Employee Promotion Prediction using Machine Learning and Flask Framework

Description

Employee promotion decisions are pivotal in shaping an organization’s success, yet they often rely on subjective judgments and biases. This research introduces a transformative approach by leveraging machine learning to enhance the precision and fairness of employee promotion decisions. Our study commences with the collection and analysis of comprehensive employee data, encompassing performance metrics, skills, experience, and relevant attributes. Machine learning models, including decision trees and ensemble techniques, are deployed to extract actionable insights from this data. These models uncover the underlying patterns and criteria that influence promotion decisions, providing transparency and objectivity to the process. Through rigorous evaluation and validation, we demonstrate the efficiency of our machine learning-driven approach in improving promotion decisions. Results reveal enhanced accuracy, reduced bias, and increased consistency, underscoring the transformative potential of this methodology