✅ Feature Engineering & Selection

When building ML models, good features can make or break performance. Here's a quick guide:

1️⃣ Feature Engineering – Creating new, meaningful features from raw data
⦁ Examples:
⦁ Extracting day/month from a timestamp
⦁ Combining address fields into region
⦁ Calculating ratios (e.g., clicks/impressions)
⦁ Helps models learn better patterns & improve accuracy

2️⃣ Feature Selection – Choosing the most relevant features to keep
⦁ Why?
⦁ Reduce noise & overfitting
⦁ Improve model speed & interpretability
⦁ Methods:
⦁ Filter (correlation, chi-square)
⦁ Wrapper (recursive feature elimination)
⦁ Embedded (Lasso, tree-based importance)

3️⃣ Tips:
⦁ Always start with domain knowledge
⦁ Visualize feature importance
⦁ Test model performance with/without features

💡 Better features give better models!