Utilizing CART Classification Decision Tree Model to Examine Unemployment Trend in Nigeria

Lekia Nkpordee¹ , Juma Ndhokero²

Section:Research Paper, Product Type: Journal-Paper
Vol.11 , Issue.3 , pp.26-37, Jun-2024

Online published on Jun 30, 2024

Copyright © Lekia Nkpordee, Juma Ndhokero . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
This particular study utilizes CART classification decision tree models to examine thirty - three years of unemployment information in Nigeria, derived from the World Bank along with the International Labour Organization. Using extensive statistical tests, such as the BDS test for nonlinearity, the ADF device root test for stationarity, and the Breusch Pagan test for heteroscedasticity, the research validates the value of the CART design for understanding the complex dynamics of unemployment in Nigeria. Important elements including household income, the industry sector in addition to education are identified and also highlighted their importance in determining unemployment rates. Results demonstrated the sequence of unemployment rates is stationary yet nonlinear, highlighting the complicated nature of unemployment dynamics. CART models display substantial predictive accuracy, discover considerable predictors of unemployment, and also offer insights for policy formulation. This research plays a role in the literature by introducing a novel approach to examining unemployment rates in Nigeria and stresses the need for innovative analytical methods to tackle socioeconomic challenges.

Key-Words / Index Term :
CART Classification, Decision Tree Model, Examination, Machine Learning, Unemployment, Trend

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