🏦 Predictive Analytics for Credit Risk Assessment - Using Python and Azure Machine Learning Studio

Project Overview

This project aims to predict credit risk using advanced data mining techniques, which assists lenders in making informed decisions. By analyzing a borrower’s likelihood of defaulting on a loan, this project helps mitigate potential financial risks before extending credit.

Objectives

To develop a robust model that can accurately predict creditworthiness.
To compare the effectiveness of different machine learning algorithms in credit risk assessment.

Technologies Used

Python: Employed for data cleaning, manipulation, and feature engineering.
Azure ML: Used for building, training, and evaluating machine learning models.
Microsoft Excel: Utilized for preliminary data analysis and visualization.

Data Collection and Pre-processing

Dataset Overview: The analysis was conducted on a dataset consisting of 25,000 customer records, originally extracted from a larger dataset of 2.2 million entries.
Cleaning and Transformation: Python scripts were used to clean the data, handle missing values, and transform features to be suitable for modeling.

Modeling Techniques

Multiclass Logistic Regression: Implemented to predict the probability of a customer being a good or bad credit risk based on historical data.
Multiclass Decision Forest: A more complex model that uses an ensemble of decision trees to improve prediction accuracy.

Results

Model Performance: Both models were rigorously tested. Logistic Regression offered a balance between accuracy and computational efficiency, whereas the Decision Forest provided higher accuracy at the cost of increased complexity.
Insights: The Decision Forest model exhibited a higher true positive rate, suggesting it is better at identifying actual credit risks.

Challenges Overcome

Data Skewness: Addressed the imbalance in the dataset by applying appropriate sampling techniques.
Feature Selection: Implemented various techniques to identify the most predictive features, which enhanced model performance.

Conclusion

The project underscores the importance of using advanced data mining techniques in financial risk management. The insights gained from this study not only enhance decision-making processes but also reduce the likelihood of financial losses due to credit defaults.

Future Work

Model Improvement: Explore additional data sources and feature engineering techniques to further enhance model accuracy.
Deployment: Develop a pipeline for real-time risk assessment in financial applications.