This course offers a deep dive into addressing dataset incompleteness. From basic drop methods to intricate regression imputations, emerge equipped to tackle any missing data challenge with confidence.
This course offers a deep dive into addressing dataset incompleteness. From basic drop methods to intricate regression imputations, emerge equipped to tackle any missing data challenge with confidence.
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.
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.