Feature Engineering and Transformation

Feature engineering is the process of selecting, modifying, and creating new features to improve the predictive performance of a model. It is one of the most important aspects of data preprocessing, as high-quality features lead to better results. Some key techniques include:

• Normalization and Standardization: Transforming numerical data to a consistent scale, such as using Min-Max Scaling or Z-score normalization.
• Encoding Categorical Variables: Converting categorical data into numerical values using methods like One-Hot Encoding or Label Encoding.
• Feature Selection: Removing irrelevant or redundant features to improve model efficiency and prevent overfitting.
• Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) and t-SNE help reduce the number of features while preserving important information.
• Polynomial Features: Creating new features by combining existing ones to capture more complex relationships in the data.

Effective feature engineering enhances model interpretability and accuracy, allowing machine learning algorithms to extract meaningful insights from the data.