Sunil Wagh, Rupali (2010) Employee Performance Prediction Model. Other thesis, Christ University.
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With the dominance of knowledge power in the success of an organization, competent human resource has become crucial for realization of organizational objectives. Human Resource Management, HRM is a set of tasks to maintain and develop a proficient human resource. A performance appraisal process helps the HRM in identifying the strengths and weaknesses of an employee. This evaluation of employee is based on several different parameters according to the work domain and organizational objectives. This activity of employee evaluation has a high significance in making strategic decisions of manpower planning than just salary reviews. The objective of the prediction model constructed in the study is to assist HR personnel in decision making by predicting the performance of an employee. This study, has developed an Employee Performance Prediction Model which can predict the performance category of an employee. Academic industry data is used for the analysis. The supervised learning approach of classification has been first applied to the performance data to construct the model. The methodology adapted by the study is a five step process of Data preprocessing, Dimensionality Reduction, Application of classification algorithm to build the classifier and Evaluation of the classifier. Based on the hierarchical characteristics of the data, three different approaches – Direct, Hierarchical and Integrated are followed for the construction of the classifiers. A comparative analysis of the classifiers’ performances based on prediction accuracy and error rate is performed. Unsupervised learning approach is used to understand natural similarities among the employees to enhance the evaluation process. The thesis is organized into five chapters. Chapter1, Introduction, starts with a brief overview of HRM and presents the objective of the study and the problem statement. It further describes a typical KDD process and role of data mining in a KDD process. Chapter 2, Literature review, discusses background work in the field of data mining and HRM. Different models and techniques used in the context are described. Chapter 3, Methodology elaborately discusses the processes and techniques used for constructing the Employee Performance Prediction Model. Starting with the block diagram, the chapter elaborates the sequence of steps that led to the results. Chapter 4, Results and Discussion discusses the performance statistics of the different classifiers constructed in the study. Chapter 5, Conclusion, summarizes the inferences concluded based on the results obtained. The chapter also discusses limitations and challenges and concludes with future scope of the study. Keywords: Human Resource Management, Performance Appraisal, Knowledge discovery, Data Mining, Supervised Learning, Unsupervised Learning, Classification and Prediction.
|Item Type:||Thesis (Other)|
|Subjects:||Thesis > MPhil > Computer Science|
|Deposited By:||Knowledge Center Christ University|
|Deposited On:||11 Jan 2014 18:22|
|Last Modified:||11 Jan 2014 18:22|
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