Скачать или смотреть Credit Risk Scorecards (6) | Exploratory Data Analysis or EDA in Python

Are you struggling to understand Credit Risk Scorecards?
In this video (Lecture 5 of our series), I’ll take you step by step through Data Preparation & Exploratory Data Analysis (EDA) — the most crucial phase in credit risk model development.

Whether you’re a beginner or already working in risk analytics, this session will show you how banks actually clean, refine, and validate data before scorecard modeling — and how you can apply the same techniques using Python in 2025.

Here’s what you’ll discover in this video:
✅ Exploratory Data Analysis (EDA) for credit risk datasets
✅ Exclusion of irrelevant variables (IDs, dates, target leakage)
✅ How to detect & treat missing values and outliers in banking data
✅ Understanding Weight of Evidence (WoE) & Information Value (IV)
✅ Multicollinearity checks using Correlation & Variance Inflation Factor (VIF)
✅ Exclusion criteria to remove biased or unstable data
✅ Finalizing feature selection for a clean, stable dataset

By the end, you’ll know exactly how to transform raw customer data into a model-ready dataset — a foundation for regulator-compliant and high-performing credit risk scorecards.

⏱️ Timestamps
00:00 – Intro to Step 3: Data Preparation & EDA
03:32 – Outliers & their impact on model accuracy
14:25 – Weight of Evidence (WoE) & Information Value (IV) explained
26:27 – Multicollinearity: Correlation Matrix & VIF
32:53 – Exclusion Criteria: Removing bias & irrelevant data
37:37 – Final Feature Selection (34 → 28 predictors)
41:25 – Wrap-up & Next Step: Segmentation

👉 If you found this video helpful, please Like 👍, Comment 💬, and Subscribe 🔔 for more content on Credit Risk Modeling, Exploratory Data Analysis (EDA), Data Science & Banking Analytics.

This Video:
credit risk scorecard development in python
data preparation and EDA in credit risk modeling
exploratory data analysis in banking datasets
how to detect and treat outliers in EDA
weight of evidence (WoE) & information value (IV) explained
multicollinearity and VIF in credit risk scorecards
feature selection in credit risk models
exclusion rules for bias in credit modeling
python coding for credit risk scorecards

🙋 About Me
I’m Rajat Kumar. On this channel, I share career tips, coding tutorials, and risk analytics knowledge to help you land your dream job in Data Science & Banking.

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