Update README.md

README.md

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-This project is developed in requirement for course Cloud Computing and Big Data at Columbia University. Team: Annapurna Vemuri (amv2164) Sowmya Sree Bhagavatula (sb3636) Madhuri S Palle (msp2167) Sri Lakshmi Chintala (sc3772)
+This project is developed in requirement for course Cloud Computing and Big Data at Columbia University. 
+Team: 
+Annapurna Vemuri (amv2164) 
+Sowmya Sree Bhagavatula (sb3636) 
+Madhuri S Palle (msp2167) 
+Sri Lakshmi Chintala (sc3772)
 
-ElanIndulgence is a
+Technologies Used: 
+a)Play framework for MVC and fast development that has an in built server. 
+b)PostGres database 
+c)Bootstrap css framework
 
-Project Report is available at Project Video is available at
-
-Technologies Used a)MVC Play framework for fast development that has an in built server. b)PostGres database c)Bootstrap Js framework
-
-Key Features: 1)Chat Box between customers and merchants for discussing and placing customized order. This is used for communication of the kind of design and color required. Price bargaining is also done in this chatbox and finally once the customer is convinced, he will checkout with the price agreed. 2)Recommendations are given based on highest rated products within the product category and merchant category. (To be extended to “Users who bought this also bought that using DataMining Apriori algorithm”) 3)Filtering results based on the preferences filled by the user in the preferencesm like colors preferred and kind of products required,etc. 4)News Feed for merchants. Merchants will see people who recently bought their items and any new orders placed.
+Key Features: 
+1)Chat Box between customers and merchants for discussing and placing customized order. This is used for communication of the kind of design and color required. Price bargaining is also done in this chatbox and finally once the customer is convinced, he will checkout with the price agreed. 
+2)Recommendations are given based on highest rated products within the product category and merchant category. (To be extended to “Users who bought this also bought that using DataMining Apriori algorithm”) 
+3)Filtering results based on the preferences filled by the user in the preferencesm like colors preferred and kind of products required,etc. 
+4)News Feed for merchants. Merchants will see people who recently bought their items and any new orders placed.