GitBook: [#2896] update

a.i.-exploiting/bra.i.nsmasher-presentation/ml-basics/feature-engineering.md

@@ -273,3 +273,20 @@ dataset.iloc[10:20] # Get some indexes that contained empty data before
 ```
 
 To fill categorical data first of all you need to think if there is any reason why the values are missing. If it's by **choice of the users** (they didn't want to give the data) maybe yo can **create a new category** indicating that. If it's because of human error you can **remove the rows** or the **feature** (check the steps mentioned before) or **fill it with the mode, the most used category** (not recommended).
+
+## Combining Features
+
+If you find **two features** that are **correlated** between them, usually you should **drop** one of them (the one that is less correlated with the target), but you could also try to **combine them and create a new feature**.
+
+```python
+# Create a new feautr combining feature1 and feature2
+dataset['new_feature'] = dataset.column1/dataset.column2
+
+# Check correlation with target column
+dataset[['new_feature', 'column1', 'column2', 'target']].corr()['target'][:]
+
+# Check for collinearity of the 2 features and the new one
+X = add_constant(dataset[['column1', 'column2', 'target']])
+# Calculate VIF
+pd.Series([variance_inflation_factor(X.values, i) for i in range(X.shape[1])], index=X.columns)
+```