Categorical Features One-Hot Encoding, Scaling & Class Balancing | PD Model Dev - 2

In this instalment, we explore advanced techniques for optimizing your PD models. Dive deep into the world of categorical features, discover the power of one-hot encoding, learn the importance of scaling, and understand strategies for class balancing.
📊 Key Topics Covered:
Categorical Features Handling
One-Hot Encoding Explained
Scaling Techniques for Model Optimization
Strategies for Balancing the Minority Class
🌐 Series Overview:
This video is the second part of our comprehensive PD Model Development Series. Whether you're a seasoned data scientist or just starting your journey, this series equips you with the skills to build robust predictive models for credit risk assessment.
🎓 Who is This For?
Data Scientists and Analysts
Credit Risk Modelers
Machine Learning Enthusiasts
Those Seeking Advanced PD Model Techniques
👍 Engage With Us:
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Resources -
Developed script in the video - github.com/LEA...
Script to understand the diff. in scores - github.com/LEA...
Automated Pre-processing - github.com/LEA...
Logistic saved model - github.com/LEA...
Random Forest saved model - github.com/LEA...
XGB saved model - github.com/LEA...
Raw data utilised in the development - github.com/LEA...
Ready to take your PD modelling skills to the next level? Watch now and enhance your knowledge of categorical features and advanced model optimization techniques!
#DataScience #PredictiveModeling #PDModelDevelopment #CategoricalEncoding #DataOptimization #MachineLearning