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  3. Dealing with Missing Data in Python

Exercise

Analyze the summary of linear model

Analyzing the performance of the different imputed models is one of the most significant tasks in dealing with missing data. It determines, the type of imputed DataFrame you can rely upon. For analysis, you can fit a linear regression model on the imputed DataFrame and check for various parameters that impact the selection of the imputation type.

In this exercise, you have already been loaded with the DataFrame diabetes_cc which is the complete case of diabetes DataFrame. The complete case acts as a base for comparison against other imputed DataFrames. You will use the package statsmodels.api loaded as sm for creating a linear regression model and generating summaries.

Instructions 1/4

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  • Set all features in the DataFrame diabetes_cc as X by adding a constant, while excluding and setting the 'Class' column as y.