Introduction to Linear Regression in Python

This project is a simple and beginner-friendly notebook that explains linear regression using Python. It covers the basics, provides code examples, and visualizes results to help anyone understand how linear regression works.

What is Linear Regression?

Linear regression is a method to find the best-fitting straight line through data points. It helps predict one value from another and is widely used in statistics and machine learning.

Project Structure

• Linear regression.ipynb: The main notebook with explanations, code, and plots.

Topics Covered

• What is linear regression?
• The equation of a line ($y = mx + c$)
• Assumptions of linear regression
• Visualizing data
• Fitting a line to data
• Evaluating the fit (R² score)
• Interpreting results
• Residuals and their plot
• Limitations of linear regression
• References for further learning

How to Use

1. Open the notebook in Jupyter or VS Code.
2. Run the cells step by step to see explanations and outputs.
3. Try changing the example data to see how the results change.

Requirements

• Python 3
• numpy
• matplotlib
• scikit-learn

Install required packages with:

pip install numpy matplotlib scikit-learn

References

• Simple Linear Regression - Wikipedia
• Scikit-learn Linear Regression Documentation

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Feel free to use, modify, or share this notebook to help others learn linear regression!