Efficiency Predictor: Predicting the Consumption Efficiency of Humans by Machine Learning Technique

Abstract

As computer science advances and integrates with statistics in the field of machine learning, the predictability of future events is increasing. Our project focuses on leveraging this domain to forecast human performance using a minimal set of attributes, thereby reducing the need for extensive labels. As present solutions in machine learning helped humanity to predict natural events there is no accurate existing solution to predict the same for human beings. Human efficiency may include the development of an individual or the development of a team or collaboration. Making progress in a work without knowing the success rate might be a challenge as the final output may or may not give the expected results. The amount of hard work engaged in work that may fail in the future causes a great loss of time and energy. The involvement of computers integrated with the statistical models motivates and helps to predict the final output. So, we have taken the initiative to predict the future performance of a person in a more accurate and precise manner. This project aims to predict the consumption efficiency performance of a person using a machine learning algorithm by Ensemble-based Progressive Prediction.