Predictions for Startups

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    In general 90 out of 100 startups fail to gain expected funding. There can be several reasons like bad management, lack of sufficient funds, good working team etc. which leads to failure of startup. This work aims to create a machine learning model for predicting the range of funding for the startups based on many key attributesthat are involved at different stages in the startup functioning. It is very important to predict the range of funding prior to the implementation of project and till today not much work is done in this respect. This paper proposes implementing a model to predict the funding of a startups based on many important factors like idea of the startup, place where the startup established, domain vertical to which the startup belongs, prior investors, type of funding the organization is expecting. A model is developed by working on real time data of startups from 2015 to 2017. Classification and regression algorithms are used to build the model.

     

     


  • Keywords


    Crunchbase, Random Forest, Startup Funding.

  • References


      [1] A. Agrawal, P. D. Deshpande, A. Cecen, G. P. Basavarsu, A. N. Choudhary, and S. R. Kalidindi. Exploration of data science techniques used to predict the strength of steel andIntegrating Materials and Manufacturing Innovation, 3(8):1–19, 2014.

      [2] T. M. Begley and W.-L. Tan. The socio-cultural environment for entrepreneurship: it is a comparison between asian and anglo-saxon countries published in Journal of international business studies, pages 537–553, 2001.

      [3] Breiman. L. for Random forests. Mach. Learn., 45(1):5– 32, Oct. 2001.

      [4] kaggle.com for startup funding data

      [5] Amar Krishna, Ankit Agrawal, AlokChoudhary. "Predicting the Outcome of Startups: Less Failure, More Success" , 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016.


 

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Article ID: 17628
 
DOI: 10.14419/ijet.v7i3.12.17628




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