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Predicting Startup Success

by on July 10, 2014

My name is Rahul Desai and I’m the CEO and co-founder of Trendify, a meta-startup that uses machine learning and big data to more reliably determine whether any given startup will succeed or not. I’d like to re-count the Trendify story, and elaborate on where BigML fits in.

bigml_startup

From News Analytics to Startup Prediction

At first, I wanted to create a news analytics platform that could predict stock activity through social sentiment analysis. Not soon after, I gave up on it because of my non-tech background, a seemingly insurmountable obstacle. After getting a job at a local startup, Encore Alert, I sought mentorship from the CEO there. He knew what I was trying to do and sent me some interesting leads (iSentium social sentiment, and a neural network for stock-picking), at which point I realized people were doing stock prediction but no one was doing startup prediction. We focus on startup prediction because it’s a fairly open market and one where risk management tools have the potential for incredible impact. By helping both investors and entrepreneurs, we can bring some incredible technologies to life, that otherwise might not see the light of day.

The Data

In February, I began gathering open data from various sources on the internet, building a set of 10,000 data points regarding 130 companies: founder, company, and funding data. Although I’d love to elaborate more on the types of data I’ve collected, we feel that we owe it to our clients and potential clients that we protect their privacy and the confidentiality of their data. However, in broad strokes, I can mention that we do an extremely thorough data collection that matches or even surpasses Bloomberg in its scope, drawing from news, social media, and business databases.

A Predictive Model of Startup Success

After collecting this data, I ended up building a model on my own; this is where I used BigML. They have an easy-to-use, gorgeous interface that’s also incredibly powerful. We created an ensemble using training data regarding 65 companies. After training and testing, this model was able to predict the eventual success of Dell, Beats, and Box, as well as the failure of Fisker, with only the data that would be available during the first few years of operation. That initial model was built just to prove that we can accomplish our mission.

In the near future, we intend to create an ensemble around our new dataset: 1,000,000+ data points spanning thousands of companies. At that scale, we can show statistical significance. The output of this model will be directly actionable for our clients, indicating success/failure with confidence levels, and offering a print-out of the most contentious factors that led to a particular decision. After our beta launch later this year, we’re going to integrate real-time analytics so that investors and entrepreneurs can monitor milestones’ effects on their companies. With easily usable platforms like BigML, companies like Trendify can be viable. Team Trendify is very thankful to BigML for helping us prove that it’s actually possible to do what we set out to, and we look forward to continuing our relationship.

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