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CrunchScoring: predicting future startup winners with machine learning and CrunchBase data

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Information about CrunchScoring: predicting future startup winners with machine learning...
Technology

Published on February 28, 2014

Author: Exacaster

Source: slideshare.net

Description

At #oohack Hackathon in Helsinki in Oct 2012, Exacaster team decided to verify a few startup myths by employing machine learning techniques to analyze CrunchBase data. Some of the questions they addressed:

- Is it true that only 2% of startups ever exit?
- Are we getting better at doing startups over time?
- Is it possible to improve your chances of picking the winners by employing publicly available data?
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What’s  your  CrunchScore?   Tes3ng  Startup  Myths  with  CrunchBase  Data     #oohack    Helsinki  Data  Hackathon,  October  16,  2012   Exacaster  Team   Sarunas  /  Egidijus  /  Rokas  /  Andrius  /  Vidmantas  /  Justas     sarunas@exacaster.com  |  www.exacaster.com  

Only  2%  Of  Startups  Did  Ever  Exit   acquired  or  IPOed   2%   raised  funding   23%   founded   75%  

Are  We  GeJng  BeKer  at  Doing  Startups  Over  Time?   Ignore  –  not   enough     Mme  to  exit,  yet   Year  company  formed  

Spot  The  Flood  …  And  The  Lack  of  Exits  

Math  to  the  rescue!    

Our  CrunchScoring  Algorithm    +  Startup  CrunchBase  profile  =   Double  The  Chance  of  Picking  the  Right  Startup   9%   8%   7%   6%   5%   4%   8%  of  companies  later   exi3ng  within  the   cohort  with  a  Top   Predicted     CrunchScore   3%   4%  of  companies  later   exi3ng  within  the   cohort  with  a  Low   Predicted     CrunchScore   2%   1%   0%   Top  500  scorers  sold   Remaining  companies  sold  

Brought  to  you  in  10  hours  by:   #oohack     Helsinki  Data   Hackathon,   October  16,   2012   exacaster.com  

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