Predicting Entrepreneurial Activities from Socio-Economic Factors Using Neural Networks

This study aims to develop a model for predicting entrepreneurial activities based on socioeconomic factors through a neural network approach. This study processes the loss function using Mean Squared Error (MSE). This calculation evaluates the gap between predicted and actual values of entrepreneurial activities. To achieve this, the following libraries are used: NumPy, Scikit-learn, and Tensorflow. After 21 epochs, the loss function decreases which helps in improving the predictability of the model. As entrepreneurial development is a key driver of socioeconomic growth, this study provides predictive accuracy that brings important insights for policymakers and researchers.