Add categorical variables section to data pre-processing in machine l…

…earning course

my-brain/machine-learning-and-data-science-course/data-pre-processing/categorical-variables.md

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+---
+id: "categorical-variables"
+title: "Categorical Variables"
+---
+
+To use some variables that are categorical we need to transform them into numerical values. This process is known as encoding or feature encoding.
+
+### Label Encoding
+
+Label encoding is a technique used to convert categorical variables into numerical values. It assigns a unique integer to each category in the variable.
+
+```python
+from sklearn.preprocessing import LabelEncoder
+
+# Create a label encoder object
+label_encoder = LabelEncoder()
+
+# Fit the encoder to the data
+data['category'] = label_encoder.fit_transform(data['category'])
+```
+
+### One-Hot Encoding
+
+This technique is used to convert categorical variables into binary vectors. It creates a new binary column for each category in the variable.
+
+```python
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.compose import ColumnTransformer
+
+# Create a column transformer object
+column_transformer = ColumnTransformer(
+    transformers=[
+        ('encoder', OneHotEncoder(), ['category', 'category2'])
+    ],
+    remainder='passthrough' # Keep the remaining columns
+)
+
+# Fit the transformer to the data
+data = column_transformer.fit_transform(data)
+```