A data mining-based technique is proposed for the selection and employment of the best-fit programmers to meet the needs of software companies. The proposed technique incorporates Bayes' theorem and artificial neural network (ANN). The datasets used were from two software companies (Company 1 and Company 2) in India, covering the years 2010–2015. Bayes' theorem is used for identifying the prognostic attributes of the best-fit programmers, while the ANN classifier is used for predicting the best-fit programmers. Using a confusion matrix, the ANN classifier performance is 97.2 and 87.3%, 95.8 and 54.5%, and 100 and 75% with regard to accuracy, precision, and recall on the two test datasets of Company 1 and Company 2, respectively. The results show that the technique is effective for predicting the best-fit programmers. Software companies can use this technique in their recruitment and selection process to determine the best-fit employees for the programmer posts. The proposed technique can also be adapted for application in other disciplines such as sports, education, etc., to identify the most suitable person to fill a relevant position.

中文翻译：

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使用贝叶斯定理和人工神经网络确定最适合的程序员

提出了一种基于数据挖掘的技术，用于选择和雇用最合适的程序员，以满足软件公司的需求。所提出的技术结合了贝叶斯定理和人工神经网络（ANN）。使用的数据集来自印度的两家软件公司（公司1和公司2），涵盖了2010-2015年。贝叶斯定理用于识别最适合的程序员的预后属性，而ANN分类器则用于预测最适合的程序员。使用混淆矩阵，就公司1和公司2的两个测试数据集的准确性，准确性和查全率而言，ANN分类器的性能分别为97.2和87.3％，95.8和54.5％，100和75％。结果表明，该技术可有效预测最适合的程序员。软件公司可以在招聘和选择过程中使用此技术来确定最适合该职位的员工。所提出的技术还可以适用于其他学科，例如体育，教育等，以识别最合适的人选来填补相关职位。