Linear Regression with scikit-learn

Linear regression is a fundamental statistical and machine learning technique that can be used to analyze and make predictions based on a continuous variable. In this article, we will explore how to perform linear regression using the popular Python library scikit-learn.

What is Linear Regression?

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The goal of linear regression is to find the best-fitting line that describes the relationship between the variables, which can then be used to make predictions.

Performing Linear Regression with scikit-learn

Scikit-learn is a powerful machine learning library for Python that provides tools for building and evaluating machine learning models. Performing linear regression with scikit-learn is relatively straightforward.

1. Import the necessary libraries

To use scikit-learn for linear regression, you will need to import the necessary libraries:

		
			from sklearn.linear_model import LinearRegression
			import numpy as np
		
	

2. Prepare the data

Next, you will need to prepare your data by separating the independent and dependent variables:

		
			X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
			y = np.array([2, 4, 6, 8, 10])
		
	

3. Create and fit the model

Now, you can create a LinearRegression object and fit the model to your data:

		
			model = LinearRegression()
			model.fit(X, y)
		
	

4. Make predictions

Once the model has been trained, you can use it to make predictions on new data:

		
			X_new = np.array([6]).reshape(-1, 1)
			prediction = model.predict(X_new)
			print(prediction)
		
	

Conclusion

Linear regression is a powerful and widely-used technique for modeling and making predictions based on continuous variables. By using scikit-learn, you can easily perform linear regression in Python and leverage its robust functionality for the task.