Startup Success Prediction using Machine Lear...

Startup Success Prediction using Machine Learning

Author Name : Nikhitha U, Nagaraju K, Srihith Cherukuri, Rahamath SK, Mrs. P. Renuka

ABSTRACT Startups play a crucial role in driving innovation and economic growth, yet their success remains highly unpredictable. Many startups fail within the first few years due to poor product-market fit, financial challenges, or ineffective business strategies. Traditional methods for evaluating startup success rely on expert opinions and historical data analysis, which can be subjective, time-consuming, and prone to errors. This research proposes an automated startup success prediction system leveraging machine learning techniques, specifically the AdaBoost algorithm. The model is trained on a dataset of startup-related factors such as funding history, market trends, team composition, and financial metrics, enabling it to classify startups based on their likelihood of success. Data preprocessing techniques, including feature scaling and selection, enhance model accuracy and robustness. The proposed system is designed for efficient decision-making, allowing investors and entrepreneurs to assess potential startup outcomes with greater confidence. Experimental results demonstrate superior predictive accuracy compared to traditional evaluation methods, reducing investment risks and improving resource allocation. This system not only benefits investors by enhancing decision-making processes but also supports startups in refining their strategies for long-term sustainability. Future improvements include incorporating real-time market data and sentiment analysis to further refine predictions, paving the way for data-driven innovation in startup investment and management.