Job Shifting Prediction and Analysis Using Machine Learning

In today's volatile employmentstructure,the employees tend to shift the job in an unexpected manner. In that case the company may face issues regarding scarcity of the workforce and find problem to reemploy quickly. Thus to overcome this problem we have designed a predictive model to anticipate the chances of an employee leaving the job. In this project the train and the test datasets are taken from Analytics Vidhya site where in the algorithm used to do the prediction are Random Forest, XGBoost, CatBoost, LightGBM out of which CatBoost has performed the best and ended up giving the most accurate prediction.The datasets provided by Analytics Vidhyawere structured in nature but incomplete in observance thus to full fill that the missing values imputation procedure had to be performed and then the data was fed to the algorithm for prediction. Knowing the employees approach towards job shift prior would actually help the company to plan out the workforce efficiently.CatBoost is a gradient boosting technique on decision trees library made available as open source by Yandex.It is universally applied across a wide range of areas and to a variety of problems. Considering accuracy, robustness, usability, extensibility catboost as an upper hand over the other models.