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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

This exercise is part of the course

Practicing Machine Learning Interview Questions in Python

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import modules
import numpy as np
import pandas as pd

# Print missing values
print(____.____().____())
Edit and Run Code