Practice - Lab: Comprehensive Data Cleaning, Transformation, and Basic Feature Engineering
Practice Questions
Test your understanding with targeted questions
What is the purpose of data cleaning?
💡 Hint: Think about why we need accurate data for ML models.
Define feature scaling.
💡 Hint: Consider how different ranges can affect models.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary reason for applying feature scaling in machine learning?
💡 Hint: Think about how different scales affect distance calculations.
True or False: Imputation of missing values can reduce dataset variance.
💡 Hint: Consider the effects of filling in values on data distribution.
1 more question available
Challenge Problems
Push your limits with advanced challenges
You are given a dataset with 30% missing values in several important columns and a few outliers. What steps would you take to prepare the data for analysis?
💡 Hint: Focus on the impact of missing data on your analysis.
After applying PCA to your dataset, you find that the first principal component explains 80% of the variance. What does this imply for the remaining features, and how might this guide feature selection?
💡 Hint: Evaluate how much information each remaining component retains.
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Reference links
Supplementary resources to enhance your learning experience.