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sensors\n\n## Dataset\n Source: UCI ML Repository\u003c/br\u003e\n Human Activity Recognition Using Smartphones Data Set\u003c/br\u003e\n https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones#\n\n## Model\n\n* Dataset has 561 attributes so Principal Component Analysis(PCA) is used to reduce the dimension.\n* Best results are obtained by taking about 200 principal components. \n* Linear SVM(\"one vs one\") was used to classify the data\n\n## About Repository\n\n* ActivityRecognition.py --- script to pickle data\n* ActivityRecognition2.py --- classification script\n* HAR pca.png -- image showing 2 principal components of the data\n* TDT.png -- shows a plot of training, development and testing accuracies over number of principal components\n\n## Results\n\n* Training accuracy ~ 99%\n* Development or cross-validation accuracy ~ 98%\n* Testing accuracy ~ 95-96%\n* Most mis-classifications were obtained for standing and sitting classes as there is not quite of a difference between the 2 postures.\n\n## Future Scope\n\n* Neural networks can be tried for the dataset\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkpriyanshu256%2Fhumanactivityrecognition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkpriyanshu256%2Fhumanactivityrecognition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkpriyanshu256%2Fhumanactivityrecognition/lists"}