Building a Complete Credit Scoring Scorecard in Python for Financial Risk Analysis скачать в хорошем качестве

Building a Complete Credit Scoring Scorecard in Python for Financial Risk Analysis

Learn how to build a complete credit scorecard in Python, starting from raw data and progressing through feature engineering, model training, and evaluation. This tutorial covers essential steps such as data loading, cleaning, exploratory analysis, variable selection using weight of evidence (WOE) and information value (IV), and logistic regression modeling. You will also learn how to handle missing target labels with reject inference and scale model outputs into interpretable credit scores. Follow along to see how to visualize data quality, assess model performance with ROC and KS statistics, and implement best practices for credit risk modeling. By the end, you will have a clear workflow for developing, validating, and deploying a robust credit scoring solution using Python. 00:00 Introduction and objectives 01:00 Importing libraries and setting up the environment 02:00 Loading accepted and rejected datasets 03:00 Data dictionary and variable overview 04:00 Creating and analyzing the target variable 05:00 Data inspection and missing value analysis 06:30 Visualizing missing values and target distribution 08:00 Numeric summary and correlation analysis 09:30 Calculating WOE and IV for feature selection 11:00 Identifying top predictive variables 12:00 Preparing data for modeling and avoiding leakage 13:00 Splitting data into training and validation sets 14:00 Training the base logistic regression model 15:00 Model evaluation with ROC and lift charts 16:30 Applying reject inference (fuzzy augmentation) 18:00 Aligning and scoring rejected applicants 19:00 Combining accepted and rejected data for modeling 20:00 Training the final model with augmented data 21:00 Scaling model outputs to credit scores 22:00 Visualizing score distributions for good vs bad loans 23:00 Validating model with ROC and KS statistics 24:00 Analyzing average bad rate by score decile 25:00 Recap and key takeaways 26:00 Creative challenge and next steps #CreditScoring #Python #MachineLearning