Unit 2 | Ep 02: The Null Hypothesis – Handling Missing Data
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Unit 2 | Ep 02: The Null Hypothesis – Handling Missing Data
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Welcome to Mindforge ML. In this episode, we tackle the most common enemy of data science: missing values.
Real-world data is rarely perfect. Sensors fail, forms get skipped, and files get corrupted. Simply deleting these gaps can ruin your model, but filling them incorrectly introduces bias. We explore the art of data imputation and the strategy behind "saving" your dataset.
Key topics:
The Root Cause: Understanding MCAR, MAR, and MNAR missing data patterns.
Deletion vs. Imputation: When to drop rows vs. when to fill them in.
Strategies: Mean/Median substitution, KNN imputation, and time-series filling.
Impact: How your choice of handling directly alters model predictions.
Learn to fix the gaps without breaking the truth.
Series: Mindforge ML | Unit 2Produced by: Chatake Innoworks Pvt. Ltd.Initiative: MindforgeAI
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