Dealing with Missing Data in Python

Get familiar with missing data and how it impacts your analysis! Learn about different null value operations in your dataset, how to find missing data and summarizing missingness in your data.

Analyzing the type of missingness in your dataset is a very important step towards treating missing values. In this chapter, you'll learn in detail how to establish patterns in your missing and non-missing data, and how to appropriately treat the missingness using simple techniques such as listwise deletion.

Embark on the world of data imputation! In this chapter, you will apply basic imputation techniques to fill in missing data and visualize your imputations to be able to evaluate your imputations' performance.

Finally, go beyond simple imputation techniques and make the most of your dataset by using advanced imputation techniques that rely on machine learning models, to be able to accurately impute and evaluate your missing data. You will be using methods such as KNN and MICE in order to get the most out of your missing data!