This tutorial will provide a hands-on overview of a business strategy project that applies machine learning with a focus on the Python code and common packages, followed by business interpretation of model results and findings.
The presented Python code and process can serve as a useful starting template for typical Data Science projects.
As Data Science matures across all companies, there is still a growing opportunity to incorporate advanced machine learning methods inside and by the business functions themselves. Despite massive investments hiring the best data scientists and even more on consulting services, many large companies are not getting the value returns that they expected.
For data-driven projects to create value, the business must ask smart questions, wrangle the relevant data, and uncover fresh insights – this is what classically trained data scientists do well. Understanding what the insights mean for the business and communicating those insights effectively comes from experience, therefore, it is crucial for existing team members across all functions within the organization to explore machine learning methods bravely.
As data-driven culture becomes the default in successful organizations, both executive and business staff will learn to conduct ML experiments by leveraging fundamental statistical knowledge with the usual ML and new AutoML tools.