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Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms

Author

Listed:
  • Priya Bijalwan

    (Department of Management Studies, Graphic Era University, Dehradun 248002, India)

  • Ashulekha Gupta

    (Department of Management Studies, Graphic Era University, Dehradun 248002, India)

  • Anubhav Mendiratta

    (Department of Management Studies, Graphic Era University, Dehradun 248002, India)

  • Amar Johri

    (College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh 11673, Saudi Arabia)

  • Mohammad Asif

    (College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh 11673, Saudi Arabia)

Abstract

One of the most significant areas of local government in the world is the municipality sector. It provides various services to the residents and businesses in their areas, such as water supply, sewage disposal, healthcare, education, housing, and transport. Municipalities also promote social and economic development and ensure democratic and accountable governance. It also helps in encouraging the involvement of communities in local matters. Workers of Municipalities need to maintain their services regularly to the public. The productivity of the employees is just one of the main important factors that influence the overall organizational performance. This article compares various machine learning algorithms such as XG Boost, Random Forest (RF), Histogram Gradient Boosting Regressor, LGBM Regressor, Ada Boost Regressor, and Gradient Boosting Regressor on the dataset of municipality workers. The study aims to propose a machine learning approach to predict and evaluate the productivity of municipality workers. The evaluation of the overall targeted and actual productivity of each department shows that out of 12 different departments, only 5 departments were able to meet their targeted productivity. A 3D Scatter plot visually displays the incentive given by the department to each worker based on their productivity. The results show that XG Boost performs best in comparison with the other five algorithms, as the value of R Squared is 0.71 and MSE (Mean Squared Error) is 0.01.

Suggested Citation

  • Priya Bijalwan & Ashulekha Gupta & Anubhav Mendiratta & Amar Johri & Mohammad Asif, 2024. "Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms," Economies, MDPI, vol. 12(1), pages 1-19, January.
    • Handle: RePEc:gam:jecomi:v:12:y:2024:i:1:p:16-:d:1318225
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    References listed on IDEAS

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    1. Hui, Gang & Chen, Zhangxin & Wang, Youjing & Zhang, Dongmei & Gu, Fei, 2023. "An integrated machine learning-based approach to identifying controlling factors of unconventional shale productivity," Energy, Elsevier, vol. 266(C).
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