Hands-On Linear Regression with Scikit-Learn in Python (Beginner Friendly)

If you are new to machine learning and want to learn linear regression using the popular Scikit-Learn library in Python, you have come to the right place. In this article, we will walk you through a beginner-friendly hands-on tutorial for implementing linear regression using Scikit-Learn.

Getting Started

Before we dive into the code, make sure you have Python and Scikit-Learn installed on your computer. If not, you can easily install them using pip, the Python package manager.


$ pip install python
$ pip install scikit-learn


Implementing Linear Regression

Now that you have Python and Scikit-Learn installed, let’s implement a simple linear regression model. We will use a sample dataset to demonstrate the process.

      import numpy as np
      from sklearn.linear_model import LinearRegression

      # Sample dataset
      X = np.array([[1], [2], [3], [4], [5]])
      y = np.array([2, 4, 6, 8, 10])

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

      # Make predictions
      X_new = np.array([[6], [7]])
      predictions = model.predict(X_new)
      print(predictions)
    

Understanding the Code

In the above code, we first import the necessary libraries and create a sample dataset with input features (X) and target values (y). We then create a linear regression model using the LinearRegression class from Scikit-Learn and fit it to the dataset using the fit method. Finally, we make predictions on new data using the predict method.

Conclusion

Congratulations! You have now successfully implemented a linear regression model using Scikit-Learn in Python. This is just the beginning of your journey into machine learning, and we hope this hands-on tutorial has helped you understand the basic concepts of linear regression. Keep practicing and exploring different machine learning models to deepen your understanding and skills.