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Authors
Soumanshu Jain,Rahul Nawkhare,Naveen Sharma
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Keywords
Employee Performance Prediction, Machine Learning Classification, Naive Bayes Classifier, Predictive Analytics, Human Resource Optimization, Model Deployment Using Pickle.
Abstract
Predicting employee performance is essential for organizations striving to improve productivity and align their workforce with long-term goals. Machine learning offers powerful tools to evaluate and forecast employee outcomes based on historical and behavioral data. In this implementation, a classification-based approach is adopted using the "INX Future Inc Employee Performance" dataset, which contains key employee attributes relevant to performance metrics. Various supervised learning algorithms were utilized, including Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, and Naive Bayes classifiers. Data preprocessing and model serialization were handled using the Pickle module to facilitate deployment and reuse of trained models. Each algorithm was evaluated based on its predictive accuracy, with Naive Bayes emerging as the most effective, achieving an accuracy of 82%. This suggests that Naive Bayes, despite its simplicity, is well-suited for handling categorical and probabilistic data common in employee performance evaluations. The outcome demonstrates the potential of integrating machine learning into human resource management systems for efficient performance tracking and decision-making. By leveraging structured data and predictive analytics, organizations can enhance their talent management strategies and proactively address areas requiring improvement.
IJCRT's Publication Details
Unique Identification Number - IJCRT25A5025
Paper ID - 286582
Author type - Indian Author
Page Number(s) - i931-i939
Pubished in - Volume 13 | Issue 5 | May 2025
DOI (Digital Object Identifier) -
No Of Downloads - 86
Author Country - India, 482001, Jabalpur, Jabalpur, 482001, Management All
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Soumanshu Jain,Rahul Nawkhare,Naveen Sharma, "Analysis Of Employee Performance Prediction", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 5, pp.i931-i939, May 2025, Available at :http://www.ijcrt.org/papers/IJCRT25A5025.pdf
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