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Exercise

The hunt for missing values

Questions about processing missing values are integral to any machine learning interview. If you are provided with a dataset with missing values, not addressing them will likely skew your results and lower your model's accuracy.

In this exercise, you'll practice the first pre-processing step by finding and exploring ways to handle missing values using pandas and numpy on a customer loan dataset.

The dataset, which you'll use for many of the exercises in this course, is saved to your workspace as loan_data.

This is where you are in the pipeline:

Machine learning pipeline

Instructions 1/4
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  • 1
    • Print out the features of loan_data along with the number of missing values.
    • 2
      • Drop the rows with missing values and print the percentage of rows remaining.
    • 3
      • Drop the columns with missing values and print the percentage of columns remaining.
    • 4
      • Impute loan_data's missing values with 0 into loan_data_filled
      • Compare 'Credit Score' using .describe() before imputation using loan_data and after using loan_data_filled.
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