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HIDDEN PATTERN MINING IN FAILED BUSINESS STARTUPS
The rapid growth of the global startup ecosystem has created significant opportunities for innovation, economic development, and technological advancement. However, despite the increasing number of startups emerging each year, a large proportion of them fail during their early operational stages due to factors such as poor market fit, financial instability, inefficient resource management, and strategic misalignment. Identifying the underlying causes of startup failure is therefore essential for investors, founders, and policymakers to make informed decisions and reduce financial risk. This project, titled “Hidden Pattern Mining in Failed Business Startups,” aims to develop an intelligent analytical platform capable of predicting startup failure risk and uncovering hidden patterns associated with unsuccessful ventures. The proposed system, called the Startup Failure Risk Intelligence Platform (SFRIP), integrates full-stack web technologies with advanced machine learning techniques to analyze structured startup data such as funding history, industry type, team size, operational duration, and burn rate. The platform employs the XGBoost algorithm for predictive classification to estimate the probability of startup success or failure. In addition, K-Means clustering is used to segment startups into meaningful risk categories, while the FP-Growth algorithm is applied to discover frequent patterns and associations that commonly occur in failed startups. These analytical techniques enable the system to generate both quantitative risk predictions and qualitative insights into contributing factors. The results are presented through interactive dashboards that transform complex analytical outputs into intuitive visualizations for non-technical users. By combining predictive analytics, pattern mining, and visualization within a web-based system, the project demonstrates how data-driven intelligence can enhance investment decision-making, support startup founders in identifying weaknesses, and ultimately contribute to reducing failure rates within the entrepreneurial ecosystem.
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