woe

Obtaining a probability of default for retail loan applicants by modelling.

Credit risk scorecard model using WOE/IV feature engineering and regularized logistic regression on Lending Club data

End-to-end solution for insurance fraud detection using WOE transformation and logistic regression scorecards. Includes preprocessing, model training, and a FastAPI service for real-time scoring with interpretable results.

This project demonstrates a machine learning solution for predicting diabetes based on user-provided health data. The application uses Streamlit for an interactive web interface and advanced interpretability tools like SHAP and permutation importance to explain model predictions.

Completed as part of the 365 Data Science Credit Risk Modeling in Python Udemy course. Developed an end-to-end credit risk modeling pipeline for consumer lending, covering data preprocessing, feature engineering, Probability of Default , Loss Given Default , Exposure at Default , scorecard development, model validation, population stability

This repo contains algorithms for data analysis required while building DL/ML models

Lead prediction case. Used GBM, RandomForest Linear Regression on H2O package

This project is using tweepy Twitter API. In this, we can get tweets trends of the country or any specific location, 100 or more tweets of the specific hashtag and sentiment analysis of specific hashtag tweets.

💰 Credit Risk Scorecard — Gradient Boosting + Logistic Regression + Decision Tree on 5,000 loans. Industry-standard metrics: Gini 0.521 · KS 0.395 · AUC 0.761 · IV/WoE table · Credit grades A-E. Basel III aligned. Production-realistic metrics. Python · scikit-learn

Production-grade Python toolkit for credit risk modeling — scorecards, LR, LightGBM/XGBoost, WOE/IV, PSI, KS, SHAP. Source-protected wheels via Cython.

End-to-end credit scoring pipeline: EDA → WoE/IV feature selection → Logistic Regression scorecard + LightGBM → OOT validation · FastAPI + Jinja2 UI

Transparent Python toolkit for probability-of-default model validation, monitoring, WoE/IV, calibration, discrimination, and stability

In this project, task is to help banking organization to identify the right customers using predictive models. Using past data of the bank’s applicants, you need to determine the factors affecting credit risk, create strategies to mitigate the acquisition risk and assess the financial benefit of the project.

Credit risk scorecard with WOE/IV, logistic regression, and a Power BI approval dashboard

Automatic optimal discretization pipeline

Interactive WOE (Weight of Evidence) and IV (Information Value) binning web tool for credit risk scoring, segmentation, and transparent scorecard development.

Weights of Evidence (WoE) and Frequency Ratio (FR) Scripts

Classification models to predict 2-year serious delinquency risk using raw and WOE-transformed datasets with logistic regression and machine learning techniques.