Hands-on Class Imbalance Treatment in Python | Oversampling | Undersampling | SMOTE | Data Science

🚀 In this video, we show you how to handle imbalanced datasets in Python! 🐍 This video is a sequel to our previous video which covered the theoretical aspects. Here we roll up our sleeves and apply various techniques to address imbalances effectively using imblearn package. 📊 We kick off by loading the popular Cancer dataset from scikit-learn library. This dataset features a target column with two categories: Malignant (0) and Benign (1). Each step of the way, we demonstrate the imbalances present and then apply appropriate techniques to bring equilibrium to the data. 🔄 Explore the effectiveness of Random Undersampling, Random Oversampling, Tomek Links, SMOTE (Synthetic Minority Over-sampling Technique), SMOTE Tomek, and Adasyn. Witness firsthand how these techniques transform the dataset, mitigating imbalances and paving the way for more accurate and robust machine learning models. 🔍 Ready to bridge the gap between theory and application? Join us on this hands-on journey to master the art of handling imbalanced datasets in Python. Happy Learning!