Data preprocessing and feature engineering transform raw data into a format suitable for machine learning models. Real-world data is often messy, containing missing values, outliers, and inconsistent formats. This section teaches techniques to clean, prepare, and transform data, as well as methods to create and select features that improve model performance.
Data preparation is the process of collecting, cleaning, and organizing raw data to ensure accuracy and quality for analysis, reporting, and machine learning. It helps eliminate errors, improve consistency, and create reliable datasets that lead to better business insights and decision‑making.
Feature engineering is the process of selecting, creating, and transforming variables in a dataset to improve the performance of machine learning models. It enhances data quality, reveals hidden patterns, and boosts model accuracy by turning raw data into meaningful, predictive features.
Lessons are available in the subtopics above.
