Exponential Regression with NumPy

NumPy provides essential tools for implementing exponential regression models from scratch. We’ll explore the key concepts of exponential regression and demonstrate how to perform exponential regression using NumPy.

Understanding Exponential Regression

Exponential regression aims to find a relationship between a dependent variable (Y) and an independent variable (X) that can be expressed as an exponential equation:

See also  How to calculate square root in Numpy?

Y = β0 * e1 * X) + ε

Where:

  • Y is the dependent variable (the variable we want to predict).
  • X is the independent variable (usually time).
  • β0 is the coefficient representing the initial value of Y.
  • β1 is the coefficient representing the growth or decay rate.
  • e is the base of the natural logarithm (approximately 2.71828).
  • ε represents the error term (the difference between the predicted and actual values).
See also  How to add two arrays in Numpy?

Performing Exponential Regression with NumPy

To perform exponential regression using NumPy, follow these steps:

  1. Import NumPy:
  2. import numpy as np
  3. Define your data: Prepare your dataset with the dependent variable (Y) and independent variable (X).
  4. X = np.array([1, 2, 3, 4, 5])
    Y = np.array([5, 20, 45, 80, 125])
    
  5. Calculate the coefficients: Use NumPy functions to calculate the coefficients β0 and β1.
  6. log_Y = np.log(Y)
    coefficients = np.polyfit(X, log_Y, 1)
    beta_1 = coefficients[0]
    beta_0 = np.exp(coefficients[1])
    
  7. Make predictions: Use the calculated coefficients to make predictions.
  8. Y_pred = beta_0 * np.exp(beta_1 * X)