Essential Features Contributing to the Success of a Food-Tech Startup

The emergence of startups has become a significant trend in a country's economic landscape. Among startups, notably, the domain of food technology (food-tech) startups has achieved remarkable traction within a brief span. It's critical to examine the analytics-driven elements that contribute to the success or failure of a food-tech firm. However, current literature on startup analytics has primarily focused on other startup categories and has subjectively investigated the predictive behavior of these factors. This research implemented Ant Colony Optimization and the Chi-Square test for feature selection among a wide range of features from food tech startups in India. The features selected by our proposed framework were then used to train popular machine learning classification models to predict success. The key features identified highlight how entrepreneurs prioritize factors for food tech startup success and maximize socio-economic benefits. These insights help reduce uncertainty, enhance performance, and assist founders, investors, and researchers in planning future strategies effectively.