Tech Fixx · Video
How to Fix Bad Training Data: Missing Values, Duplicates & Bias
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Bad training data can make an AI model inaccurate, unfair or unreliable. This practical guide shows how to diagnose common dataset problems before training and how to choose a correction that matches the situation.
You’ll learn: • Detecting missing and impossible values • Finding duplicate or conflicting records • Standardising formats, units and categories • Checking label errors and class imbalance • Looking for sampling and representation bias • Separating genuine outliers from data-entry mistakes • Recording every cleaning decision
Do not delete unusual data automatically: an outlier can be an error, a rare but valid case or the most important signal in the dataset.
#TrainingData #DataQuality #ResponsibleAI