Data Regression with Python
Python Data Regression
Correlations from data are obtained by adjusting parameters of a model to best fit the measured outcomes. The analysis may include statistics, data visualization, or other calculations to synthesize the information into relevant and actionable information. This tutorial demonstrates how to create a linear, polynomial, or nonlinear functions that best approximate the data and analyze the result. Script files of the Python source code with sample data are available below.
Linear and Polynomial Regression
Regression with Python (GEKKO or Scipy)
While this exercise demonstrates only one independent parameter and one dependent variable, any number of independent or dependent terms can be included. See Energy Price regression with three independent variables as an example.
Regression with APM Python
Excel and MATLAB
This regression tutorial can also be completed with Excel and Matlab. A multivariate nonlinear regression case with multiple factors is available with example data for energy prices in Python. Click on the appropriate link for additional information.
There is additional information on regression in the Data Science online course.