# 2: Regression

**Date: 25th October 2023**

💡 **Linear regression** in simple terms is finding the **line of best fit** given a set of data points that loosely follow a linear pattern. One way of doing this is by **minimising the loss** (difference between actual and computed value), making it useful for **predicting numerical outcomes** based on input features. **Polynomial regression** extends linear regression by allowing the model to capture **nonlinear relationships** between variables, using polynomial functions to fit the data more flexibly. 💡

You can access our **demonstration notebook** here: 📘 **Tutorial 2 Notebook**

The solution is available in the same folder.

You can access our **slides** here: 💻 **Tutorial 2 Slides**

The **recording** from this session is available here: 🎤 **Tutorial 2 Recording**