Data Preprocessing and Feature Engineering

Data Preprocessing and Feature Engineering focus on preparing raw data and creating meaningful input features for machine learning models. Proper preprocessing improves data quality, while feature engineering enhances model accuracy and performance. This training explains techniques such as data cleaning, normalization, encoding, handling missing values, and outlier detection. It also covers feature selection, feature extraction, dimensionality reduction, and transformation methods used in predictive modeling. You will learn how to prepare structured and unstructured datasets for analytics and machine learning workflows. The course also highlights best practices for building efficient, scalable, and high-quality data pipelines.