TrackML

Machine learning approaches for the particle tracking problem of the CERN Atlas detector.
EDA
Feature engineering
Feature selection (various types)
Investigation of Hough methods, Spherical hashing and RANSAC-based methods
TrackML on Kaggle

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
Dataset.ipynb		Dataset.ipynb
Distributions.ipynb		Distributions.ipynb
FE.ipynb		FE.ipynb
FS_DR.ipynb		FS_DR.ipynb
FS_DR_Changing_Features.ipynb		FS_DR_Changing_Features.ipynb
FS_DR_RFE.ipynb		FS_DR_RFE.ipynb
FS_Feature_Importances.ipynb		FS_Feature_Importances.ipynb
FS_Filter_Methods.ipynb		FS_Filter_Methods.ipynb
FS_RFE.ipynb		FS_RFE.ipynb
Hits_Viz.ipynb		Hits_Viz.ipynb
Hough.ipynb		Hough.ipynb
Hough_Triplets.ipynb		Hough_Triplets.ipynb
Hough_with_DBSCAN_Preprocessing.ipynb		Hough_with_DBSCAN_Preprocessing.ipynb
RANSAC.ipynb		RANSAC.ipynb
README.md		README.md
SH.ipynb		SH.ipynb
Scoring.ipynb		Scoring.ipynb
Trajectories_Viz.ipynb		Trajectories_Viz.ipynb
Volumes_Layers.ipynb		Volumes_Layers.ipynb

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