TY - GEN
T1 - Predicting the Outcome of Startups
T2 - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
AU - Krishna, Amar
AU - Agrawal, Ankit
AU - Choudhary, Alok
PY - 2016/7/2
Y1 - 2016/7/2
N2 - 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.
AB - 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.
KW - Accuracy
KW - Precision
KW - Prediction
KW - Startups
KW - Weka
UR - http://www.scopus.com/inward/record.url?scp=85015246246&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015246246&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2016.0118
DO - 10.1109/ICDMW.2016.0118
M3 - Conference contribution
T3 - IEEE International Conference on Data Mining Workshops, ICDMW
SP - 798
EP - 805
BT - Proceedings - 16th IEEE International Conference on Data Mining Workshops, ICDMW 2016
A2 - Domeniconi, Carlotta
A2 - Gullo, Francesco
A2 - Bonchi, Francesco
A2 - Bonchi, Francesco
A2 - Domingo-Ferrer, Josep
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Zhou, Zhi-Hua
A2 - Wu, Xindong
PB - IEEE Computer Society
Y2 - 12 December 2016 through 15 December 2016
ER -