Implementing Policy for Missing Values in Python

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Chris Behrens

54:00

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1. Course Overview

1. Course Overview.mp4

02:00

02. Filling in the Blanks - Basic Strategies and Simple Imputations

01. Course and Module Introduction.mp4

01:51

02. What Is Missing Data and What Causes It.mp4

04:13

03. The Impact of Missing Data.mp4

01:15

04. The Decision Crossroads To Drop or Not.mp4

01:46

05. Introduction to Imputation.mp4

01:19

06. Exploring Imputation with Mean.mp4

01:37

07. Balancing Data with Median.mp4

01:48

08. Catering to Categoricals - Imputing with Mode.mp4

01:35

09. Sequencing Solutions - Forward and Backward Fill.mp4

01:34

10. Demo - Introduction to the Dataset.mp4

02:43

11. Demo - Setting up Your Environment.mp4

02:04

12. Demo - Dealing with Missing Data - Part 1.mp4

05:15

13. Demo - Dealing with Missing Data - Part 2.mp4

05:27

14. Module Summary.mp4

01:28

3. Beyond the Basics - Navigating the Maze of Advanced Imputation

1. Module Introduction.mp4

00:51

2. Regression Imputation Unveiled.mp4

02:11

3. How Does Regression Fill the Void.mp4

02:43

4. Beyond Regression - KNN and MICE.mp4

03:49

5. Choosing the Right Technique.mp4

02:57

6. Demo - Imputation of Numerical Missing Values Using Reg.mp4

03:41

7. Module Summary.mp4

01:53

Description

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.

What You'll Learn?

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.

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Python

Chris Behrens

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Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.