80% of the data generated and collected today contains location information. Most of our activities using GPS systems, mobile apps and IoT devices produce geospatial data that organizations are now starting to leverage for improving their businesses. Location Intelligence (LI) is now a growing field that uses spatial information analysis and mapping tools, to derive trends and actionable insights, vital to framing better strategies in a vast amount of industries. Among other applications, Retail Site Selection can greatly benefit from modern LI implementations, with the aim of increasing sales and reducing marketing costs, by identifying the right place-to-be for any product and their target audience. In this presentation we will discuss some of the possibilities that Python offers in terms of geo-data visualization and analysis, and show how we can utilize machine learning algorithms within LI, to generate accurate predictions on real retail expansion case studies.