Python: Creating Polynomial Regression Line, using sklearn Library in Google Colab
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.
We hope you find this tutorial helpful in understanding polynomial regression and its implementation in Python. Don't forget to like the video and subscribe to our channel for more tutorials and updates. Happy coding!
Colab Notebook: https://colab.research.google.com/dri...
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Chapters:
00:14 - Introduction to Polynomial Regression in sklearn
00:45 - Creating a new Python Notebook in Google Colab
01:25 - Importing the necessary libraries
02:02 - Creating a Sales Dataset in MS Excel
05:15 - Creating Polynomial Features and Plotting a Chart
06:30 - Train and Predict the Polynomial Regression Line
07:55 - Visualizing the Predicted Data using Pyplot Library
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