Human Activity Recognition From Accelerometer Data Using Ensemble Learning Alternatives

This project aims to classify the human activities using ensemble learning method. In this project, we compared the recognition accuracy among different classifiers, visualized the data using seaborn library and t-SNE, and tuned the hyperparameters using grid search and k-fold cross-validation.
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This project aims to classify the human activities using ensemble learning method. In this project, we compared the recognition accuracy among different classifiers, visualized the data using seaborn library and t-SNE, and tuned the hyperparameters using grid search and k-fold cross-validation.
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