[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119121-en":3,"doc-seo-119121-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119121,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Employee Turnover Prediction - A Machine Learning Approach - Project Work","The project addresses employee turnover as a common organizational problem in a highly competitive intelligent technology market, emphasizing negative business impacts. It develops a machine learning solution to predict employees’ turnover for Company X, relying on careful analysis and preparation of a complex, imbalanced dataset. Hyperparameter tuning, including class-weight adjustment via grid search, supports stronger performance. The work evaluates models using train-test split and K-fold cross-validation and identifies key variables tied to turnover while forecasting future leavers.","MDSAA  \nMaster Program in  \nData Science and Advanced Analytics  \nEmployee Turnover Prediction  \nA Machine Learning Approach  \nRafael Jorge Oliveira Nunes  \nProject Work  \nPresented as partial requirement for obtaining the Master’s degree in Data Science and Advanced Analytics  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nEMPLOYEE TURNOVER PREDICTION – A MACHINE LEARNING  \nAPPROACH  \nby  \nRafael Jorge Oliveira Nunes  \nProject Work presented as partial requirement for obtaining the Master’s degree in Data Science and Advanced Analytics, with specialization in Business Analytics  \nAdvisor: Dr. João Bruno Morais de Sousa Jardim  \nCo-Advisor: Prof. Doutor Miguel de Castro Simões Ferreira Neto  \nNovember 2023  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledge the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nRafael Jorge Oliveira Nunes  \nLisbon, 21 November 2023  \nACKNOWLEDGMENTS  \nA special thank you to my parents who always provided me with the necessary assistance and support to achieve my dreams. To my friends that helped me in the worst days, they cheered me up and pushed me to do my best. And to all the professors that taught me in this journey of my life, sharing the knowledge and helping me to prepare myself to deal with adversities but also walk into a brighter future.  \nThank you all.  \nABSTARCT  \nThe competition in the intelligent technology market is very harsh. Therefore, turnover is a common problem for all organizations and leads to a negative impact on the business. Machine learning approaches to predict employee turnover are emerging, due to their predictive power and capacity to identify patterns in the data, leading to organization competitive advantage. The aim of this project was to develop a solution to predict the employees’ turnover from Company X. Due to a complex and imbalance dataset, the data analysis and preparation phase was crucial for the development of the project, for example adjust hyperparameter “class weight” from grid search was a necessity. To assess the solution, it was employed mostly tree-based ensemble methods supervised machine learning algorithms, fine-tuned with the specific hyperparameters to improve model performance. Regarding model evaluation, it was applied train-test split and K-fold cross-validation to have a more robust assessment. Furthermore, the solution can identify the variables that contributed to the turnover rate increase, but also predict the employees that are most likely to leave the company in the future.  \nKEYWORDS  \nTurnover Prediction; Classification problem; Cross-Industry Standard Process for Data Mining (CRISPDM); Machine Learning (ML); Ensemble Algorithms.  \nObjetivos do Desenvolvimento Sustentável (ODS):  \nINDEX  \n1. Introduction .................................................................................................................. 1  \n2. Theoretical Backgroung Overview – ML Aplications ....................................................4  \n2.1. Logistic Regression ................................................................................................4  \n2.2. Decision Tree .........................................................................................................5  \n2.3. Ensemble ...............................................................................................................6  \n2.4. Random Forest ......................................................................................................7  \n2.5. Adaboost.................","cbCaibHiBbzToetc","https://ap.wps.com/l/cbCaibHiBbzToetc","pdf",2669951,1,65,"English","en",105,"# Introduction\n# Theoretical Background Overview - ML Applications\n## Logistic Regression\n## Decision Tree\n## Ensemble\n## Random Forest\n## Adaboost\n## Extreme Gradient Boosting\n## Catboost\n# Literature Review\n# Methodology\n## Tools\n## Methods\n## Business understanding\n## Data Understanding\n## Data Preparation\n## Modelling\n## Evaluation\n## Deployment\n# Results and discussion\n# Conclusions\n# Limitations and recommendations\n# Bibliography","[{\"question\":\"What is the main goal of the project?\",\"answer\":\"To develop a machine learning solution that predicts employees’ turnover for Company X and supports organizational decision-making.\"},{\"question\":\"Why was data preparation considered crucial in this work?\",\"answer\":\"The dataset is complex and imbalanced, making analysis and preparation necessary for effective modeling.\"},{\"question\":\"How are models evaluated and tuned?\",\"answer\":\"Evaluation uses train-test split and K-fold cross-validation, while model performance is improved through fine-tuned hyperparameters such as class-weight adjustment from grid search.\"}]","Employee Turnover Prediction - 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