DAY 20 | Credit Risk Feature Selection in Python-Part 1 | 100 Days of ECL MASTERY SERIES #creditrisk

DAY 20 | Credit Risk Feature Selection in Python-Part 1 | 100 Days of ECL MASTERY SERIES Credit Risk Feature Selection in Python: WoE, IV, VIF, Correlation, Lasso, Stepwise, OOT and PSI explained step by step. Learn the 10-step pipeline that takes you from 300 variables to a defensible final PD model for IFRS 9 ECL, interviews and audits. --- 📚 TOPICS COVERED: • Importance of feature selection in PD modelling and model validation • Complete 10-step feature selection pipeline • Regulatory expectations: IFRS 9, Basel/EBA IRB, SR 11-7 & PRA SS1/23 • Data quality checks, missing values, imputation and data leakage • Weight of Evidence (WoE), WoE binning, monotonicity and OptimalBinning • Information Value (IV), thresholds and limitations • Correlation analysis: Pearson, Spearman and Cramér's V • VIF, multicollinearity, p-values and coefficient stability • Business logic, sign checks and Lasso vs Stepwise selection • OOS/OOT validation, Population Stability Index (PSI) and CSI • Python implementation toolkit: pandas, optbinning, statsmodels and scikit-learn • Interview Hot Seat: 6 key questions and 5 mistakes auditors catch first Coming next: Part 2 with the full Python implementation, then Logistic Regression in Credit Risk. --- 🔗 RESOURCES: 📚 IFRS 9 & Expected Credit Loss: Complete Guide    • IFRS 9 & Expected Credit Loss (ECL): Compl...   📚 Model Risk Mangement (MRM): Complete Guide    • Model Risk Management (MRM): The Complete ...   📚 Complete 100 Days of ECL Mastery Series Playlist:    • 100 Days of ECL MASTERY SERIES #creditrisk   📚PD Scorecard Model Development in Python:    • Python for Credit Risk Modeling | Start He...   --- 🎯 INTERVIEW QUESTIONS YOU'LL BE READY FOR: 1.What is feature selection in credit risk modelling? 2.How to do feature selection in Python for a PD model? 3.What is Weight of Evidence (WoE) in credit risk? 4.How to calculate WoE and IV in Python? 5.What is a good Information Value (IV) for a variable? 6.Why is IV above 0.5 suspicious? 7.What are the limitations of Information Value? 8.How to do WoE binning in Python using optbinning? 9.Why should WoE bins be monotonic? 10.What is VIF in credit risk models? 11.What is an acceptable VIF value in logistic regression? 12.What is the difference between correlation and VIF? 13.How to remove multicollinearity in a credit scorecard? 14.What is data leakage in credit risk models? 15.How to detect data leakage in a PD model? 16.What is the difference between Lasso and stepwise selection? 17.What is out-of-time (OOT) validation in credit risk? 18.What is the difference between OOS and OOT? 19.What is PSI and how is it calculated in credit risk? 20.What is a good PSI value for a credit risk model? 21.What is the difference between PSI and CSI? 22.Why should coefficient signs be checked in a logistic regression PD model? 23.What do regulators expect for variable selection under IFRS 9? 24.What are the most common mistakes auditors find in credit risk models? 25.What are the top credit risk modelling interview questions? --- 🔔 SUBSCRIBE ‪@riskmodellinghub‬ & HIT BELL FOR: ✓ Weekly credit risk modeling tutorials ✓ IFRS 9 & Basel deep dives and case studies ✓ Interview preparation for banking roles ✓ PD, LGD, EAD model validation techniques ✓ Real-world examples from major institutions ✓ Career advancement strategies in risk management --- 📖 THIS IS VIDEO 20 OF 100 IN OUR SERIES: If you complete this entire 100-video series, you'll understand credit risk modeling at an expert level & qualified to explain these concepts confidently in any interview, implement models in any bank, and validate frameworks used by regulators. --- 📊 DISCLAIMERS & IMPORTANT NOTES: This educational content is for learning purposes. The techniques shown are industry-standard methods used in professional credit risk modeling. For actual production implementations, consult with your organization's risk management and compliance teams. --- © Risk Modelling Hub - All Rights Reserved Educational Content for Finance Professionals --- #creditrisk #ifrs9 #featureselection #woe #ecl #bankingexplained #riskmanagement #pythonforfinance #datascience credit risk feature selection, feature selection in python, woe and iv in python, weight of evidence, information value, vif multicollinearity, variance inflation factor, credit risk modelling, credit risk modeling python, pd model development, probability of default model, ecl model, expected credit loss, ifrs 9 ecl, ifrs 9 impairment, credit scorecard development, scorecard variable selection, woe binning, optimal binning python, population stability index, psi in credit risk, out of time validation, lasso feature selection, stepwise regression, data leakage in machine learning, credit risk interview questions, model validation banking, risk modelling hub