Crowdfunding is an increasingly popular way of funding projects. Kickstarter is one of those crowdfunding organizations. But is there a way to predict a project’s success or failure? Kickstarter enthusiast Dan Misener has put together a site called The Kickback Machine that helps to organize the data from Kickstarter projects. We used that data to create this predictive model: Predicting Kickstarter Success!
 

Let’s Date is an app that helps you find people to date. The Let’s Date team took a different approach to matching. “We wanted to eliminate laborious surveys and questionnaires, fruitless repetitive messaging and endless search criteria and useless result lists. It was important for us to create something that requires less commitment, less effort and more results. You just casually browse people’s cards and let us know your reaction and our system learns how to deliver better cards to you.” Using BigML, the team has built a machine learning backend to learn from users likes and dislikes and increase the success of proposed dates. Check out Let’s Date for yourself.
 

Just recently we published a Clojure library for our API in our constant effort to make machine learning simple for you. For more details and some examples, you can read more here.

There are many places where you can find great data. Windows Azure Marketplace DataMarket is one of those places. You can now browse datasets and get them into your BigML dashboard without leaving BigML or even without up- or downloading! Just click the ones that you want to work with and we’ll get it from the Windows Azure DataMarket to your dashboard. Check this blog post for more details.