Missing Data in Scikit-learn Models

Requirements Clarification & Assessment

1. Understanding Scikit-learn's Capabilities:

   • Scikit-learn does not natively support datasets with missing values in most of its models.
   • Models will raise errors when encountering missing data unless preprocessed.

2. Assessing the Dataset:

   • Determine the extent and pattern of missing data in the dataset.
   • Identify whether missing data is random or follows a pattern.
   • Evaluate the impact of missing data on model performance and data integrity.

3. Choosing an Appropriate Strategy:

   • Consider the size of the dataset and the proportion of missing values.
   • Evaluate the importance of the missing values in the context of the analysis.
   • Decide between simple imputation, advanced imputation, or removal based on data characteristics.