This course delves into the critical preprocessing steps required to convert raw data into meaningful formats for analysis. Students will learn techniques for handling missing data, detecting outliers, scaling features, and encoding categorical variables. The course also emphasizes feature engineering and selection strategies to improve the performance of machine learning models. Through practical exercise.
Prerequisite Courses