MST IASD 2023-2024 (Département Génie Informatique)
Groupe: Samady Ahmed - Chibani fahd - Daghmoumi Marouan
Sujet: Couche d’Ozone
Dataset: https://archive.ics.uci.edu/ml/datasets/Ozone+Level+Detection
Modèles: Support Vector Machines (SVM) avec ou sans utilisation de la PCA VS DT
Webographie:
- Zhang, K., Fan, W., & Yuan, X. (2008). Ozone Level Detection. UCI Machine Learning Repository. https://doi.org/https://doi.org/10.24432/C5NG6W
- Under-sampling — version 0.11.0. (n.d.). https://imbalanced-learn.org/stable/under_sampling.html#condensed-nearest-neighbors
- Over-sampling — version 0.11.0. (n.d.). https://imbalanced-learn.org/stable/over_sampling.html
- The flowchart of the SVM-SMOTE algorithm. (n.d.-b). ResearchGate. https://www.researchgate.net/figure/The-flowchart-of-the-SVM-SMOTE-algorithm_fig3_358509219
- EzzatEsam. (n.d.). EzzatEsam/SVM-Implementation-Python-QuadraticProgramming. GitHub. https://github.com/EzzatEsam/SVM-Implementation-Python-QuadraticProgramming/blob/master/svm_quad.ipynb
- Meng, Z. (2019). Ground ozone level prediction using machine learning. Journal of Software Engineering and Applications, 12(10), 423–431. https://doi.org/10.4236/jsea.2019.1210026
- Rocklen. (2021, April 19). Ozone level detection. Kaggle. https://www.kaggle.com/code/rocklen/ozone-level-detection