Machine Learning | Predictive Analytics | Risk Scoring

Overview

This project develops a Credit Risk Classification Model to predict whether a loan applicant is likely to default or repay. The model uses demographic, financial, and behavioral factors to classify clients into Low Risk or High Risk, enabling lenders to make more informed and profitable decisions.

Business Problem

Lenders struggle when:

• High-risk customers are approved

• Low-risk customers are mistakenly denied

• Default rates increase due to poor risk screening

• Decisions rely on manual review instead of data-driven scoring

A predictive model helps automate and improve the loan approval process, reduce defaults, and optimize lending strategies.

Project Objectives

1. Build a supervised classification model for credit risk

2. Identify which customer attributes influence default risk

3. Optimize model accuracy using proper training and validation

4. Visualize model performance using industry metrics

5. Create actionable business insights for lending decisions

Tools & Technologies

• Python

• Pandas, NumPy

• Scikit-learn

• Matplotlib

• Seaborn (for EDA only)

• Jupyter Notebook

Data Preparation & Cleaning

Preparation steps included:

• Handling missing values

• Encoding categorical variables (One-Hot, Label Encoding)

• Standardizing numerical features

• Splitting data into train and test sets

• Checking for class imbalance (often a major issue in credit datasets)

Applied techniques:

• Correlation analysis

• Outlier handling

• Feature transformation

• Oversampling / undersampling (if applicable)

Exploratory Data Analysis

EDA revealed trends such as:

• Higher default rates for lower-income or high-debt customers

• Employment length and loan purpose influence risk

• Customers with poor credit histories are the strongest predictors

Visualization examples:

• Income distribution by risk class

• Loan amount vs. credit score

• Heatmap of feature correlations

Modeling Approach

Multiple algorithms were tested:

• Logistic Regression

• Random Forest Classifier

• Decision Tree

• Gradient Boosting / XGBoost (optional)

Best Model: Random Forest (typically performs well on tabular risk data)

Performance Evaluation Metrics:

• Accuracy

• Precision

• Recall

• F1 Score

• Confusion Matrix

• ROC Curve (optional)

These help understand both predictive power and risk of misclassification.

Business Impact

A well-calibrated credit risk model can help lenders:

• Reduce default rates

• Improve loan approval accuracy

• Increase overall profitability

• Make faster, automated decisions

• Enhance fairness and consistency in credit decisions

Deliverables

• Cleaned and prepared dataset

• Jupyter Notebook with full EDA + model pipeline

• Visualizations (confusion matrix, feature importance)

• Final trained model and evaluation summary

• Recommendations for production deployment

Project Links

• View Github Repository

• Read Full Medium Article

• View Power BI Dashboard

Let’s Work Together

I help organizations build predictive analytics solutions that improve decision-making and reduce operational risk.