Feature Engineering
This section covers feature engineering techniques and best practices.
Topics Covered
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Prerequisites
- Basic Python and pandas
- Understanding of ML fundamentals
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Feature Engineering
Tutorial
Jun 1, 20264 min read
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Handling Missing Values
Handling Missing Values: MCAR, MAR, MNAR and Imputation Techniques Missing values are one of the most common reasons models underperform — not because the algor…
Tutorial
Jun 1, 20263 min read
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Handling Imbalanced Datasets
Handling Imbalanced Datasets: Upsampling and Downsampling A model that achieves 99% accuracy but never predicts the minority class isn't accurate — it's broken.…
Tutorial
Jun 1, 20263 min read
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SMOTE
SMOTE: Synthetic Minority Oversampling for Imbalanced Data Basic upsampling has a known flaw: it duplicates minority samples. If you have 100 fraud cases and up…
Tutorial
Jun 1, 20264 min read
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Handling Outliers
Handling Outliers: Detection and Treatment with IQR A single extreme value can quietly distort your entire analysis. The mean of a column shifts, variance infla…
Tutorial
Jun 1, 20263 min read
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Categorical Encoding
Categorical Encoding: One-Hot, Label, Ordinal, and Alternatives Most machine learning models expect numeric input. If your data has columns like "color", "count…