Utilize this human activity dataset to detect individuals’ actions real time and estimate the calories burnt so as to make necessary changes.
dataset consists of accelerometer and gyroscope 3-axial raw signals readings from user smartphones that are placed in their waist while performing these 12 activities (6 basic activities: standing, sitting lying, walking, walking downstairs and walking upstairs and 6 postural transitions: stand-to-sit, sit-to-stand, sit-to-lie, lie-to-sit, stand-to-lie, lie-to-stand). The dataset has been pre-processed and converted from time series data into tabular data with 516 features and ~10.000 rows.
- scikit-learn
- pandas
- numpy
- matplotlib
- seaborn
- tensorflow
- factor_analyzer
- timeit
- imblearn
- input (Contain project dataset)
- Project.ipynb (Main notebook for RFE model)
- Neural Network.ippynb (Neural Network model train)
- Grouped-activites.ipynb (Notebook for dataset grouped into 4 activities)