📘 Linear Regression from Scratch – Colab Notebook

A simple implementation of Linear Regression using just NumPy, written in Python and hosted on Google Colab ☁️.
Perfect for understanding the basics of machine learning and how linear models work under the hood.

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📌 What's Inside?

• 📊 Pure Python + NumPy implementation
• 🧠 Gradient Descent-based training
• 📉 Visualization of predictions
• 💡 Great for learning ML fundamentals

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🧮 The Math

We aim to fit a linear model:

ŷ = w * x + b

Where:

• ŷ is the predicted output
• w is the weight (slope)
• b is the bias (intercept)

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🎯 Loss Function (Mean Squared Error)

J(w, b) = (1/n) * Σ (ŷᵢ - yᵢ)²

Where:

• n is the number of data points
• ŷᵢ is the predicted value
• yᵢ is the actual value

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🔁 Gradient Descent Updates

w = w - α * ∂J/∂w b = b - α * ∂J/∂b

Where α is the learning rate.

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▶️ Try it on Colab

Click below to open the notebook in Colab:

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🧑‍💻 Author

Made with ❤️ by CraftyEngineer

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📜 License

This project is open source and available under the MIT License.