- This project tries to recognize types of body movements based on biometrics signals (linear acceleration, angular velocity, etc.) using supervised learning algorithms (Naive Bayes, SVM and Decision Trees)
- Uses data from a public data repository (UCI Machine Learning Repository): https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
- It's built on Python 2.7.10
- It's possible to install dependencies by running
pip install -r requirements.txt. If you don't havepip, you can visit this page - Now, you are can run the main script by tipping
python activityRecognition.pyon console
- Data pre-processing / Feature evaluation
- Feature computation
- Boxplots
- Dimensionality reduction
- Feature selection (Variance threshold)
- Feature extraction (PCA)
- Classification (Supervised learning)
- Stratified sampling
- Naive Bayes, SVC and Decision Tree