Welcome to another Python tutorial where we will learn how to create a polynomial regression line using the scikit-learn library. Polynomial regression is a type of linear regression that enables us to model non-linear relationships between variables.
In this tutorial, we will cover the following steps:
- Introducing polynomial regression and its non-linearity.
- Understanding the degree parameter that defines the non-linearity of the regression line.
- Setting up the environment in Google Colab to work on the tutorial.
- Importing necessary libraries like scikit-learn and matplotlib.
- Creating a sales dataset in MS Excel, generating random data, and converting it into Python arrays.
- Visualizing the original sales data over thirty days using matplotlib.
- Transforming the features using PolynomialFeatures to introduce non-linearity.
- Visualizing the transformed features and how they create a non-linear line.
- Training the linear regression model with the transformed data.
- Predicting the non-linear regression line based on the trained model.
- Customizing the chart using different colors for dots and lines.
- Exploring the impact of changing the degree parameter on the regression line.
Colab Notebook: https://colab.research.google.com/drive/178bLanYnwxGRqCwLf0YHONBiGtGBJ1Vr?usp=sharing
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