Handling Missing Data

Handling missing data is an essential task in feature engineering, ensuring that the datasets used for machine learning models are as accurate and complete as possible. This section talks about the common challenges posed by missing data and the strategies available to address them effectively. You'll gain insights into why data might be missing and how that can impact the performance of your models.

As you progress through this section, you'll learn about various techniques for detecting and managing missing data, including imputation methods like mean, median, and mode replacement, as well as more advanced approaches like k-nearest neighbors and multiple imputation. You'll also look into when it might be more appropriate to simply remove data points or features with missing values.

By the end of this section, you'll be equipped with practical skills to handle missing data, enabling you to preprocess your datasets more effectively and improve the strength of your predictive models.