Exponential fit with the least squares Python

Exponential fitting is a common task in data analysis when dealing with exponential growth or decay. The least squares method is often used to find the parameters of the exponential curve that best fit the given data.
Make sure you have the required libraries installed. You can install them using:
Let's create a simple Python script to demonstrate the process of fitting an exponential curve using the least squares method.
exponential_function: This is the function we want to fit to our data. It represents the general form of an exponential curve, where a, b, and c are the parameters to be determined.
params, covariance: The curve_fit function returns two values - params contains the optimized parameters of the fit, and covariance is the estimated covariance of the parameters.
The script generates sample data (x_data and y_noisy), fits an exponential curve to it, and plots the original noisy data, the true underlying curve, and the fitted curve.
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