Decision Science in Action

I joined   CoreCompete   on  Feb 18, 2013 Monday. It has been a great learning and exciting experience. When I walked into Banjara Hills, Hyderabad office of Core Compete (now shifted to Gachibowli, 3rd office) only thought in my mind was did I take wise decision by choosing Core Compete over a global giant.  Today, I can say that it was the best professional decision I have taken. I feel proud and amazed when I think about our growth from 4 to 200+ in period of 6 years in the niche business of advance analytics and big data technology and recognized as fastest growing consulting organisation in North America ( http://www.consultingmag.com/fastest-growing-firms-2016/   ) for last three consecutive years. Yes, we are growing fast, but that’s not the only reason that I love working at Core Compete.  In last 6 years I worked for multiple clients across multiple domain using different advance analytical techniques and technology platform.I got opportun...

The objective of this article is to propose a design of a personalized recommendations application for restaurants owners which can be used by the customers of the given credit/debit card issuer. The developed recommendation application can be installed as mobile application in the mobile phone of the customers. The recommendation engine will use machine learning algorithms and predictive analytics techniques to derive information from  the credit transaction data, mobile location data, social media data (Facebook, Twitter), public data (Yelp.com/Bundle.com etc.), mobile browser data and data of the restaurants*. The recommendation engine will have two modules. First module will use the clustering algorithm to derive actionable information from credit card data and restaurants data. It will also use the data mining techniques to integrate the useful information available in the web space with restaurant data (explained later). This module will execute in server of the bank...