The project synopsis deals with the technique of Data-Analytics which is becoming a very influential tool for decision-making today. Software data analytics is key for helping stakeholders make decisions, and thus establishing a measurement and data analysis program is a recognized best practice within the software industry. However, practical implementation of measurement programs and analytics in industry is challenging. In this chapter, we discuss real-world challenges that arise during the implementation of a software measurement and analytics program. We also report lessons learned for overcoming these challenges and best practices for practical, effective data analysis in industry. The main objective of this work is to understand data for decision making. The author here tries to select those locations that are not already crowded with restaurants within the region and have a greater population by using various data science and analysis techniques to reach their goal of selecting optimal locations. The advantages of each area will be clearly expressed so that the best possible final location can be chosen by the stakeholders. The Author initialized a crawler to scrape the data about the areas of cities using Wikipedia web page and the real-time data set. Python’s geocoder library with ArcGIS as a geocode provider, to get the coordinates of the neighborhoods. After which the author applies the clustering algorithm, K-Means, on the data to cluster the neighborhood based on general venue density and analyze & compare the sets in each cluster to conclude the most promising and optimal locations for each restaurant type. Which is then filter out only the target restaurant of interest in each neighborhood, to analyze within the clusters.