Feature Engineering and Data Transformation

In this lesson, we will learn feature engineering and data transformation. Creating features, encoding, scaling, binning, date features, text features, interaction features, missing value handling, and preparing an ML-ready dataset will be explained. ## Lesson objective In this lesson, we will learn Feature Engineering and Data Transformation. In the previous lesson, we covered Model Evaluation and Metrics. We explained train/test performance, baseline model, confusion matrix, accuracy, precision, recall, F1-score, ROC-AUC, MAE, RMSE, R², MAPE, validation data, cross-validation, and error analysis.