%0 Journal Article %1 noauthororeditor %A Naik, Dr. R. S. Kamath | Dr. S. S. Jamsandekar | Dr. P. G. %D 2019 %J International Journal of Trend in Scientific Research and Development %K -iron %P 62-67 %R https://doi.org/10.31142/ijtsrd23065 %T Machine Learning Approach for Employee Attrition Analysis %U https://www.ijtsrd.com/computer-science/artificial-intelligence/23065/machine-learning-approach-for-employee-attrition-analysis/dr-r-s-kamath %V Special Issue %X Talent management involves a lot of managerial decisions to allocate right people with the right skills employed at appropriate location and time. Authors report machine learning solution for Human Resource HR attrition analysis and forecast. The data for this investigation is retrieved from Kaggle, a Data Science and Machine Learning platform 1 . Present study exhibits performance estimation of various classification algorithms and compares the classification accuracy. The performance of the model is evaluated in terms of Error Matrix and Pseudo R Square estimate of error rate. Performance accuracy revealed that Random Forest model can be effectively used for classification. This analysis concludes that employee attrition depends more on employees’ satisfaction level as compared to other attributes. Dr. R. S. Kamath | Dr. S. S. Jamsandekar | Dr. P. G. Naik "Machine Learning Approach for Employee Attrition Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23065.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23065/machine-learning-approach-for-employee-attrition-analysis/dr-r-s-kamath

@article{noauthororeditor, abstract = {Talent management involves a lot of managerial decisions to allocate right people with the right skills employed at appropriate location and time. Authors report machine learning solution for Human Resource HR attrition analysis and forecast. The data for this investigation is retrieved from Kaggle, a Data Science and Machine Learning platform 1 . Present study exhibits performance estimation of various classification algorithms and compares the classification accuracy. The performance of the model is evaluated in terms of Error Matrix and Pseudo R Square estimate of error rate. Performance accuracy revealed that Random Forest model can be effectively used for classification. This analysis concludes that employee attrition depends more on employees’ satisfaction level as compared to other attributes. Dr. R. S. Kamath | Dr. S. S. Jamsandekar | Dr. P. G. Naik "Machine Learning Approach for Employee Attrition Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23065.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23065/machine-learning-approach-for-employee-attrition-analysis/dr-r-s-kamath }, added-at = {2019-06-29T10:11:53.000+0200}, author = {Naik, Dr. R. S. Kamath | Dr. S. S. Jamsandekar | Dr. P. G.}, biburl = {https://www.bibsonomy.org/bibtex/2717bf9466c17b5c2318e7db5876ceb7a/ijtsrd}, doi = {https://doi.org/10.31142/ijtsrd23065}, interhash = {4a54b139e34398bd635ff9e0a7585e97}, intrahash = {717bf9466c17b5c2318e7db5876ceb7a}, issn = {2456-6470}, journal = {International Journal of Trend in Scientific Research and Development}, keywords = {-iron}, language = {English}, month = {March}, pages = {62-67}, timestamp = {2019-06-29T10:11:53.000+0200}, title = {Machine Learning Approach for Employee Attrition Analysis }, url = {https://www.ijtsrd.com/computer-science/artificial-intelligence/23065/machine-learning-approach-for-employee-attrition-analysis/dr-r-s-kamath}, volume = {Special Issue}, year = 2019 }