Simple Linear Regression with sklearn

In machine learning, simple linear regression is a method used to model the relationship between a single independent variable and a dependent variable. This relationship is represented by a straight line.

sklearn is a popular machine learning library in Python that provides tools for data preprocessing, model selection, and evaluation. It also includes modules for implementing simple linear regression.

Here is an example of how to perform simple linear regression using sklearn:

from sklearn.linear_model import LinearRegression
import numpy as np

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

# Create a LinearRegression model
model = LinearRegression()

# Fit the model to the data
model.fit(X, y)

# Make predictions
predictions = model.predict(X)

After running this code, the model will be trained on the data, and predictions will be made based on the input values. You can then use these predictions to evaluate the performance of the model.

Simple linear regression is a useful tool for understanding the relationship between two variables and making predictions based on that relationship. By using sklearn, you can easily implement and test simple linear regression models in Python.