?How do you handle missing data in a dataset, and why is it important

Handling missing data is crucial to ensure the reliability of a model. Common techniques include: Removing Rows/Columns: If the missing data is minimal and won't impact results significantly. Imputation: Using mean, median, or mode for numerical data; or frequent categories for categorical data. Advanced Methods: Such as using predictive modeling (e.g., regression or KNN) to fill gaps. These steps prevent biased results and maintain the dataset's integrity for analysis.

?How do you explain a machine learning model to a non-technical stakeholder

Focus on simplicity and outcomes. Explain the Purpose: Highlight what the model does (e.g., predicting customer churn). Simplify the Methodology: Use relatable analogies (e.g., "It's like a teacher learning from past tests to predict future answers"). Emphasize Results: Share clear metrics or visualizations (e.g., "The model predicts churn with 85% accuracy"). This approach bridges technical details and business understanding.

Data science mock interview basics

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Data Science Interview Mock Data Science Interview question for freshers

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Data Science Interview Mock Data Science Interview Questions Data Science Interview Experience

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Data Science Mock Interview with Mani Data Science Interview question and answer

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Data science mock interview basics

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