Linear and Polynomial Regression using Scikit-learn [Part 12] | Machine Learning for Beginners

Welcome to Part 12 of our Machine Learning for Beginners series. In this article, we will be discussing linear and polynomial regression using the Scikit-learn library in Python.

Introduction to Regression

Regression analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It is often used to make predictions and understand the underlying patterns in the data.

Linear Regression

Linear regression is a simple and widely used statistical technique for predictive modeling. It assumes a linear relationship between the dependent variable and the independent variables. The goal of linear regression is to find the best-fitting line that minimizes the sum of the squared differences between the observed and predicted values.

Polynomial Regression

Polynomial regression is a form of linear regression in which the relationship between the independent and dependent variables is modeled as an nth degree polynomial. This allows for more complex and flexible modeling of the data compared to simple linear regression.

Using Scikit-learn for Regression

Scikit-learn is a popular machine learning library in Python that provides a wide range of tools for building and evaluating predictive models. It includes implementations of both linear and polynomial regression models, making it easy to apply these techniques to real-world datasets.

Code Example

Below is an example of how to use Scikit-learn to fit a linear regression model to a sample dataset:

      
        import numpy as np
        from sklearn.linear_model import LinearRegression

        # Create sample data
        X = np.array([[1], [2], [3]])
        y = np.array([2, 4, 6])

        # Fit the model
        model = LinearRegression()
        model.fit(X, y)

        # Make predictions
        predictions = model.predict([[4]])
      
    

Conclusion

Linear and polynomial regression are powerful techniques for modeling the relationships between variables in a dataset. By using Scikit-learn, we can easily apply these techniques to real-world problems and make informed predictions.

Stay tuned for the next part of our Machine Learning for Beginners series, where we will explore more advanced regression techniques.