Hands-on Feature Selection in Python | Choose just the right features for your model | Data Science

🚀 In this hands-on tutorial we use the Parkinson's Disease dataset from Kaggle! 📊 We apply various feature selection techniques that have been introduced in the previous video - variance threshold, select k best, recursive feature elimination (RFE), select from model, and sequential feature selector. Dataset Link: https://www.kaggle.com/datasets/thecansin/parkinsons-data-set 🧠 Follow along as we implement logistic regression models for each technique and evaluate their performance using F1-score with cross-validation. The exciting part? We explore how the model's performance remains robust even as we systematically reduce the number of features. 📈 Discover the power of feature selection in enhancing model efficiency and interpretability, all while ensuring optimal performance. Happy Learning!