This study and analysis delves into the relationship between employee data and their likelihood to recommend their employer. It utilizes a dataset comprising user IDs linked to various workplace factors like job title, salary, satisfaction level, tenure, and propensity to recommend their company. The main goal is to pinpoint factors that affect employee satisfaction based on a Python decision tree classification algorithm.
A significant part of the study involved optimizing a decision tree model. After testing various combinations of splits and depths, the model’s predictive accuracy is low, with just over 60% correct classification rates on both training and validation sets. This low accuracy, coupled with the potential biases in the data, suggests that the model is not yet suitable for practical business applications. The findings underline the need for more comprehensive data to enhance the model’s reliability and applicability in a corporate environment.
