Machine Learning Modelling of GDP Datasets of Botswana

Author(s)	R Sivasamy, Moseki, K. K, Makatjane, K, K.Kelepile
Country	Botswana
Abstract	The main objective of this paper is to investigate how exogenous variables X= (X1, X2, …, X14) affect the gross domestic product (GDP) of Botswana data (Y) using different multivariate methods such as PCA, linear discriminatory analysis (LDA), factor analysis and other classifiers. Using supervised learning techniques, machine learning models are fitted and performance is monitored against test data, calculating the error between observed Y and predicted Y in each case. The results show 100% accuracy for some classifiers and more than 75% accuracy for some other classifiers.
Keywords	machine learning, dimensionality reduction, classifier analysis.
Field	Mathematics > Statistics
Published In	Volume 5, Issue 5, September-October 2023
Published On	2023-10-31
Cite This	Machine Learning Modelling of GDP Datasets of Botswana - R Sivasamy, Moseki, K. K, Makatjane, K, K.Kelepile - IJFMR Volume 5, Issue 5, September-October 2023. DOI 10.36948/ijfmr.2023.v05i05.8209
DOI	https://doi.org/10.36948/ijfmr.2023.v05i05.8209
Short DOI	https://doi.org/gs38pz

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