markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping
Python implementation of KNN and DTW classification algorithm
This project helps classify human activities like sitting or walking based on smartphone sensor data. It takes in time-series data from accelerometers and gyroscopes and outputs a prediction of the activity being performed. This is useful for researchers or developers working on activity recognition applications.
791 stars. No commits in the last 6 months.
Use this if you need to classify time-series data, especially for human activity recognition from sensor streams.
Not ideal if your classification problem does not involve time-series data or if you need highly optimized, production-ready solutions for complex real-time applications.
Stars
791
Forks
212
Language
Jupyter Notebook
License
—
Category
Last pushed
Oct 03, 2018
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/markdregan/K-Nearest-Neighbors-with-Dynamic-Time-Warping"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
lmcinnes/umap
Uniform Manifold Approximation and Projection
pyRiemann/pyRiemann
Machine learning for multivariate data through the Riemannian geometry of positive definite...
geomstats/geomstats
Computations and statistics on manifolds with geometric structures.
higra/Higra
Hierarchical Graph Analysis
pavlin-policar/openTSNE
Extensible, parallel implementations of t-SNE