A MACHINE LEARNING MODEL FOR PREDICTING SUSTAINABLE ENTREPRENEURSHIP AND INNOVATION MANAGEMENT OUTCOMES

Abstract

The study describes a machine learning model created to forecast the results of sustainable entrepreneurship and innovation management, which is a key factor of business success in the contemporary competitive environment. The article handles the issue of future sustainability and innovation performance, which the conventional approaches do not accurately forecast. Through the supervised learning method, the model has incorporated the maximum number of variables, such as sustainability and innovation factors, to forecast the business performance. It was found that the model has strong predictive power with the key metrics being accuracy (85.4%), precision (82.3%), recall (79.1%), and F1-score (80.6%), as well as AUC-ROC (0.91%). The findings indicate that the proposed model is highly competitive compared to the traditional machine learning models, such as logistic regression, random forest, and the support vector machine learning model, in terms of predicting the success of sustainability and innovation strategies over a long period. The proposed model performed better in all the important metrics, showing that it is more precise in differentiating between the sustainable and non-sustainable business results. This comparison shows how machine learning is superior in incorporating sustainability and innovation metrics to forecast business success. The results are helpful to both entrepreneurs, business managers, and policymakers to enhance the way they make decisions and align innovation strategies with the Sustainable Development Goals. Through the model to forecast the success of sustainability and innovation initiatives, the business is able to allocate its resources in a more efficient manner, make investments in sustainable activities a priority, and have a deeper insight into the effectiveness of the long-term effects of its plans. The study also adds to the scope of the wider entrepreneurship and innovation management by showing how machine learning can be used to estimate the sustainability performance and could represent a potent mechanism that business organizations can utilize in planning and developing their strategies, with profound implications for both research and practice in the future.