Using Deep Learning to help consumers get the best deals from local stores
Our Solution Data Science was used to track, predict, and forecast prices based on various factors. We built an application in Ruby on Rails that lets shoppers pick the best prices for products from highly popular Australian stores, such as Coles and Woolworths. The app delivered great user experience by leveraging React for the web […]
Improve the prediction of losses due to mortgage delinquency using Machine Learning and Augmenting Customer Data
Our Solution A popular short term lending portal empowers mortgage brokers to compete with banks and large retail lenders, by providing predictive technology and tools. Its CoreLogic model predicts pre-payments and loan defaults. We did the segmentation to increase the accuracy to predict delinquency of a loan using credit bureau data, customer transaction data, and […]
Modelling and visualization of Credit Default and Interest Rate Swaps
Our Solution We extracted, translated and loaded the data from three different sources. We built custom models using Python, which was then applied to the crunched data. The resulting predictions were visualized using D3.js.
Intelligent B2B prospecting
Our Solution The system we have built has collected over 5 million company names and 8 million contacts. We continue to add 10,000 data daily and we are looking to reach 1 billion data points (which is about 30 million companies) in the next 6 months. Firm-specific data from several disparate sources is first collected. […]
Improving reporting performance by over 90X
Our Solution We reduced the average report generation time of a healthcare company to 10 seconds, this a 90X performance improvement despite reducing the number of RDS instances by 20%. To do this, we first identified the performance bottlenecks in the current system using AppDynamics. Then we re-architected the data replication strategy. We optimized encryption/decryption […]