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 # VertiKin
 
-> VertiKin is an e-commerce platform which allows the user to search through various products. It has a one-of-its-kind feature of **automatically detecting what user might be interested in buying in near future**. 
+> VertiKin is an e-commerce platform that allows the user to search through an online product inventory. It is also able to **automatically detect what users might be interested in buying**. 
 
 ## How VertiKin works
 
-[VertiKin Mobile app](https://github.com/prabhakar267/vertikin/tree/master/Android) learns from user inputs on his phone (**we do not read passwords and private information, so the user can be assured of his security**). This data is sent to the [VertiKin server](https://github.com/prabhakar267/vertikin/tree/master/server), where using **Natural language processing (NLP)**, important information is extracted and using frequency as a parameter, it tries to find if the user is interested in buying some product or not. If so, a GCM push notification is sent to the user.
+[VertiKin Mobile app](https://github.com/prabhakar267/vertikin/tree/master/Android) learns from user inputs on the mobile device (**we do not read passwords and private information, so the user can be assured of his or her security**). User data is then sent to the [VertiKin server](https://github.com/prabhakar267/vertikin/tree/master/server) and analyzed with **natural language processing (NLP)**. NLP identifies key information, especially frequency, to predict potential product interests. If VertiKin identifies an interest, the server sends a GCM push notification to the user.
 
 ## VertiKin Improves itself
 
-Let's say VertiKin realised user wants to buy product A, but user did not want it, we give him a small feedback form, where he simply needs to click **No** and VertiKin would improve the suggestion from next time. User starts with  a [``DEFAULT_THRESHOLD``](https://github.com/prabhakar267/vertikin/blob/master/server/constants.py#L11) and according to the user feedback, user threshold changes by [``THRESHOLD_DELTA``](https://github.com/prabhakar267/vertikin/blob/master/server/constants.py#L13).
+If VertiKin incorrectly gauged user interest in a product, the user can offer feedback by pressing **No** on an in-app form. This feedback is then used to improve further predictions. Users start with  a [``DEFAULT_THRESHOLD``](https://github.com/prabhakar267/vertikin/blob/master/server/constants.py#L11) the [``THRESHOLD_DELTA``](https://github.com/prabhakar267/vertikin/blob/master/server/constants.py#L13) adjusts over time in response to feedback.
 
 ## Impact
 
-+ According to a [2010 study by The Nielsen Company](http://www.nielsen.com/us/en/insights/news/2010/global-online-shopping-report.html), people tend to talk more about their needs and reviews influencing other people's opinion. So we used this information to detect, **if a person tends to buy something, he tends to talk about it**. 
-+ **Cognitive fluency**  is the human tendency to prefer things that are familiar and easy to understand. According to this [article published on boston.com](http://archive.boston.com/bostonglobe/ideas/articles/2010/01/31/easy__true/?page=full), people buy stuff which is easier to understand. So using this information and the fact that after a certain point of time our primary source of knowledge becomes our peer group, we can deduce it is highly likely a person discusses a product with his friends / family.
++ According to a [2010 Nielsen Report](http://www.nielsen.com/us/en/insights/news/2010/global-online-shopping-report.html), users often discuss product purchases online. We used this to better predict future purchases. 
++ **Cognitive fluency**  is the human tendency to prefer things that are familiar and easy to understand. According to this [article published on boston.com](http://archive.boston.com/bostonglobe/ideas/articles/2010/01/31/easy__true/?page=full), users prefer easy-to-grasp products. Using this information and knowledge of peer group dynamics, VertiKin can predict that consumers are likely to discuss purchases with family and friends.
 
 ## Screenshot