Salary Prediction Model using Principal Component Analysis and Deep Neural Network Algorithm


Authors : Habibu Aminu; Badamasi Imam Yau; Fatima Umar Zambuk; Emanuel Ramsom Nanin; Abdulmutallib Abdullahi; Ismail Zahraddeen Yakubu

Volume/Issue : Volume 8 - 2023, Issue 12 - December

Google Scholar : http://tinyurl.com/5ehpe26e

Scribd : http://tinyurl.com/2p9t5nj2

DOI : https://doi.org/10.5281/zenodo.10629245

Abstract : Machine learning implementations are growing in every organization for forecasting their employee’s working nature and competence by calculating the time taken by the employee to complete the task. Numerous recent research have been published to evaluate the effectiveness of various clssification algorithms for predicting the employee salary classes. From the machine learning perception, Salary prediction is a difficult task due to the small sample size, relatively high dimensionality, and presence of noise. To address this, to find more useful features, deeper architectures are required. Additionally, more data analysis and data processing can be pragmatic to make the prediction model go beyond the correlation and precision standards by feature extraction techniques. Hence, this study proposes an enhanced method for salary prediction that selects a subset of characteristics from all available data using a PCA system and a deep neural network (DNN) model for the classification process. Upon assessment with other classical machine learning methods such as DT and RF. Better classification accuracy, precision, recall, and F-score are achieved by the proposed DNN model. Furthermore, the proposed DNN model achieves the highest MAE of 94.9% as compared to DT and RF, which attain an MAE score of 89.6% and 76.4% respectively. This result suggests that the proposed model has a prediction error of 5.1% which is fewer when compared to DT and RF which has prediction error of as much as 10.4% and 23.6%, thereby, signifying the dominance of deep learning algorithm over conventional machine learning algorithms in salary classification and prediction task.

Keywords : Principal Component Analysis, Random Forest, Deep Neural Network, Machine Learning, Deep Learning, and Decision Tree.

Machine learning implementations are growing in every organization for forecasting their employee’s working nature and competence by calculating the time taken by the employee to complete the task. Numerous recent research have been published to evaluate the effectiveness of various clssification algorithms for predicting the employee salary classes. From the machine learning perception, Salary prediction is a difficult task due to the small sample size, relatively high dimensionality, and presence of noise. To address this, to find more useful features, deeper architectures are required. Additionally, more data analysis and data processing can be pragmatic to make the prediction model go beyond the correlation and precision standards by feature extraction techniques. Hence, this study proposes an enhanced method for salary prediction that selects a subset of characteristics from all available data using a PCA system and a deep neural network (DNN) model for the classification process. Upon assessment with other classical machine learning methods such as DT and RF. Better classification accuracy, precision, recall, and F-score are achieved by the proposed DNN model. Furthermore, the proposed DNN model achieves the highest MAE of 94.9% as compared to DT and RF, which attain an MAE score of 89.6% and 76.4% respectively. This result suggests that the proposed model has a prediction error of 5.1% which is fewer when compared to DT and RF which has prediction error of as much as 10.4% and 23.6%, thereby, signifying the dominance of deep learning algorithm over conventional machine learning algorithms in salary classification and prediction task.

Keywords : Principal Component Analysis, Random Forest, Deep Neural Network, Machine Learning, Deep Learning, and Decision Tree.

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