Feature Scaling & Normalization

Summary

Rescaling features to comparable ranges (standardization, min-max scaling) — required for distance-based and gradient-based algorithms to converge and perform correctly.

How to Learn This

1Practice StandardScaler and MinMaxScaler in scikit-learn.
2Learn which algorithms actually need scaling (KNN, SVM, neural nets) vs. which don't (tree-based models).
3Fit scalers only on training data and apply them to test data to avoid leakage.

Customer Churn Classifier

One-Hot & Label Encoding

More topics in Feature Engineering & Model Evaluation

One-Hot & Label Encoding Feature Selection Handling Imbalanced Data Hyperparameter Tuning (Grid/Random Search)Ensemble Methods (Bagging, Boosting)XGBoost & LightGBM Evaluation Metrics (Accuracy, Precision, Recall, F1)ROC-AUC Confusion Matrix Bias-Variance Tradeoff Overfitting & Regularization (L1/L2)

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