Authors - Chaithra G, Ambika P R, Manjunath R, Shivashankar, Niranjan R, Sowmya Naik P Abstract - Gross Domestic Product (GDP) forecasting using traditional Econometric models is indispensable for evidence-based decision-making. However, these models are often limited in their ability to handle linear relationships and adapt to high-dimensional data. This paper introduces EcoVision, an open-source web-based forecasting platform that incorporates AI and machine learning to accurately predict GDP and other associated socio-economic variables using the Gap minder dataset (175 countries, 1998-2018). Four machine learning models were used: Support Vector Machine Regression, Polynomial Regression, Decision Tree Regression, and Random Forest Regression. These were built using Python and the Flask/Scikit-learn stacks. Models were evaluated using Average Absolute Error, Squared Error, and R² values. Results show that the Decision Tree Regression model has a perfect fit (R² = 1.0, AAE = 0, SE = 0), making it the best model compared to the other models. The web interface is built using pure HTML5/CSS3/Chart.js. The integrated "Gemini API Module" enables the automatic generation of easily understandable policy summaries, thus allowing for faster extraction of insights. Results from testing the system on 3532 clean data records proved that the system is accurate in forecasting ≥ 85%, Artificial Intelligence summary relevance ≥ 80%, and export success 100%, making it a potential decision-support system for economists, researchers, and governments.
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