Python projects with source code
Extend your Python knowledge further through our Takeoff Projects. The Python projects with source code are created to help students real-time experience in-depth knowledge of programming principles. In every project, you are given the entire source code, which you can then analyze, change, and learn from. The wide-ranging subjects applications in web development, data analysis, generative AI, deep learning, computer vision and machine learning and as a result, Takeoff helps for both beginners and advanced learners. By working on python based projects students improve coding skills and also gain real-time experience which is very important. Through Takeoff Projects, you will evolve into a capable Python developer, ready to deal with intricate problems across various industries.
Project Code: TCPGPY420
Project Title:Discovering Knee Osteoarthritis using CNN enhanced With Alex NetView DetailsProject Code: TCPGPY439
Project Title:Machine Learning Model For Prediction of Smartphone AddictionView DetailsProject Code: TCPGPY438
Project Title:Medical Insurance Premium Prediction with Machine LearningView DetailsProject Code: TCPGPY437
Project Title:Analysis and Prediction of Crime Hotspots using Machine Learning with Stacked Generalization ApproachView DetailsProject Code: TCPGPY436
Project Title:Mosquito Species Classification Through Wingbeat Analysis: A Hybrid Machine Learning ApproachView DetailsProject Code: TCPGPY435
Project Title:Resampling Techniques for Enhanced Network Slice Classification in 5G Networks a SMOTE -Tomek PerspectiveView DetailsProject Code: TCPGPY434
Project Title:Machine Learning in Planetary Defense Early Warning Systems For Hazardous AsteroidsView DetailsProject Code: TCPGPY433
Project Title:Web Based Book Recommendation System using Collaborative FilteringView DetailsProject Code: TCPGPY432
Project Title:Theoretical Evaluation of Machine Learning Approaches For Hotel RecommendationView Details Items per page:
1 – 10 of 115
S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TCPGPY420 | Discovering Knee Osteoarthritis using CNN enhanced With Alex Net | |
| 2 | TCPGPY439 | Machine Learning Model For Prediction of Smartphone Addiction | |
| 3 | TCPGPY438 | Medical Insurance Premium Prediction with Machine Learning | |
| 4 | TCPGPY437 | Analysis and Prediction of Crime Hotspots using Machine Learning with ... | |
| 5 | TCPGPY436 | Mosquito Species Classification Through Wingbeat Analysis: A Hybrid Ma... | |
| 6 | TCPGPY435 | Resampling Techniques for Enhanced Network Slice Classification in 5G ... | |
| 7 | TCPGPY434 | Machine Learning in Planetary Defense Early Warning Systems For Hazard... | |
| 8 | TCPGPY433 | Web Based Book Recommendation System using Collaborative Filtering | |
| 9 | TCPGPY432 | Theoretical Evaluation of Machine Learning Approaches For Hotel Recomm... | |
| 10 | TCPGPY431 | Scientific Paper Recommendation system |
Items per page:
1 – 10 of 115
Project Code: TCPGPY420
Project Title:Discovering Knee Osteoarthritis using CNN enhanced With Alex NetView DetailsProject Code: TCPGPY439
Project Title:Machine Learning Model For Prediction of Smartphone AddictionView DetailsProject Code: TCPGPY438
Project Title:Medical Insurance Premium Prediction with Machine LearningView DetailsProject Code: TCPGPY437
Project Title:Analysis and Prediction of Crime Hotspots using Machine Learning with Stacked Generalization ApproachView DetailsProject Code: TCPGPY436
Project Title:Mosquito Species Classification Through Wingbeat Analysis: A Hybrid Machine Learning ApproachView DetailsProject Code: TCPGPY435
Project Title:Resampling Techniques for Enhanced Network Slice Classification in 5G Networks a SMOTE -Tomek PerspectiveView DetailsProject Code: TCPGPY434
Project Title:Machine Learning in Planetary Defense Early Warning Systems For Hazardous AsteroidsView DetailsProject Code: TCPGPY433
Project Title:Web Based Book Recommendation System using Collaborative FilteringView DetailsProject Code: TCPGPY432
Project Title:Theoretical Evaluation of Machine Learning Approaches For Hotel RecommendationView Details Items per page:
1 – 10 of 115
S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TCPGPY420 | Discovering Knee Osteoarthritis using CNN enhanced With Alex Net | |
| 2 | TCPGPY439 | Machine Learning Model For Prediction of Smartphone Addiction | |
| 3 | TCPGPY438 | Medical Insurance Premium Prediction with Machine Learning | |
| 4 | TCPGPY437 | Analysis and Prediction of Crime Hotspots using Machine Learning with ... | |
| 5 | TCPGPY436 | Mosquito Species Classification Through Wingbeat Analysis: A Hybrid Ma... | |
| 6 | TCPGPY435 | Resampling Techniques for Enhanced Network Slice Classification in 5G ... | |
| 7 | TCPGPY434 | Machine Learning in Planetary Defense Early Warning Systems For Hazard... | |
| 8 | TCPGPY433 | Web Based Book Recommendation System using Collaborative Filtering | |
| 9 | TCPGPY432 | Theoretical Evaluation of Machine Learning Approaches For Hotel Recomm... | |
| 10 | TCPGPY431 | Scientific Paper Recommendation system |
Items per page:
1 – 10 of 115
Project Code: TCPGPY420
Project Title:Discovering Knee Osteoarthritis using CNN enhanced With Alex NetView DetailsProject Code: TCPGPY439
Project Title:Machine Learning Model For Prediction of Smartphone AddictionView DetailsProject Code: TCPGPY438
Project Title:Medical Insurance Premium Prediction with Machine LearningView DetailsProject Code: TCPGPY437
Project Title:Analysis and Prediction of Crime Hotspots using Machine Learning with Stacked Generalization ApproachView DetailsProject Code: TCPGPY436
Project Title:Mosquito Species Classification Through Wingbeat Analysis: A Hybrid Machine Learning ApproachView DetailsProject Code: TCPGPY435
Project Title:Resampling Techniques for Enhanced Network Slice Classification in 5G Networks a SMOTE -Tomek PerspectiveView DetailsProject Code: TCPGPY434
Project Title:Machine Learning in Planetary Defense Early Warning Systems For Hazardous AsteroidsView DetailsProject Code: TCPGPY433
Project Title:Web Based Book Recommendation System using Collaborative FilteringView DetailsProject Code: TCPGPY432
Project Title:Theoretical Evaluation of Machine Learning Approaches For Hotel RecommendationView Details Items per page:
1 – 10 of 115
S.no | Project Code | Project Name | Action |
|---|---|---|---|
| 1 | TCPGPY420 | Discovering Knee Osteoarthritis using CNN enhanced With Alex Net | |
| 2 | TCPGPY439 | Machine Learning Model For Prediction of Smartphone Addiction | |
| 3 | TCPGPY438 | Medical Insurance Premium Prediction with Machine Learning | |
| 4 | TCPGPY437 | Analysis and Prediction of Crime Hotspots using Machine Learning with ... | |
| 5 | TCPGPY436 | Mosquito Species Classification Through Wingbeat Analysis: A Hybrid Ma... | |
| 6 | TCPGPY435 | Resampling Techniques for Enhanced Network Slice Classification in 5G ... | |
| 7 | TCPGPY434 | Machine Learning in Planetary Defense Early Warning Systems For Hazard... | |
| 8 | TCPGPY433 | Web Based Book Recommendation System using Collaborative Filtering | |
| 9 | TCPGPY432 | Theoretical Evaluation of Machine Learning Approaches For Hotel Recomm... | |
| 10 | TCPGPY431 | Scientific Paper Recommendation system |
Items per page:
1 – 10 of 115
Related Projects
Takeoff Projects for the best Python projects and codes! Here are the projects of our collection: data analysis, web scraping, machine learning, and automation. No matter if you are a newcomer or an advanced coder you will be provided with a vast amount of clear and documented code which will allow you to make progress and hone your practical experience. Every project offers specific guidelines, therefore, the Solutions are easy to incorporate and adapt. Polish your coding knowledge and develop relevant applications with the most suitable Python projects listed here. Visit Takeoff Edu Group today to get the best source code that will help elevate your programming.
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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, and not projects of or by IEEE.
