoding for categorical variables
• StandardScaler for numerical features
• Outlier treatment using IQR-based capping (Winsorization)

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5. Models Implemented

The following models were trained and evaluated:

• Logistic Regression
• Decision Tree
• Support Vector Machine (SVM)
• Random Forest
• XGBoost

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6. Hyperparameter Tuning

• Used RandomizedSearchCV and GridSearchCV

• Focused on improving:

  • Random Forest
  • XGBoost

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7. Evaluation Metrics

Models were evaluated using:

• Accuracy
• Precision
• Recall
• F1 Score
• Confusion Matrix

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Results

• Random Forest and XGBoost performed the best
• XGBoost achieved the highest accuracy after tuning
• Feature engineering significantly improved model performance

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Technologies Used

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn
• XGBoost
• Jupyter Notebook

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How to Run

1. Clone the repository

git clone https://github.com/prabandkumar/loan-default-risk-prediction.git

2. Install dependencies

pip install -r requirements.txt

3. Run the notebook

jupyter notebook

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Future Improvements

• Build a web app using Flask or FastAPI
• Add model explainability (SHAP values)
• Deploy model on cloud
• Improve feature engineering

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