𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗕𝗮𝘀𝗶𝗰𝘀

𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗕𝗮𝘀𝗶𝗰𝘀

Linear Regression is a basic tool for machine learning. It predicts a number based on your data.

You use it for these tasks:

• House price predictions.
• Sales forecasts.
• Salary estimates.

The model finds a straight line. This line fits your data points. It makes the prediction error small.

Two types exist:

• Simple Linear Regression: Uses one input.
• Multiple Linear Regression: Uses many inputs.

Why use this model?

• It is easy to read.
• It trains fast.
• It sets a baseline for other models.

Watch out for these:

• It fails with non-linear data.
• Extreme values change the results.

You track success with these metrics:

• MAE: The average error.
• MSE: The squared error.
• R2 Score: How well the model explains the data.

Start with Linear Regression. It builds your foundation for complex models like Neural Networks.

Source: https://dev.to/tarun6208/understanding-linear-regression-a-foundation-of-machine-learning-17bo Optional learning community: https://t.me/GyaanSetuAi