Prediction of Indian GDP using XGBoost Compared with Adaboost

The primary aim of this study is to assess the precision of the XGBoost and Adaboost algorithms in estimating India's GDP while also addressing limitations associated with ensemble learning. The study utilized both the XGBoost and Adaboost classifiers on a dataset containing twenty samples. The statistical analysis was conducted using IBM SPSS software version 26, incorporating a 95% confidence interval, an alpha level of 0.05, and a statistical power (G power) of 80. For each algorithm, the pseudocode described methodical procedures for selecting data, cleaning it, creating features for it, creating a model, and validating it. The research, which used IBM SPSS software version 26, discovered that Adaboost had an accuracy of 89% and XGBoost had a 94
^%
prediction accuracy for the Indian GDP. These outcomes demonstrate that, when it comes to projecting the GDP of India, XGBoost outperforms Adaboost in terms of prediction accuracy. In conclusion, XGBoost outperforms Adaboost in estimating Indian GDP, demonstrating a 5% increase in accuracy, underscoring the importance of algorithm selection.