Web App for Microsoft Data Science Challenge 2016
As part of Microsoft Azure Data Science Challenge 2016 (a 24-hour Machine Learning hackathon), my team and I decided to create a predictive model to predict the probabiliity of a property in Singapore being sold en-bloc (in Singapore, the practice of an entire property block being sold at the same time to the Government). We first trained a predictive model using algorithms such as Gradient-Boosted Trees and Ensemble, followed which we created a web service API endpoint using Microsoft Azure. Finally, we created a webapp using Google Maps to allow any user to enter the postal code of a property and by querying our model, find the probability of it being sold en-bloc. For our efforts, we finished 1st Runners Up overall in the competition.