10 Questions on Feature Engineering in Data Science and AI Interviews

The “Data Science & AI Interview Question Series” is a valuable resource for freshers and experienced data science professionals preparing for job interviews. It provides a comprehensive guide to essential data science concepts, techniques, and practical applications. The series covers critical topics like feature engineering, feature selection, and handling missing data, along with real-world examples and expert insights. By studying this series, candidates can gain a deeper understanding of data science principles, improve their problem-solving skills, and increase their confidence in interview settings. Whether you’re a new graduate or a seasoned data scientist, this series can help you succeed in your data science career.

This document focuses on “Feature Engineering” for data science and AI interviews. It covers the definition and importance of feature engineering, common techniques (binning, one-hot encoding, etc.), and the challenges involved. It also explains feature selection, wrapper methods, and the difference between feature engineering and feature selection. Finally, it discusses handling high cardinality categorical variables and missing data, emphasizing the role of domain knowledge in feature engineering.

Questions Covered:

𝟏.   𝐖𝐡𝐚𝐭 𝐢𝐬 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞, 𝐚𝐧𝐝 𝐰𝐡𝐲 𝐢𝐬 𝐢𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭?

𝟐.   𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐬𝐨𝐦𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐮𝐬𝐞𝐝 𝐢𝐧 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠?

𝟑.   𝐖𝐡𝐚𝐭 𝐢𝐬 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐰𝐡𝐲 𝐢𝐬 𝐢𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐢𝐧 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠?

𝟒.   𝐂𝐚𝐧 𝐲𝐨𝐮 𝐞𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧?

𝟓.   𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐖𝐫𝐚𝐩𝐩𝐞𝐫 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐢𝐧 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐡𝐨𝐰 𝐝𝐨 𝐭𝐡𝐞𝐲 𝐰𝐨𝐫𝐤?

𝟔.   𝐂𝐚𝐧 𝐲𝐨𝐮 𝐞𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧?

𝟕.   𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐬𝐨𝐦𝐞 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐢𝐧 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠?

𝟖.   𝐇𝐨𝐰 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐡𝐚𝐧𝐝𝐥𝐞 𝐡𝐢𝐠𝐡 𝐜𝐚𝐫𝐝𝐢𝐧𝐚𝐥𝐢𝐭𝐲 𝐜𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐜𝐚𝐥 𝐯𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬?

𝟗.   𝐇𝐨𝐰 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐡𝐚𝐧𝐝𝐥𝐞 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐝𝐮𝐫𝐢𝐧𝐠 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠?

𝟏𝟎.   𝐖𝐡𝐚𝐭 𝐫𝐨𝐥𝐞 𝐝𝐨𝐞𝐬 𝐝𝐨𝐦𝐚𝐢𝐧 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐩𝐥𝐚𝐲 𝐢𝐧 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠?

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