How can I preprocess my data using scikit-learn?

How can I preprocess my data using scikit-learn?

You can use scikit-learn's StandardScaler for normalization, OneHotEncoder for categorical variables, and SimpleImputer for handling missing values.

Can you show me an example of using StandardScaler?

Sure! Here's a simple example:

from sklearn.preprocessing import StandardScaler
import numpy as np

# Sample data
X = np.array([[1, 2], [3, 4], [5, 6]])

# Initialize the scaler
scaler = StandardScaler()

# Fit and transform the data
X_scaled = scaler.fit_transform(X)
print(X_scaled)

What if I have categorical data?

For categorical data, you can use OneHotEncoder. It converts categorical values into a format that can be provided to ML algorithms.