information-value

This repository contains analysis of churn in telephone service company (using IV and WOE), comparison of effect size and information value and quick tutorial how to use information value module (created for this analysis).

Find missing values in data set using Euclid distance, normalization and calculating information value, weight of evidence

Logistic Regression technique in machine learning both theory and code in Python. Includes topics from Assumptions, Multi Class Classifications, Regularization (l1 and l2), Weight of Evidence and Information Value

Automatic optimal discretization pipeline

Weight of Evidence Encoding & Information Value

The objective of this project is to predict credit score of the borrowers using logistic regression and provide threshold cut-off recommendation

The objective of this project is to predict credit score of the borrowers using logistic regression and provide threshold cut-off recommendation

Credit Score Modelling: Perform a Weight of Evidence Logistic Regression Modelling (WoELR) to generate credit scorecard for loan approval with the help of optbinning library.

The repo consists of Statistics Algorithms

Implementation of weight of evidence and Information value techniques using Python

Credit Score Modelling: Perform a Weight of Evidence Logistic Regression Modelling (WoELR) to generate credit scorecard for loan approval.

The creation of the credit risk prediction models of a lending company.

Capstone Project: Acquisition ScoreCard

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

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

💰 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

LIDE v3.3 — Latent Information Discovery Engine: Epistemic Decompression · Verification · Mechanism Isolation · Experiment Design

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

IFRS 9 Credit Risk Scorecard & Expected Credit Loss (ECL = PD * LGD * EAD) Engine under Basel III / EBA standards. Features R Weight of Evidence (WoE) binning & Information Value, Python PD models (Logistic Regression Gini=0.7467 vs XGBoost), 3-Stage Staging, PostgreSQL, automated Excel financial models, and a 2-page Power BI Dashboard.