Welcome to our website where you will find a selection of data science projects in Python that are perfect for students who want to learn data science. Our projects are designed to be both challenging and educational, providing students with a hands-on learning experience in data science.
Project Code: TCPGPY1813
Project Title:Enhancing Phishing Detection A Machine Learning Approach with Feature Selection and Deep Learning ModelsView DetailsProject Code: TCPGPY1945
Project Title:HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical ThingsView DetailsProject Code: TCPGPY1944
Project Title:Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological DisordersView DetailsProject Code: TCPGPY1941
Project Title:Conversational Predictive Maintenance Assistant for EV FleetView DetailsProject Code: TCPGPY1940
Project Title:Automated Root Cause Analysis of Network Failures in IPMPLS Network Using Machine Learning and Case-Based ReasoningView DetailsProject Code: TCPGPY1938
Project Title:Kidney Tumor Detection in CT scans Combining 3D CNN and Transformer NetworksView DetailsProject Code: TCPGPY1936
Project Title:Comparative Analysis of Pixel-Based Segmentation Models for Accurate Detection of Impacted Teeth on Panoramic RadiographsView DetailsProject Code: TCPGPY1935
Project Title:PDF Knowledge Extraction System with RAG and CAG ModelsView DetailsProject Code: TCPGPY1934
Project Title:Shape Penalized Decision Forests for Imbalanced Data ClassificationView Details S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TCPGPY1813 | Enhancing Phishing Detection A Machine Learning Approach with Feature ... | |
| 2 | TCPGPY1945 | HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection Syste... | |
| 3 | TCPGPY1944 | Exploring the Effectiveness of Machine Learning and Deep Learning Tech... | |
| 4 | TCPGPY1942 | ANALYSING AND DETECTING MONEY LAUNDERING ACCOUNTS | |
| 5 | TCPGPY1941 | Conversational Predictive Maintenance Assistant for EV Fleet | |
| 6 | TCPGPY1940 | Automated Root Cause Analysis of Network Failures in IPMPLS Network Us... | |
| 7 | TCPGPY1938 | Kidney Tumor Detection in CT scans Combining 3D CNN and Transformer Ne... | |
| 8 | TCPGPY1936 | Comparative Analysis of Pixel-Based Segmentation Models for Accurate D... | |
| 9 | TCPGPY1935 | PDF Knowledge Extraction System with RAG and CAG Models | |
| 10 | TCPGPY1934 | Shape Penalized Decision Forests for Imbalanced Data Classification |
Project Code: TCPGPY1813
Project Title:Enhancing Phishing Detection A Machine Learning Approach with Feature Selection and Deep Learning ModelsView DetailsProject Code: TCPGPY1945
Project Title:HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection System for the Internet of Medical ThingsView DetailsProject Code: TCPGPY1944
Project Title:Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological DisordersView DetailsProject Code: TCPGPY1941
Project Title:Conversational Predictive Maintenance Assistant for EV FleetView DetailsProject Code: TCPGPY1940
Project Title:Automated Root Cause Analysis of Network Failures in IPMPLS Network Using Machine Learning and Case-Based ReasoningView DetailsProject Code: TCPGPY1938
Project Title:Kidney Tumor Detection in CT scans Combining 3D CNN and Transformer NetworksView DetailsProject Code: TCPGPY1936
Project Title:Comparative Analysis of Pixel-Based Segmentation Models for Accurate Detection of Impacted Teeth on Panoramic RadiographsView DetailsProject Code: TCPGPY1935
Project Title:PDF Knowledge Extraction System with RAG and CAG ModelsView DetailsProject Code: TCPGPY1934
Project Title:Shape Penalized Decision Forests for Imbalanced Data ClassificationView Details S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TCPGPY1813 | Enhancing Phishing Detection A Machine Learning Approach with Feature ... | |
| 2 | TCPGPY1945 | HIDS-IoMT: A Deep Learning-Based Intelligent Intrusion Detection Syste... | |
| 3 | TCPGPY1944 | Exploring the Effectiveness of Machine Learning and Deep Learning Tech... | |
| 4 | TCPGPY1942 | ANALYSING AND DETECTING MONEY LAUNDERING ACCOUNTS | |
| 5 | TCPGPY1941 | Conversational Predictive Maintenance Assistant for EV Fleet | |
| 6 | TCPGPY1940 | Automated Root Cause Analysis of Network Failures in IPMPLS Network Us... | |
| 7 | TCPGPY1938 | Kidney Tumor Detection in CT scans Combining 3D CNN and Transformer Ne... | |
| 8 | TCPGPY1936 | Comparative Analysis of Pixel-Based Segmentation Models for Accurate D... | |
| 9 | TCPGPY1935 | PDF Knowledge Extraction System with RAG and CAG Models | |
| 10 | TCPGPY1934 | Shape Penalized Decision Forests for Imbalanced Data Classification |
Each project comes with step-by-step instructions and well-commented source code that students can use as a starting point for their own data science projects in python. Takeoff Edu Group projects are designed to teach students how to use Python and its libraries, such as NumPy, Pandas, and Matplotlib, to perform data analysis and visualization.
Whether you are new to data science or already have some experience, these data science projects in python will help you develop a deeper understanding of data science concepts and techniques, and give you the confidence to tackle more complex data science problems.
Are You Looking for Data Science Projects in Python & Assistance?
We are always updating our collection with new data science projects in python, so make sure to check back often for updates. Our goal is to provide students with the resources they need to succeed in their data science journey.
Start your data science journey today by exploring our collection of data science projects in Python. Whether you're a beginner or an experienced data scientist, these projects will help you grow as a data scientist and give you a strong foundation for further learning and development.
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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.
