Complete Feature Engineering in OneShot - 5 Hours

Learn comprehensive feature engineering techniques through this 5-hour tutorial covering the complete data preprocessing pipeline from raw data handling to advanced feature selection methods. Master the fundamentals by exploring different feature types including numerical, categorical, ordinal, binary, date-time, text, image, and signal data, while understanding the distinctions between feature engineering, selection, and extraction. Develop skills in handling missing data through various imputation techniques such as mean/median imputation, forward/backward fill, KNN imputation, and regression-based methods. Gain expertise in categorical encoding methods including label encoding, one-hot encoding, ordinal encoding, frequency encoding, and target encoding. Understand the importance of feature scaling and implement standard scaling, MinMax scaling, robust scaling, MaxAbs scaling, log transformation, and power transformation techniques. Learn to identify and handle outliers using Z-score analysis, IQR method, winsorization, isolation forest, and local outlier factor approaches. Explore advanced feature creation techniques including ratio features, aggregation-based features, polynomial interactions, boolean rules, cumulative features, and rank-based features. Master feature transformation methods such as square root transformation and binning techniques using equal width and frequency approaches. Dive into dimensionality reduction with Principal Component Analysis, explained variance analysis, Linear Discriminant Analysis, and feature agglomeration. Complete your learning with feature selection methodologies including variance threshold, correlation-based selection, SelectKBest, recursive feature elimination, L1 regularization (Lasso), and tree-based feature importance, with practical implementation and free source code provided.