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

Aug 3, 2026

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As machine learning advances and integrates with statistics, predicting future outcomes becomes increasingly feasible. This project develops an approach to forecast human performance using a minimal attribute set, reducing reliance on extensive labeling. Existing ML solutions predict natural events, but an accurate method for human consumption efficiency is limited. The work proposes an Ensemble-based Progressive Prediction model to estimate future performance more precisely, helping decision-making when success rates are otherwise unknown.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages3

Reading Time8 min

AccessFree to download

Introduction

Approaches of Prediction of Human Performances

Prediction Based on C4.5 Algorithm

Efficiency Predictor: Predicting the Consumption Efficiency of Humans by Machine Learning Technique 1Dr. S. Vidya, 2Dr. T. Veeramakali, 3Dr. N. C. Brintha, 4S. Inbasekar 1CSE, Sri Sairam Institute of Technology Chennai, India [e-mail: vidya.cse@sairamit.edu.in](e-mail: vidya.cse@sairamit.edu.in) 2Data Science and Business Systems, SRM Institute of Science & Technology Chennai, India [e-mail: ](e-mail: veeramat@srmist.edu.in)[veeramat@srmist.edu.in](e-mail: veeramat@srmist.edu.in) 3CSE, Kalasalingam Academy of Research and Education Srivilliputhur, India [e-mail: brinthachris2k@gmail.com](e-mail: brinthachris2k@gmail.com) 4CSE, Sri Sairam Institute of Technology Chennai, India e-mail: [sit20cs033@sairamtap.edu.in](sit20cs033@sairamtap.edu.in) 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. Keywords-Ensemble Based Progressive Predictor, Naive Bayes, C4.5 Classification, Performance Prediction, Future Performance. I. INTRODUCTION Nowadays the performance of human beings is crucial in any sector as the world is moving towards robots for more performance. But before a decade human performance is not a concern in industries since the technology has not evolved in such a way that it can overcome humans in any aspect. It completely changed after the evolution of AI, IoT, etc. These technologies have the capability to overcome the performance of human beings in every aspect. So now it is a concern for humans in the aspect of their performance in their respective works. Hence, Machine Learning (ML) is proposed for this problem. With the help of ML, humans can able to know what will be their future performance in their work or respective fields. So, we develop an ML model that will predict the human’s future performance in an optimized and more accurate manner. It may give the wrong prediction for a few individuals, to overcome that we develop an EPP model that will be able to overcome the above errors. the applicable criteria that follow. II. APPROACHES OF PREDICTION OF HUMAN PERFORMANCES In today's world, optimizing human consumption performance efficiency is a pressing concern for state sanitary organizations and similar institutions dedicated to public health and safety. To address this critical need, we are currently in the process of developing a Machine Learning (ML) model tailored to predict and enhance the performance of various aspects related to human consumption. This initiative goes beyond the boundaries of a specific industry or sector, with the potential to benefit a wide range of organizations, including state sanitary organizations, etc. As our initial step, we have undertaken the development of a sophisticated ML model, known as the Ensembled base Progressive Predictor (EPP) model, which has been diligently trained using da

FAQs

What problem does the Efficiency Predictor address?

It focuses on forecasting human consumption efficiency performance when expected outcomes and success rates are not clearly known, helping reduce wasted time and effort.

Why does the project use an Ensemble-based Progressive Prediction (EPP) model?

The EPP model aims to improve accuracy and reduce prediction errors by leveraging ensemble-based progressive prediction trained on collected data.

How does the C4.5 algorithm contribute to the prediction method?

C4.5 builds decision trees using information entropy, selecting features at each node that maximize normalized information gain to separate samples into class-specific subsets.

Aug 3, 2026

Upload by Aurora