Fill in missing data with realistic, representative synthetic datapoints without coding!
In this comprehensive tutorial, we delve into the complexities of handling missing values in data sets using MOSTLY AI's Smart Imputation feature.
Key moments:
00:00 - Introduction to Smart Imputation
00:03 - Understanding the Challenge of Missing Data
00:07 - Identifying the Significance of Missing Values
00:10 - Analyzing the Impact of Missing Data on Analysis and ML Models
00:17 - Strategies for Imputing Missing Values
00:26 - Introducing MOSTLY AI's Smart Imputation Feature
00:34 - Working with the UCI Adult Income Data Set
00:41 - Synthesizing Data with MOSTLY AI
00:50 - Quality Assurance of Synthetic Data
01:03 - Visualizing Original vs. Synthetic Data Distributions
01:10 - Comparing with the Ground Truth Data Set
01:17 - Advantages of Synthetic Data in Data Privacy
01:20 - Conclusion and Further Learning Resources
🔍 What You'll Learn:
- Why is it important to address missing values in data sets.
- The distinction between meaningful and problematic missing values.
- How to use MOSTLY AI for Smart Imputation to fill data gaps.
- Comparing imputed data with original and modified data sets.
- The role of synthetic data in preserving privacy while maximizing data utility.
🔗 Register your free account on MOSTLY AI's synthetic data platform: https://bit.ly/3M8Lhkb
Github repo with data and notebook: https://bit.ly/477r8Uj