Feature Engineering and Preprocessing

Feature engineering and preprocessing are among the most impactful steps in a machine learning project. The quality and representation of your features often matter more than the choice of algorithm. Better features lead to better models — even a simple algorithm with well-engineered features can outperform a complex algorithm with raw, unprocessed data.

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Why Feature Engineering Matters

Aspect	Impact
Model performance	Good features capture the true signal in data, improving accuracy
Training speed	Fewer, better features mean faster training
Interpretability	Meaningful features make models easier to understand
Generalisation	Proper preprocessing prevents data leakage and overfitting

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Data Preprocessing

Handling Missing Values

Strategy	When to Use	Scikit-Learn Class
Drop rows	Few missing values, large dataset	DataFrame.dropna()
Mean/Median imputation	Numerical features, missing at random	SimpleImputer(strategy='mean')
Mode imputation	Categorical features	SimpleImputer(strategy='most_frequent')
KNN imputation	Values related to nearby data points	KNNImputer(n_neighbors=5)
Iterative imputation	Complex relationships between features	IterativeImputer()