Feature Engineering Step by Step: ML Data Preparation

Learn strategies to handle missing data, from mean, median, and mode imputation to modeling-based approaches like KNN and regression. Apply these to numerical and categorical features, including frequent category imputation.

Explore feature transformation techniques for machine learning, including scaling, normalization, log and power transformations, binning, and one hot encoding for categorical data.

Learn to scale and normalize features using standardization to zero mean and unit variance or min-max normalization to 0–1, improving model training on housing data.

Compare one-hot encoding and label encoding for transforming categorical data into numerical features, weighing dimensionality against potential ordinal assumptions in different datasets.

Learn feature extraction and creation techniques to enhance machine learning models. Apply polynomial and interaction features, and extract time-based and text features using word counts, TF-IDF, and IDF.

Create polynomial features by raising existing features and interacting them to reveal non-linear relationships that linear models miss. Use ridge or lasso regularization to balance complexity and reduce overfitting.

Combine two or more original features to create interaction features that reveal how variables influence each other, helping models capture non-linear relationships and improve predictions without excessive dimensionality.

Extract features from dates and text to convert patterns in data into numerical inputs. Use year, month, day, day of week, word counts, and TF-IDF to improve model predictions.

Identify the most informative features to boost model performance and reduce dimensionality using correlation matrices, Anova, chi square, regularization, and tree-based methods.

Explore feature engineering with statistical methods like correlation matrices, anova, and chi-square tests to select features that most relate to the target variable, as shown with housing data.

Explore how regularization techniques like Lasso, Ridge, and Elastic Net prevent overfitting by penalizing regression coefficients, enable feature selection, and improve generalization in high-dimensional data with multicollinearity.

Explore tree-based methods like random forest and xgboost for classification and regression, and learn how feature importance scores—using features such as bedrooms, bathrooms, and square footage—guide selection.

Discover advanced feature engineering techniques, including target encoding, feature hashing, and principal component analysis, plus time series features like lag and rolling statistics for housing price prediction.

Automate feature engineering using tools like feature tools and deep feature synthesis to generate and select features, explore AutoML for model optimization, and handle time series and complex data efficiently.

Master feature engineering with best practices for documentation, tracking, and version control to ensure reproducible models that address bias, fairness, and interpretability through continuous learning.

Maintain model relevance and performance through continuous learning, iteration, and feature engineering that adapt to evolving data patterns, reveal hidden relationships, and balance predictive power with efficiency.