Employee Retention And Attrition Analysis: A Novel Approach On Attrition Prediction Using Fuzzy Inference And Ensemble Machine Learning.

In Today’s world, AI has become an essential tool for achieving and creating the unthinkable. It is helping in creating innovative solutions for almost every industry there is. In the wake of this ever-growing demand of computerized intelligence, the analysis of automation by HR (Human Resources) in the form of predictive form can be formulated. As the Human Resource departments is solely responsible for recruiting and bring valuable talent to the industry, it becomes essential that this task is done with maximum efficacy. Through this project, we intend to predict which employee would prefer a job change and which employee would stay in a company and hence, help constitutes as an active research domain is how AI based intelligence can be interpreted and utilized assess the input resources required to put in an employee. The present techniques are in sufficient to deal the current uncertainties caused due to confliction in their nature. In order to work on this, we propose using natural language processing, opinion mining, fuzzy logic and various widely used classifiers namely Random Forest (RF), Cat Boost Classifier, Support Vector Machine (SVM), and Naïve Bayes (NB). We also used Mamdani based fuzzy inference system using nine input and out output, for the attrition prediction in a company. In future, this model can be improved further by incorporating more data. [ABSTRACT FROM AUTHOR]