The OLS Assumptions in Python – No Multicollinearity
The OLS Assumptions in Python – No Multicollinearity shows how to detect possible collinearity between several data set features and deal with them. In this example, we investigate the possible collinearity between several car features and remove the unnecessary ones. Some other topics you might be interested in exploring are OLS Assumptions in Python - No Multicollinearity, Linear Regression Model in Python – Residuals. You can now download the Python template for free. The OLS Assumptions in Python - No Multicollinearity 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 use linear regression by satisfying the no multicollinearity OLS Assumptions.
How it can help you
Linear regression is a very handy tool for modeling data . One of the OLS assumptions that needs to be respected whenever performing linear regression is the abesnce of multicollinearity. This template aims to show how we can detect correlated features and how to deal with them.