Linear regression

import numpy as np

def linear_regression_from_scratch(x, y):
    """
    Simple linear regression using least squares.

    Args:
    - x: numpy array or a list of independent variable.
    - y: numpy array or a list of dependent variable.

    Returns:
    - coefficients: Tuple containing slope and intercept for the best fit line.
    """
    # Convert lists to numpy arrays if necessary
    x = np.array(x)
    y = np.array(y)

    # Calculating the mean of x and y
    mean_x = np.mean(x)
    mean_y = np.mean(y)

    # Total number of values
    n = len(x)

    # Using the formula to calculate 'm' and 'c'
    numerator = 0
    denominator = 0
    for i in range(n):
        numerator += (x[i] - mean_x) * (y[i] - mean_y)
        denominator += (x[i] - mean_x) ** 2

    m = numerator / denominator  # Slope of the line
    c = mean_y - (m * mean_x)  # Intercept of the line

    return (m, c)

# Example usage
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

slope, intercept = linear_regression_from_scratch(x, y)
print(f"Slope: {slope}, Intercept: {intercept}")

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Last update: March 21, 2024