Predicting the Outcome of Startups: Less Failure, More Success

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

On an average 9 out of 10 startups fail(industry standard). Several reasons are responsible for the failure of a startup including bad management, lack of funds, etc. This work aims to create a predictive model for startups based on many key things involved at various stages in the life of a startup. It is highly desirable to increase the success rate of startups and not much work have been done to address the same. We propose a method to predict the outcome of a startups based on many key factors like seed funding amount, seed funding time, Series A funding, factors contributing to the success and failure of the company at every milestone. We can have created several models based on the data that we have carefully put together from various sources like Crunchbase, Tech Crunch, etc. Several data mining classification techniques were used on the preprocessed data along with various data mining optimizations and validations. We provide our analysis using techniques such as Random Forest, ADTrees, Bayesian Networks, and so on. We evaluate the correctness of our models based on factors like area under the ROC curve, precision and recall. We show that a startup can use our models to decide which factors they need to focus more on, in order to hit the success mark.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
EditorsCarlotta Domeniconi, Francesco Gullo, Francesco Bonchi, Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
PublisherIEEE Computer Society
Pages798-805
Number of pages8
ISBN (Electronic)9781509054725
DOIs
StatePublished - Jul 2 2016
Event16th IEEE International Conference on Data Mining Workshops, ICDMW 2016 - Barcelona, Spain
Duration: Dec 12 2016Dec 15 2016

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume0
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Other

Other16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
CountrySpain
CityBarcelona
Period12/12/1612/15/16

Keywords

  • Accuracy
  • Precision
  • Prediction
  • Startups
  • Weka

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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