Sharpen your skills in handling missing data! This quiz explores common imputation techniques like mean, median, and mode substitution, as well as scenarios where dropping data is the better choice. Learn when to apply each method, their impact on data distribution, and how they affect machine learning model performance. Ideal for beginners and aspiring data scientists.
Test your knowledge of using hash maps and sets in preprocessing for categorical encoding, frequency counting, deduplication, and handling missing or unknown categories. This quiz covers foundational concepts and practical scenarios relevant to efficient data preparation using hash-based structures.
Test your foundational knowledge of SQL joins, group-by aggregations, and per-entity statistics to enhance feature engineering in relational data. This quiz covers key concepts like join types, aggregate functions, and techniques for generating features such as entity counts and group-wise summaries.
Test your knowledge of data preprocessing essentials! This quiz covers strategies for handling missing values, detecting and resolving inconsistencies, imputation techniques, and ensuring data quality for machine learning models. Perfect for aspiring data scientists and analysts looking to sharpen their data cleaning skills.
Sharpen your skills in feature engineering with this quiz! Explore key techniques for encoding categorical variables, including one-hot encoding, label encoding, target encoding, and handling high-cardinality features. Ideal for data scientists and ML enthusiasts aiming to improve model accuracy and preprocessing workflows.