Random Forest Algorithm Final Year Projects with Source Code

Byadmin Final Year Projects

Random Forest Algorithm Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Random Forest Algorithm 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.

Random Forest Algorithm 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. Applying One-Class Algorithms for Data Stream-Based Insider Threat Detection
    This project focuses on detecting insider threats, which are harmful actions by people who have authorized access to an organization’s network. Traditional security tools often fail, so the study uses machine learning to improve detection. It combines supervised and semi-supervised learning, analyzes data streams, and retrains models periodically. The best results were achieved using the Isolation Forest algorithm, showing good accuracy in identifying both harmful and safe activities.
  3. 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.
  4. Deep vs. Shallow A Comparative Study of Machine Learning and Deep Learning Approaches for Fake Health News Detection
    This project focuses on detecting fake health news on the internet. It compares two types of models: one that uses only the news text and another that also considers readability features. Different machine learning and deep learning methods were tested. The study found that using readability features improves detection, and the AdaBoost-Random Forest model gave the best results.
  5. Travel Direction Recommendation Model Based on Photos of User Social Network Profile
    This project creates a smart travel recommendation system using photos from a user’s social media account. It analyzes images and related data to suggest countries the user might like to visit. The system uses machine learning methods to compare, classify, and group data for accurate suggestions. Tests show it can correctly predict trips most of the time, and it can improve further by using photo location information.
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