Regression Analysis with Python
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Linear models have been known to scholars and practitioners and studied by them for a long time now. Before they were adopted into data science and placed into the syllabi of numerous boot camps and in the early chapters of many practical how-to-do books, they have been a prominent and relevant element of the body of knowledge of statistics, economics, and of many other respectable quantitative fields of study.
Consequently, there is a vast availability of monographs, book chapters, and papers about linear regression, logistic regression (its classification variant), and the different types of generalized linear models; models where the original linear regression paradigm is adapted in its formulation in order to solve more complex problems. Yet, in spite of such an embarrassment of riches, we have never encountered any book that really explains the speed and ease of implementation of such linear models when, as a developer or a data scientist, you have to quickly create an application or API whose response cannot be defined programmatically but it does have to learn from data.
Of course we are very well aware of the limitations of linear models (being simple unfortunately has some drawbacks) and we also know how there is no fixed solution for any data science problem; however, our experience in the field has told us that the following advantages of a linear model cannot be easily ignored:
- It's easy to explain how it works to yourself, to the management, or to anyone
- It's flexible in respect of your data problem, since it can handle numeric and probability estimates, ranking, and classification up to a large number of classes
- It's fast to train, no matter what the amount of data you have to process
- It's fast and easy to implement in any production environment
- It's scalable to real-time response toward users
If for you, as it is daily for us, it is paramount to deliver value from data in a fast and tangible way, just follow us and discover how far linear model can help you get to.