Brief info on Feature Engineering

Let's start with a good definition of Feature engineering

"Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data."

👉 Why is Feature Engineering Crucial?

Short Answer: Imagine constructing a magnificent building on a weak foundation - the result would be instability and fragility. Similarly, without thoughtful feature engineering, even the most advanced machine learning algorithms can stumble.

👉 The Art and Science of Feature Engineering.

Feature engineering is both an art and a science. It is a highly iterative process that requires constant evaluation and refinement. It involves a feedback loop, where insights gained from model performance guide the creation of new features or adjustments to existing ones.

Some of more well known techniques are listed below

1. One-Hot Encoding
2. Label Encoding
3. Target Encoding
4. Binning
5. Log Transformations
6. Interaction Features
7. Polynomial Features
8. Time-Based Features
9. Feature Scaling
10. Dimensionality Reduction

Take a look at below workflow !

Feature Engineering Flow


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