⚡ Linear Regression with sklearn

These are Luis's notes on how to implement a linear regression model using the scikit-learn library. This demonstration will help you understand how scikit-learn simplifies the process of training, evaluating, and using linear regression models.

The interactive notebook includes:

• Preparation and visualization of synthetic data.
• Implementation of a linear regression model with scikit-learn.
• Splitting the data into training and test sets.
• Model training and evaluation.
• Visualization of data and the regression line.
• Example of prediction for new data.

Want to modify the code and experiment on your own?
Open the notebook in Google Colab

Bibliography

• scikit-learn API Reference – LinearRegression: Estimator parameters, attributes, and practical notes. https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
• scikit-learn User Guide – Train/Test Split: Best practices for train_test_split and validation strategies. https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation
• scikit-learn Metrics – Regression: Definitions of R², MAE, and other metrics used in the example. https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics
• Pandas Documentation – DataFrame: Data preparation utilities referenced in the notebook. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html