SharkTank Deal Prediction: Dataset and Computational Model
Document Type
Conference Paper
Publication Date
12-5-2019
Publication Source
2019 11th International Conference on Knowledge and Systems Engineering (KSE)
Abstract
SharkTank is a television show where start-ups pitch their idea to a panel of five investors (sharks) in hopes of striking a deal in the form of equity or royalties for money and other business perks. Since its inception, SharkTank has been a center of discussion and analysis for fans, statisticians, and business people alike in hopes of cracking the code to the start-up world and figuring out the formula for the next big `thing'. Most of these discussions and analyses have come in the form of blogs, articles, and academic research. However, there has been a lack of complete datasets and application of computational models for further analysis. In this paper, we investigate factors that play into the SharkTank deal. To this end, we first collect a new dataset, SharkTank Deal Dataset (STDD), by combining data from multiple public sources. The dataset includes descriptive features of each start-up such as product category, team composition, valuation, equity offering, specific sharks that appear on that episode, and state origin. For the computational model, we propose a new computational model to predict whether a start-up strikes a deal with a shark. We conduct experiments to demonstrate the superiority of our model over the baselines.
ISBN/ISSN
2164-2508
Publisher
IEEE
Keywords
startup, deal prediction, dataset, computational model
eCommons Citation
Sherk, Thomas; Tran, Minh-Triet; and Nguyen, Tam V., "SharkTank Deal Prediction: Dataset and Computational Model" (2019). Computer Science Faculty Publications. 223.
https://ecommons.udayton.edu/cps_fac_pub/223
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