Practice - Prepare Data for Classification
Practice Questions
Test your understanding with targeted questions
What is feature scaling and why is it important?
💡 Hint: Think about distance calculations in algorithms like KNN.
Describe one method for handling missing values in a dataset.
💡 Hint: Consider how we can keep data instead of losing it.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is the main goal of data preprocessing?
💡 Hint: Think about how preprocessing helps in building effective models.
Feature scaling is crucial for which type of machine learning methods?
💡 Hint: Consider algorithms that rely on measuring distances.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
You have a dataset for predicting customer churn with 2000 records where 5% are churners. How would you prepare this dataset for a classification model?
💡 Hint: Consider how best to maintain class proportions while preparing the data for modeling.
Given a dataset containing continuous variables, discuss how you would choose between standardization and min-max scaling based on the distribution of your features.
💡 Hint: Visualize or describe your data's distribution to inform your scaling choice.
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Reference links
Supplementary resources to enhance your learning experience.