Prediction Models for Startups Success: A Empirical Analysis
Ramakrishna Allu1, Venkata Nageswara Rao Padmanabhuni2

1Ramakrishna Allu*, Research Scholar, Department of Computer Science and Engineering, GITAM (Deemed to be University), Visakhapatnam, India and Associate Director in Novartis, Hyderabad, India.
2Venkata Nageswara Rao Padmanabhuni, Professor, Dept. of CSE, GITAM (Deemed to be university), Visakhapatnam, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 10, 2020. | PP: 1647-1650 | Volume-9 Issue-5, March 2020. | Retrieval Number: E3053039520/2020©BEIESP | DOI: 10.35940/ijitee.E3053.039520
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Abstract: Small and Medium-Scale Enterprises have been recognized by the government due to their significant role in the country’s economy. The risk of capital investment is high in the enterprises and various factors need to be properly analyzed for the prediction of success of an enterprise. Machine learning techniques can be adopted to predict the success of startups that helps the entrepreneur to make a decision accordingly. In this paper, a details analysis has been carried out on the existing methodologies on startup success prediction to analyze the benefits and limitations. Fewer researches has been carried out in the startup success prediction and achieves the considerable prediction performance. Major limitation has been found among startup success prediction model that use irrelevant features. Some researchers have used social media datasets like Twitter data to increase the performance of the developed method. From existing methods, it has been observed that Random Forest classifiers have been out performed than Logistic Regression method. 
Keywords: Logistic Regression, Machine learning Techniques, Random Forest Classifiers, Startup Success Prediction, and, Twitter data.
Scope of the Article: Machine Learning