Welcome to our GDP Prediction Project! This project explores the intriguing relationship between a country's GDP and its energy dynamics, including consumption and production levels. Our goal is to understand how these factors interplay to influence the economic output, enabling more informed policy-making and economic planning.
In this analysis, we delve deep into the datasets representing energy consumption and production across various countries. By leveraging these indicators, we aim to forecast GDP with an aim toward accuracy and actionable insights. After experimenting with multiple machine learning models, we've identified XGBoost as the top performer, achieving an impressive accuracy of 99.81%.
- Linear Regression
- Random Forest
- SVM (Support Vector Machine)
- XGBoost (Extreme Gradient Boosting) - Our model of choice with the highest accuracy.
Why XGBoost? XGBoost has proven its efficiency in handling large datasets with a blend of speed and performance. It excels in scenarios where precision is paramount, making it ideal for our needs in GDP prediction. This model has enabled us to capture the complex nonlinear relationships between energy metrics and GDP outcomes effectively.