This repository includes transportation mode detection with unconstrained smartphones sensors using various Machine learning algorithms.
Dataset is based on the thirteen users who collected the data during their daily activities. The dataset includes all sensors available in phones and distinguishes five transportation modes: being on a car, on a bus, on a train, standing still and walking.
Sensors included in the dataset are:
- Accelerometer
- Sound
- Orientation
- Linear acceleration
- Speed
- Gyroscope
- Rotation vector
- Game rotation vector
- Gyroscope uncalibrated
Dataset link https://www.kaggle.com/fschwartzer/tmd-dataset-5-seconds-sliding-window
The following classification models are used:
- Random Forest
- lightGBM
- Gradient Boosting Classifier
- KNN
- Naivye Bayes
- SVC
- xgboost
| Model | Accuracy |
|---|---|
| Random Forest | 0.945 |
| lightGBM | 0.851 |
| Gradient Boosting Classifier | 0.963 |
| KNN | 0.821 |
| Naivye Bayes | 0.586 |
| SVC | 0.745 |
| xgboost | 0.973 |
Out of all the algorithms used, xgboost gives the best accuracy of 0.9737.
Good Luck!