Learn Data Analysis in Python

Linear Regressions

Linear regression is a statistical technique used to model the relationship between two or more variables. In the context of data analysis, linear regression is commonly used to predict a continuous variable based on one or more independent variables.

To perform a linear regression in Python, you can use the scikit-learn library, which provides a simple interface for building and fitting linear regression models. Here is an example code snippet that demonstrates how to perform a linear regression on a sample dataset:

        
            import numpy as np
            from sklearn.linear_model import LinearRegression

            # Create some sample data
            X = np.array([[1], [2], [3], [4]])
            y = np.array([2, 4, 6, 8])

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

            # Make predictions
            predictions = model.predict([[5]])

            print(predictions)
        
    

In the code above, we first create some sample data points with the numpy library. We then create an instance of the LinearRegression class from scikit-learn and fit the model using the fit() method. Finally, we can make predictions using the predict() method.

Linear regression is a powerful tool in data analysis for understanding and making predictions based on relationships between variables. By mastering linear regression in Python, you can unlock valuable insights from your data and make informed decisions based on data-driven analysis.