Unsupervised Clustering for Optimal Locality Detection

Data science is the most sought over domain in today's world and has been known for its accurate decision-making capabilities, delivering recommendations that have the best profits and much more. The demand for this analysis is the growing technology and population that opens a new dimension of demands leading to the world crisis in every sector. Clustering is the part that helps in making these decisions more accurate and has been evolving through time. Impacts of neighborhoods and localities for businesses are often marked by many factors. To understand the factors and outline them to the proper perspective, through this research the authors performed perspective data cleaning, wrangling, visualization to understand the factors and cluster them for a much prospective decision-making process.