Prediction Model projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Prediction Model projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Prediction Model Projects
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A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction
This project aims to improve the accuracy of financial distress prediction using a combination of network analysis and machine learning. It focuses on creating company networks based on financial similarities and correlations to better capture relationships between firms. From these networks, important structural features are extracted and added to the dataset. Community detection is used to group companies, helping to identify patterns linked to financial risk. Machine learning models are then trained and tested with both traditional and network-based features to enhance prediction performance. The study helps researchers understand how interconnected financial behavior influences company stability and supports better financial decision-making. -
Machine Learning Approaches for Power System Parameters Prediction – A Systematic Review
The main objective of this project is to improve prediction accuracy in power system networks. It aims to predict load, voltage, and frequency using machine learning models. The project focuses on using network topology behavior as input instead of isolated bus data. It tests different models like regression, decision tree, and LSTM. The goal is to provide better planning and reliability for dynamic and interconnected power systems. -
A Hybrid Predictive Model as an Emission Reduction Strategy Based on Power Plants Fuel Consumption Activity
This project creates a smart prediction system to help coal power plants in Indonesia estimate their future carbon emissions. It uses data and machine learning to predict how much coal will be used, how much electricity will be produced, and how much carbon will be released. These predictions help the plants plan better to reduce pollution and prepare for carbon trading. The system supports cleaner and more efficient energy management.
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