Introduction to Scikit-learn

Scikit-learn (sklearn) is the most widely-used machine learning library in Python. It provides simple, efficient tools for data analysis and modeling.

Why Scikit-learn?

The Sklearn Pattern

Every model in sklearn follows the same pattern - learn this once, use it everywhere.

Common Sklearn Models

Train/Test Split

Never evaluate on training data! Use sklearn's train_test_split.

Complete ML Workflow Example

Preprocessing with Sklearn

Sklearn provides tools for data preprocessing too.

Model Selection Helpers

Sklearn Quick Reference

Key Takeaways

• Sklearn provides a consistent API: fit(), predict(), score()
• Always split your data into train and test sets
• Use preprocessing tools to scale features
• The pattern is the same for all models:
  1. Import the model
  2. Create an instance
  3. Fit on training data
  4. Predict on test data
  5. Evaluate performance

You now have the foundational tools for ML! Next, we'll apply them to build real models, starting with Linear Regression.