Regression Summary Table with sklearn in Python Template
The following Regression Summary Table with sklearn in Python template shows how to solve a multiple linear regression problem using the machine learning package sklearn. We create a summary table in the form of a dataframe which stores the features of the model, the corresponding coefficients and their p-values. Open the .ipynb file using Jupyter notebook. Some other related topics are Regression summary table with statsmodels Python, R-squared and Adjusted R-squared with sklearn Python. You can now download the Python template for free. Regression Summary Table with sklearn in Python is among the topics covered in detail in the 365 Data Science program.
Who is it for
This is an open-access Python template in .ipynb format that will be useful for anyone who wants to work as a Data Analyst, Data Scientist, Business Analyst, Statistician, Software Engineer, and anyone who works with Python.
How it can help you
Regression analysis is used whenever we are equipped with past data that we can analyze with the aim of making predictions on similar events. Linear regression, for example, is applied whenever the features in a dataset are linearly dependent with the target. That is, as the value of the feature increases, the target value increases proportionally. Once a best linear fit of the data has been found, we would like to see the parameters of this fit. This is where statsmodels' regression summary tables come very handy.