Machine Learning Algorithms Final Year Projects with Source Code

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Machine Learning Algorithms Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Machine Learning Algorithms projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Machine Learning Algorithms Final Year Projects

  1. A Novel Machine Learning Approach for Android Malware Detection Based on the Co-Existence of Features
    This project focuses on detecting Android malware using machine learning. It looks at how certain permissions and app actions appear together in malicious apps compared to normal ones. The researchers created special datasets of these feature combinations and used algorithms to find the most important patterns. Their model was able to identify malware with very high accuracy, even better than existing methods.
  2. A Signature Transform of Limit Order Book Data for Stock Price Prediction
    This project focuses on predicting stock prices using advanced machine learning techniques. It takes detailed order book data from stock exchanges and extracts key patterns called signature features. These features are then used to train models like deep neural networks and random forests. The results show that using signature features improves prediction accuracy and efficiency, especially in developed markets.
  3. Automated Stroke Prediction Using Machine Learning An Explainable and Exploratory Study With a Web Application for Early Intervention
    This project focuses on predicting strokes using machine learning. The researchers developed a system that can identify people at risk early, which may help save lives. They tested several models and found that more advanced ones achieved up to 91% accuracy. They also used techniques to explain how these models make decisions, making the predictions more understandable for medical professionals.
  4. Constructing a Meta-Learner for Unsupervised Anomaly Detection
    This project focuses on automatically picking the best anomaly detection method for any given dataset without needing labeled data. The researchers created a meta-learning system that looks at characteristics of the dataset to suggest the most suitable algorithm. They tested it on over 10,000 datasets and found it works better than existing methods. The study also shows that while a few dataset features are enough, the choice of the meta-learning model strongly affects performance.
  5. Clinical Errors From Acronym Use in Electronic Health Record A Review of NLP-Based Disambiguation Techniques
    This study looks at how medical records often contain confusing acronyms that can cause errors in patient care. It explains why electronic health records (EHRs) sometimes increase mistakes and why understanding these acronyms is important. The research also explores how artificial intelligence, especially machine learning, can help automatically clarify the meaning of these acronyms. Finally, it reviews how EHRs are used worldwide and the latest AI methods for reducing errors caused by unclear medical terms.
  6. An Intelligent Approach to Improving the Performance of Threat Detection in IoT
    This project focuses on making Internet of Things (IoT) systems more secure. It uses machine learning and data analysis techniques to detect attacks that try to overwhelm the system, known as DDoS attacks. The researchers tested their approach using real datasets and measured how well the system could detect attacks and how fast it could learn. Overall, their method improved both detection accuracy and training speed.
  7. Application of Text Rank Algorithm Fused With LDA in Information Extraction Model
    The project builds a new model that can find important keywords from large amounts of text more accurately. It mixes two types of text analysis methods to improve the results. Tests show that this new model finds the correct keywords more often than older models. This helps make information extraction faster and more reliable.
  8. A Hybrid Method of Feature Extraction for Signatures Verification Using CNN and HOG a Multi-Classification Approach
    This project focuses on verifying handwritten signatures using images. The researchers combined two methods to extract important features from signatures. They then used a feature selection algorithm to pick the most useful ones. Finally, three machine learning models tested the system, achieving high accuracy in detecting both real and skilled fake signatures.
  9. The Role of Artificial Intelligence in Future Rehabilitation Services A Systematic Literature Review
    This project reviews how artificial intelligence can help provide rehabilitation services remotely. It looks at machine learning methods that track movements, recognize activities, and predict patient status. The study highlights technologies that monitor patients without being intrusive. It also points out that more testing is needed to ensure these tools work reliably for different patients at home.
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