OLS Assumptions in Python - Linearity Template
The OLS Assumptions in Python - Linearity shows how to transform non-linear dependencies into linear. In this example, we check the dependencies between the price of a car with respect to the year of manufacturing, its price and its mileage. Some other related topics you might be interested are OLS assumptions in Python – Linearity and Linear regression model in Python - residuals. You can now download the Python template for free. The OLS Assumptions in Python - Linearity template is among the topics covered in detail in the 365 Data Science program.
Who is it for
This is an open-access Python template that is going to be very useful for Data Analysts, Data Scientists, Machine Learning Engineers and anyone who is interested in learning how to model data in Python using linear regression.
How it can help you
Linear regression is a very handy tool for modeling data. We should, however, always use it with caution. Before deciding to employ this approach, it is worth exploring the conditions under which linear regression is a good algorithm to use. These are the OLS asasumptions. One of them is that the features are assumed to bear a linear dependence with the target. Whenever that is not the case, the data can be transformed such that this assumption is satisfied.