Epython Lab

🚀 Model Comparison for Loan Classification

4 years ago, I built and compared several classification models to predict loan applicants as Creditworthy or Non-Creditworthy. After performing data cleansing, handling missing values, and tuning parameters, I evaluated the models using precision, recall, and F1-score.

🔍 The Random Forest Classifier stood out with an AUC of 80% and an accuracy of 79%, successfully classifying 418 loans as Creditworthy and 82 as Non-Creditworthy.

Looking back, it's been a great learning experience, and I encourage exploring different tuning parameters and cross-validation techniques to improve model performance even further.
Check out the full source code on GitHub! 💻
https://medium.com/@epythonlab/best-practices-of-classification-models-towards-predicting-loan-type-c510d9b0dff6

Medium

Best Practices for Classification Models in Predicting Loan Types

In this article, I have outlined the best practices for machine learning models that predict loan types (Creditworthy or Non-Creditworthy).

www.tgoop.com/epythonlab/1972

720 viewsApr 25 at 14:16

🚀 Model Comparison for Loan Classification

4 years ago, I built and compared several classification models to predict loan applicants as Creditworthy or Non-Creditworthy. After performing data cleansing, handling missing values, and tuning parameters, I evaluated the models using precision, recall, and F1-score.

🔍 The Random Forest Classifier stood out with an AUC of 80% and an accuracy of 79%, successfully classifying 418 loans as Creditworthy and 82 as Non-Creditworthy.

Looking back, it's been a great learning experience, and I encourage exploring different tuning parameters and cross-validation techniques to improve model performance even further.
Check out the full source code on GitHub! 💻
https://medium.com/@epythonlab/best-practices-of-classification-models-towards-predicting-loan-type-c510d9b0dff6

BY Epython Lab