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Handling missing data is a crucial step in the data preprocessing phase of any data science project. The approach taken to manage missing data can significantly impact the results and insights derived from the analysis. Below are several strategies employed to address missing data, categorized by the type of data: numerical and categorical.
Mean Imputation:
Median Imputation:
Forward/Backward Fill:
Predictive Modeling (e.g., Linear Regression):
Mode Imputation:
New Category Creation:
K-Nearest Neighbors (KNN) Imputation:
Multiple Imputation:
In conclusion, the choice of strategy for handling missing data depends on the context and nature of the dataset. It's crucial to assess the impact of each method on the analysis and choose the one that aligns best with the objectives of the project.