A Machine Learning Based Suitable Startup Recommendation Scheme and Investor Assistance Mobile Application

Investing in startups is a complex decision-making process that requires identifying suitable companies and matching them with compatible investors. The existing works did not present any intelligent startup suitability prediction for funding and investor assistance mobile applications by taking into account growth rate, annual profit, rating, and reputation factors. This paper initiates a machine learning-based recommendation system for predicting suitable startups and matching them with appropriate investors. The proposed system leverages a comprehensive dataset of startup and investor characteristics, including market segment, geographical region, and city. In comparison with Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) schemes, our results show that TF-IDF scheme (Term Frequency-Inverse Document Frequency) is selected for startup recommendation due to its high recall and precision value. Our investor assistance mobile application offers features like login, apply for funds, best funder recommendation, best entrepreneur selection, and user rating features. Our application evaluation results indicated the more than 60 percent users are satisfied with the feasibility of the proposed mobile application.