Complex pipelines can be opaque. We’ll configure sklearn to display interactive visual diagrams of your pipeline structure, making it easier to understand and debug the flow of data.

Combine features generated by different transformers. We’ll use FeatureUnion to concatenate results from multiple independent transformer pipelines into a single, rich feature set.

Different data types need different treatments. Learn to apply specific transformations to specific columns (e.g., scaling numbers vs. encoding text) simultaneously within a single workflow.

Stop manual preprocessing. We'll use Pipeline to chain steps—scaling, encoding, and modeling—into a single object, ensuring your training and testing data undergo identical transformations.

Extend sklearn's capabilities by building your own transformers. We’ll subclass BaseEstimator to create custom cleaning steps that integrate seamlessly into standard sklearn pipelines.

Sometimes continuous data works better as categories. Learn to use KBinsDiscretizer to transform continuous features into buckets, helping linear models handle non-linear relationships.

ML models require numbers, not strings. We’ll convert categorical data into machine-readable formats using One-Hot Encoding for nominal data and Ordinal Encoding for ranked categories.

Real-world data is rarely clean. Move beyond dropping rows by learning to impute missing values using simple strategies like the mean, or advanced multivariate techniques like KNN imputation.

Algorithms like SVM and KNN are sensitive to scale. We’ll apply Standardization and Min-Max scaling to normalize features, ensuring no single variable dominates the model due to magnitude.

Never test on training data. Learn to use train_test_split for validation, and explore stratified splitting to maintain class balance and time-series splitting for temporal data.