Evaluating Start-up Success Factors in Modern versus Traditional Industries in India: Insights from Poisson and Machine learning Models

This study explores the success of startups in modern industries (FinTech, EdTech, AI) compared to traditional sectors (manufacturing, textiles, agriculture) in India, focusing on factors such as funding, location, and industry type. Using a Poisson regression model, which shows strong overall significance with a p-value of 1.40e-50, the research identifies Total Funding, CB Rank, and South India as key predictors of financing rounds. Although the Poisson model struggles with startups having 12 or more funding rounds, the Random Forest model provides improved fit and prediction accuracy, with an RMSE of 0.427. The study finds no significant difference in success between technological and traditional startups, nor does it confirm that startups with 3 to 5 founders are more successful. However, it confirms that higher total funding correlates with greater success and that startups in South India outperform those in Central India. These findings offer valuable insights into startup success factors and suggest that future research could benefit from larger datasets and additional variables.