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: If you find this content valuable, don't forget to like, share, and subscribe for more in-depth tutorials on predictive modelling and data science. Share your thoughts and questions in the comments below – we love hearing from our community! You may also like to watch:- What is probability of default - https://youtu.be/N9rmiGFzSnw?si=2xO6QPHM7iwOT3EO Exploratory Data Analysis - https://youtu.be/fRVXW_oCTj0?si=ifvEe-gk-47Y0F4N Data Science Playlist - https://youtube.com/playlist?list=PL4GjoPPG4VqOmyh7hQ730evtLaz04LwSf&si=uAlA4E-8MYnqzDtW Pandas Full Playlist - https://youtube.com/playlist?list=PL4GjoPPG4VqP0BNdnIZQqjCYodmtFCcee NumPy Full Playlist - https://youtube.com/playlist?list=PL4GjoPPG4VqNz8W3JRDD-uRcM9WVJHYve Matplotlib Full Playlist - https://youtube.com/playlist?list=PL4GjoPPG4VqN4dsP39m4QfpZSUrv2y8Iu Seaborn Full Playlist - https://youtube.com/playlist?list=PL4GjoPPG4VqOAwSNw2I-PXUcjw1frHmW2 📌 PD Model Dev Series Playlist: https://www.youtube.com/playlist?list=PL4GjoPPG4VqOICQi3Zo7Yxo2Fe3zU8MfN Resources - Developed script in the video - https://github.com/LEARNEREA/Data_Science/blob/main/Scripts/Credit_Risk_PD_Model.ipynb Script to understand the diff. in scores - https://github.com/LEARNEREA/Data_Science/blob/main/Scripts/Understand_differences_in_numbers.ipynb Automated Pre-processing - https://github.com/LEARNEREA/Data_Science/blob/main/Scripts/PreProcessing_PD_Model.py Logistic saved model - https://github.com/LEARNEREA/Data_Science/blob/main/Scripts/logisticPDmodel.pkl Random Forest saved model - https://github.com/LEARNEREA/Data_Science/blob/main/Scripts/RandomForesPDmodel.zip XGB saved model - https://github.com/LEARNEREA/Data_Science/blob/main/Scripts/XGBpdModel.pkl Raw data utilised in the development - https://github.com/LEARNEREA/Data_Science/blob/main/Data/credit_risk_dataset.csv 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