Every dataset, no matter its origin, often faces the issue of missing values. Such gaps can skew analysis, lead to erroneous conclusions, and even derail machine learning models. In this course, Implementing Policy for Missing Values in Python, you’ll gain the ability to effectively handle and impute missing values in any dataset. First, you’ll explore the implications of missing data and understand foundational strategies like dropping instances or attributes. Next, you’ll discover the art and science of imputation, diving deep into techniques involving mean, median, and mode. Finally, you’ll learn how to utilize regression models and other advanced methods to intelligently predict and fill these data voids. When you’re finished with this course, you’ll have the skills and knowledge of data imputation needed to ensure dataset integrity and boost the quality of your data-driven decisions.
Author Name: Pratheerth Padman
Author Description:
Pratheerth is a Data Scientist who has entered the field after an eclectic mix of educational and work experiences. He has a Bachelor’s in Engineering in Mechatronics from India, Masters in Engineering Management from Australia and then a couple of years of work experience as a Production Engineer in the Middle East. Then when the A.I bug bit him, he dropped everything to dedicate his life to the field. He is currently working on mentoring, course creation and freelancing as a Data Scientist.

Table of Contents

• Course Overview
  2mins
• Filling in the Blanks: Basic Strategies and Simple Imputations
  34mins
• Beyond the Basics: Navigating the Maze of Advanced Imputation
  18mins