Polynomial Regression in Python - sklearn

Unlock the potential of polynomial regression with this hands-on tutorial using Python and Scikit-Learn. Ideal for beginners and intermediate learners, this video walks you through the process of building and implementing a polynomial regression model from scratch. You’ll learn how to set up your Python environment, preprocess your data, and use Scikit-Learn to create and train your model. The tutorial covers essential concepts like feature engineering, degree selection, and model evaluation, with practical coding examples to help you apply what you’ve learned to real-world datasets. Whether you're working on predicting trends, fitting nonlinear data, or enhancing your regression analysis skills, this video provides the tools and knowledge you need. By the end of this tutorial, you'll have a solid understanding of polynomial regression and how to leverage Python and Scikit-Learn for advanced predictive modeling. Perfect for data scientists, analysts, and anyone looking to elevate their machine learning projects.

Please feel free to download the dataset from this link:

The complete notebook is available here:

As mentioned in the video, here is the link to the simple linear regression explanation:

Please feel free to check out my Data Science blog where you will find a lot of data visualization, exploratory data analysis, statistical analysis, machine learning, natural language processing, and computer vision tutorials and projects:

Twitter page:

Facebook Page:

#polynomialregression #machinelearning #datascience #artificialintelligence #dataAnalytics #python #sklearn #jupyternotebook

[Landon_R]

You are an excellent teacher. Thank you for your videos.

[anis.ldx]

Thank you so much for your simplistic explanations!

[meshackamimo]

wonderful way to simplify a diffcult topic to beginners. keep it up!

[VolantData]

Amazing explanation! Thank you very much

[arunthashapiruthviraj]

Recommend her for beginer. well structured explanation

[దావీదురాజు-ధబ]

Is it correct to apply scaling(standard scaling in block 7, line 3) after label encoding on categorical column? for example for female: 1, male: 2

[maithreyans]

how do input new input values and predict a value for them

[farahmarsusi]

Thanks for the video. A question: is poly.fit(X_poly_train, y_train) necessary?

[ISHMAMBINROFI]

Will you please upload a tutorial for random forest?

[maheshmaskey]

Thank you for the excellent post; what about other statistics like R-squared and correlation coefficient?
Have you thought about the multivariate polynomial equation model? As you mentioned, training is overfitting but validation is very poor. Any suggestions are welcome.

[ShuaimingJing-jc]

Why we used fit_transform for trained data, but just use transform for test data?