Data-Driven Framework for Classification and Management of Start-Up Risk for High Investment Returns

Abstract

This research explores the classification of startup risks to achieve high investment returns using Random Forest Regression. The study aims to identify and predict potential risks faced by startups, thereby aiding investors in making informed decisions. We analyzed a dataset comprising various features such as funding levels, market size, expenses, team experience, product development stage, customer satisfaction scores, and revenue streams. We employed a Random Forest Regression model to evaluate the predictive power of these features. The model's performance was assessed using several metrics: Mean Squared Error (MSE), R-squared, Mean Absolute Error (MAE), Mean Squared Logarithmic Error (MSLE), and Explained Variance Score.The model demonstrated robust predictive capabilities, with an MSE of 0.255, R-squared of 0.9515, MAE of 0.782, MSLE of 0.219, and an Explained Variance Score of 0.915. These results indicate that the model effectively captures the variance in startup risks and predicts them with high accuracy. Feature importance analysis revealed that expenses and funding levels were the most critical factors influencing startup risk classification. The distribution of risks identified 12.4% Strategic Risks, 12.6% Financial Risks, 13.1% Operational Risks, 13.7% Market Risks, and 48.2% of activities with no significant risks.Based on our findings, we recommend that investors focus on key features as outlined in this research when assessing startup risks. By employing the insights provided by our model, investors can better identify high-potential startups, optimize resource allocation, and improve their investment strategies.The Random Forest Regression model offers a reliable tool for predicting and classifying startup risks, providing valuable insights that can enhance investment decision-making and ultimately lead to higher returns.