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Aman's Projects

bazaardroid icon bazaardroid

We aim to provide a framework of interactive user application in Android with its real time implementation in demo version of an virtual BigBazaar . The project does so by using Google’s C2DM service (Cloud to Device Messaging it is available in Android 2.2 & above), Application server (Authenticated by Google server with unique Auth ID) maintained by the shopping mall manager and application installed on device. The application registers itself to get updates from application server at C2DM server. Using our application, the user can browse floor plans, run various searches, get live updates from retailers, rate the product, get offers and other important data. The beauty to the application is added by using data mining to show user the events or products that user would like to checkout. Currently, few applications have been made in this context like ShopDroid, Shop Savvy, etc. When the application is installed Service starts in the device which receives the pushed notifications from application server. The application uses pushing concept for sending the notification hence battery life and the performance of the device is not affected (or degraded).On receiving notification the device can send request for the new data from application server. In our implemented design, the entire database comes with application (SQLite) and runtime information, the registration ID (that is generated by C2DM server) of application are stored on application server. The design addresses the problems in mall management of communicating updates and to the target audience, provide very interactive and easy to use application.

data_project_structure icon data_project_structure

We will learn about how to structure the data projects, for productionizing your Data Science & ML work. This is Part 1

machine-learning-click-through-rate-project icon machine-learning-click-through-rate-project

For this project, we were tasked with predicting whether a customer would click on a given ad. Looking at the original data, we had 32 million instances consisting of 24 anonimized variables designating various categories.

managingbigdata icon managingbigdata

For this project we used AWS eco-system to manage the Amazon reviews data(~50 GB) and do sentiment analysis on the reviews.

opencv icon opencv

Open Source Computer Vision Library

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