Random Walk Analysis with Linear Regression

This function generates a random walk using numpy, converts it to a DataFrame using pandas, and then fits a linear regression model from sklearn to the random walk. It returns the coefficient of the model, which can be used to evaluate the trend of the random walk.

Technology Stack : numpy, pandas, sklearn

Code Type : Custom function

Code Difficulty : Intermediate

                
                    
        def random_walk(n_steps):
            import numpy as np
            import pandas as pd
            from sklearn.linear_model import LinearRegression

            # Create a random walk using numpy
            random_walk = np.random.randn(n_steps)

            # Create a DataFrame from the random walk
            df = pd.DataFrame(random_walk, columns=['Steps'])

            # Fit a linear regression model to the random walk
            model = LinearRegression().fit(df, df)

            # Return the model's coefficients
            return model.coef_[0]