How to Handle Missing Data with Python - Machine Learning Mastery


Data can have missing values for a number of reasons such as observations that were not recorded and data corruption. Handling missing data is important as many machine learning algorithms do not support data with missing values. In this tutorial, you will discover how to handle missing data for machine learning with Python. Note: The examples in this post assume that you have Python 2 or 3 with Pandas, NumPy and Scikit-Learn installed, specifically scikit-learn version 0.18 or higher. How to Handle Missing Values with Python Photo by CoCreatr, some rights reserved.