AN EFFICIENT NOVEL APPROACH FOR PREDICTION OF START-UP COMPANY SUCCESS RATES THROUGH ML PARADIGAMS

Abstract

The rapid growth of start-up ecosystems has made it increasingly important to predict the success rate of emerging companies. Start-ups face high uncertainty due to factors such as market competition, funding availability, business models, and management strategies. Traditional evaluation methods rely heavily on manual analysis and expert judgment, which can be subjective and time-consuming. This project proposes an efficient and novel approach for predicting start-up company success rates using machine learning paradigms. The proposed system utilizes historical data of start-ups, including features such as funding rounds, industry type, team experience, revenue growth, and market conditions. Data preprocessing techniques such as cleaning, normalization, and feature selection are applied to improve model performance. Various machine learning algorithms such as Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines are implemented to classify start-ups as successful or unsuccessful. The models are trained and tested using an 80:20 dataset split, and their performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. Experimental results indicate that ensemble models like Random Forest provide higher accuracy and better generalization compared to other algorithms. This approach provides a data-driven and objective solution for predicting start-up success, which can assist investors, entrepreneurs, and policymakers in making informed decisions and reducing investment risks.