How Do You Handle Missing Data in Machine Learning Projects?
Real-world datasets often contain gaps. Over 70% of them face incomplete entries, which can distort model performance. For example, the Titanic dataset has empty ‘Age’ and ‘Cabin’ fields—common issues impacting predictions. Ignoring these gaps risks biased results and a 22%…
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