Polynomial Regression in Python (Step-by-Step) | Scikit-Learn Machine Learning Tutorial
Code & Data File:
https://github.com/hakeemrehman/Polyn...
Polynomial Regression is a supervised machine learning algorithm used to model non-linear relationships between variables. In this tutorial, you will learn how Polynomial Regression works and how to implement it in Python using the Scikit-Learn library.
This video explains how higher-degree polynomial features allow linear models to capture complex patterns in data. Using a practical dataset example, we build a polynomial regression model, evaluate its performance, and compare metrics such as Mean Squared Error (MSE) and R-squared.
📊 What You Will Learn
What Polynomial Regression is
Difference between Linear and Polynomial Regression
How polynomial features work
Selecting the best polynomial degree
Avoiding underfitting and overfitting
Python implementation using Scikit-Learn
Model evaluation using MSE and R²
💻 Tools Used
Python
Scikit-Learn
Pandas
NumPy
Matplotlib
🎯 Example Covered
Inventory level vs cost prediction using polynomial regression.
This tutorial is ideal for students, data analysts, and machine learning beginners who want a clear understanding of polynomial regression with a real example.
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What is Polynomial Regression?
Polynomial Regression is a machine learning algorithm that models nonlinear relationships by transforming input features into higher-degree polynomial terms while keeping the model linear in parameters.
When should Polynomial Regression be used?
Polynomial Regression is used when the relationship between independent and dependent variables is curved rather than linear.
What is the difference between Linear and Polynomial Regression?
Linear Regression fits a straight line, while Polynomial Regression fits curved relationships by adding squared or higher-degree features.
How do you implement Polynomial Regression in Python?
Polynomial Regression can be implemented using PolynomialFeatures from sklearn.preprocessing and LinearRegression from sklearn.linear_model.
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