Dealing with missing data
Dealing with missing data Data Science Project
Introduction to Supervised Learning with scikit-learn

Dealing with missing data

During this project, we are focusing on different ways of handling missing values. We are exploring various techniques and strategies to effectively deal with missing values in the dataset.
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Dealing with missing dataDealing with missing data
Project Created by

Verónica Barraza

Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

multiplechoice

Did you find any missing value?

Calculate the percentage of missing values.

codevalidated

Drop rows with missing values

Drop rows with missing values, and store the new dataframe in data_dropna.

codevalidated

Fill missing values with mean and mode

Fill missing values with the mean for age and Salary, and the mode value for UniversityDegree and Sex. Store the result in data_filled.

multiplechoice

Analyzing the Impact

Based on previous code select the correct sentence.

Dealing with missing dataDealing with missing data
Project Created by

Verónica Barraza

This project is part of

Introduction to Supervised Learning with scikit-learn

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