Data Mining projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Data Mining 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 Data Mining Projects
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Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling – A Critical Review
The project aims to predict chronic diseases early using machine learning. It focuses on improving medical data quality by handling missing values, outliers, feature selection, normalization, and imbalance. The goal is to choose the best machine learning methods that give high accuracy and reliability. The study also reviews existing research and highlights challenges and future directions for better prediction performance. -
Masked Modeling-Based Ultrasound Image Classification via SelfSupervised Learning
This project uses artificial intelligence to improve how ultrasound images are analyzed. It teaches a computer to understand images without needing human labeling. The system learns by filling in missing parts of images, helping it recognize patterns more effectively. This method makes ultrasound image classification more accurate, even when the images are unclear or noisy. -
Unleashing Competitive Intelligence: News Mining Analysis on Technology Trends and Digital Health Driving Healthcare Innovation
This project focuses on helping healthcare businesses understand trends in digital health using technology. It collects information from public news and event data. The system uses language processing to find important topics and connections in the data. This helps companies make better decisions and keep up with new developments like telehealth or precision medicine. -
A Semi-Supervised Learning Approach to Quality-Based Web Service Classification
This project focuses on helping users choose the best web service by using machine learning. It introduces a smart system that can learn even when only a small amount of data is labeled. The system automatically classifies and ranks services based on quality. As a result, it improves accuracy and performance compared to traditional methods.
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