Practice - Dataset Selection and Initial Preparation
Practice Questions
Test your understanding with targeted questions
What is an imbalanced dataset?
💡 Hint: Consider the context of fraud detection.
What does imputation mean?
💡 Hint: Think about how you would handle gaps in your data.
3 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the goal of imputation in data preprocessing?
💡 Hint: Consider what happens when data entries are incomplete.
True or False: One-Hot Encoding is used to convert numerical features into categorical ones.
💡 Hint: Think about the direction of the conversion.
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Challenge Problems
Push your limits with advanced challenges
Consider a binary classification task where one class is significantly rarer than the other. How would you prepare your dataset and why?
💡 Hint: Highlighting how preprocessing aids in model generalization.
You are given a dataset with a high number of missing values in certain features. Provide a comprehensive strategy for addressing these issues.
💡 Hint: Focus on maintaining data integrity while minimizing information loss.
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Reference links
Supplementary resources to enhance your learning experience.
- Credit Card Fraud Detection Dataset
- Data Preprocessing in Machine Learning
- Missing Data Treatment in Machine Learning
- Understanding One-Hot Encoding
- Feature Scaling Techniques
- Train-Test Split in Machine Learning
- Imputation Techniques for Missing Values
- Preprocessing Data for Machine Learning Algorithms