Missing values are an issue for both humans and machines. Learn how to address the problem of missing data points using a synthetic data generator. MOSTLY AI, the world's leading synthetic data generator, works like autocorrect for your data, filling in the gaps with realistic, statistically representative synthetic data.
Using a real-world example, the UCI Adult Income Survey dataset, this tutorial guides you through configuring Smart Imputation on MOSTLY AI's free synthetic data platform. The resulting synthetic dataset not only eliminates missing values but also closely models the original distribution, showcasing the power of generative AI for realistic value replacements. MOSTLY AI's Smart Imputation enhances human readability and the training data used for machine learning models.
🔍 Key highlights include:
00:00 - 00:03: Introduction to Dealing with Datasets and Missing Values
00:14 - 00:29: MOSTLY AI's Solution: Smart Imputation
01:25 - 01:39: Creating Synthetic Dataset with Smart Imputation
01:43 - 01:55: Analyzing Synthetic Data and Gap Closure
02:07 - 02:19: Impact of Smart Imputation on Data Distribution
02:32 - 02:53: Recovery of Original Distribution through Imputation
🔗 Sign up for a free account on MOSTLY AI's synthetic data platform: https://tinyurl.com/ymen9zz7
🔗 Learn more about Smart data imputation from this blog: https://tinyurl.com/yjssz6bw