Handling Missing Data
Explore techniques to address missing data in real-world datasets. Understand how to impute missing values using the mean and how to remove incomplete rows. Learn when to apply each method to prepare data effectively for machine learning models.
Missing data is unavoidable in real-world datasets, and how you deal with it can significantly impact the quality of your analysis or model. In this lesson, we’ll practice two foundational techniques: imputing missing values using the mean and dropping incomplete rows entirely. Let’s get started.
Imputing missing values with the mean
You are provided with a dataset containing customer information. Some of the values are missing, represented as None. Your task is to implement a function that imputes the missing values in the dataset using the mean of the available values for each feature.
Implement a function impute_missing_values(data) (line 19) that fills the missing values in ...