How to handle missing data in a dataset in Machine Learning?
Answer / Pranay Kumar Suryavanshi
Handling missing data in a machine learning dataset can be done using several techniques: 1) Deletion Methods (Listwise and Pairwise): These methods either remove the entire row or column with missing values. 2) Imputation Methods: These methods fill missing values based on certain strategies, such as mean, median, mode imputation, or multiple imputation techniques (e.g., MICE). 3) Predictive Modeling Methods: These methods use machine learning algorithms to predict the missing values.
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