Best Machine Learning Projects
Machine learning projects are made easy with Takeoff Projects – a leading project provider for engineering students. We offer hands-on support and ready-to-use kits to help students build smart and practical solutions. From machine learning (ML) and AI to IoT and data science, Takeoff guide you at every single step. With Takeoff Projects, turning ideas into real, working projects becomes simple and effective.
Project Code: TCMAPY1848
Project Title:Hybrid Ai model for online payment fraud detection using machine learningView DetailsProject Code: TCMAPY1847
Project Title:Smart Industry Anomaly Detection Dataset for Cyber-Physical Threat Monitoring in Industrial IoT EnvironmentView DetailsProject Code: TCMAPY1817
Project Title:Thermally Aware Machine Learning for Unified and Energy-Efficient Resource Management in Cloud Data CentersView DetailsProject Code: TCMAPY1747
Project Title:Automated Root Cause Analysis of Network Failures in IPMPLS Network Using Machine Learning and Case-Based ReasoningView DetailsProject Code: TCMAPY1794
Project Title:Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological DisordersView DetailsProject Code: TCMAPY1767
Project Title:Conversational Predictive Maintenance Assistant for EV FleetView Details S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TCMAPY1858 | AI-Based Code Review Assistant | |
| 2 | TCMAPY1848 | Hybrid Ai model for online payment fraud detection using machine learn... | |
| 3 | TCMAPY1847 | Smart Industry Anomaly Detection Dataset for Cyber-Physical Threat Mon... | |
| 4 | TCMAPY1829 | QOS AWARE ROUTING IN SDN USING MACHINE LEARNING | |
| 5 | TCMAPY1817 | Thermally Aware Machine Learning for Unified and Energy-Efficient Reso... | |
| 6 | TCMAPY1747 | Automated Root Cause Analysis of Network Failures in IPMPLS Network Us... | |
| 7 | TCMAPY1794 | Exploring the Effectiveness of Machine Learning and Deep Learning Tech... | |
| 8 | TCMAPY1791 | ANALYSING AND DETECTING MONEYLAUNDERING ACCOUNTS | |
| 9 | TCMAPY1767 | Conversational Predictive Maintenance Assistant for EV Fleet | |
| 10 | TCMAPY1742 | Predicting customer purchase behaviour |
Project Code: TCMAPY1848
Project Title:Hybrid Ai model for online payment fraud detection using machine learningView DetailsProject Code: TCMAPY1847
Project Title:Smart Industry Anomaly Detection Dataset for Cyber-Physical Threat Monitoring in Industrial IoT EnvironmentView DetailsProject Code: TCMAPY1817
Project Title:Thermally Aware Machine Learning for Unified and Energy-Efficient Resource Management in Cloud Data CentersView DetailsProject Code: TCMAPY1747
Project Title:Automated Root Cause Analysis of Network Failures in IPMPLS Network Using Machine Learning and Case-Based ReasoningView DetailsProject Code: TCMAPY1794
Project Title:Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological DisordersView DetailsProject Code: TCMAPY1767
Project Title:Conversational Predictive Maintenance Assistant for EV FleetView Details S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TCMAPY1858 | AI-Based Code Review Assistant | |
| 2 | TCMAPY1848 | Hybrid Ai model for online payment fraud detection using machine learn... | |
| 3 | TCMAPY1847 | Smart Industry Anomaly Detection Dataset for Cyber-Physical Threat Mon... | |
| 4 | TCMAPY1829 | QOS AWARE ROUTING IN SDN USING MACHINE LEARNING | |
| 5 | TCMAPY1817 | Thermally Aware Machine Learning for Unified and Energy-Efficient Reso... | |
| 6 | TCMAPY1747 | Automated Root Cause Analysis of Network Failures in IPMPLS Network Us... | |
| 7 | TCMAPY1794 | Exploring the Effectiveness of Machine Learning and Deep Learning Tech... | |
| 8 | TCMAPY1791 | ANALYSING AND DETECTING MONEYLAUNDERING ACCOUNTS | |
| 9 | TCMAPY1767 | Conversational Predictive Maintenance Assistant for EV Fleet | |
| 10 | TCMAPY1742 | Predicting customer purchase behaviour |
Project Code: TCMAPY1848
Project Title:Hybrid Ai model for online payment fraud detection using machine learningView DetailsProject Code: TCMAPY1847
Project Title:Smart Industry Anomaly Detection Dataset for Cyber-Physical Threat Monitoring in Industrial IoT EnvironmentView DetailsProject Code: TCMAPY1817
Project Title:Thermally Aware Machine Learning for Unified and Energy-Efficient Resource Management in Cloud Data CentersView DetailsProject Code: TCMAPY1747
Project Title:Automated Root Cause Analysis of Network Failures in IPMPLS Network Using Machine Learning and Case-Based ReasoningView DetailsProject Code: TCMAPY1794
Project Title:Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological DisordersView DetailsProject Code: TCMAPY1767
Project Title:Conversational Predictive Maintenance Assistant for EV FleetView Details S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TCMAPY1858 | AI-Based Code Review Assistant | |
| 2 | TCMAPY1848 | Hybrid Ai model for online payment fraud detection using machine learn... | |
| 3 | TCMAPY1847 | Smart Industry Anomaly Detection Dataset for Cyber-Physical Threat Mon... | |
| 4 | TCMAPY1829 | QOS AWARE ROUTING IN SDN USING MACHINE LEARNING | |
| 5 | TCMAPY1817 | Thermally Aware Machine Learning for Unified and Energy-Efficient Reso... | |
| 6 | TCMAPY1747 | Automated Root Cause Analysis of Network Failures in IPMPLS Network Us... | |
| 7 | TCMAPY1794 | Exploring the Effectiveness of Machine Learning and Deep Learning Tech... | |
| 8 | TCMAPY1791 | ANALYSING AND DETECTING MONEYLAUNDERING ACCOUNTS | |
| 9 | TCMAPY1767 | Conversational Predictive Maintenance Assistant for EV Fleet | |
| 10 | TCMAPY1742 | Predicting customer purchase behaviour |
Related Projects
At Takeoff Edu Group, we offer hands-on machine learning projects for students to develop their skills and gain practical experience in the field of Machine Learning. Our projects are designed to challenge students and help them build a strong foundation in the subject.
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
