-
Stars2
-
LanguagePython
-
Created over 5 years ago
-
Updated about 5 years ago
Reviews
There are no reviews yet. Be the first to send feedback to the community and the maintainers!
Repository Details
Machine learning project made by our team at Hacktech 2019, Caltech. Tracks the pose and orientation of the user's hand and compares it against a database of know signs. Integrating more leap-motion cameras and a user friendly interface! Also, building out the gesture/sign language library!
More Repositories
1
UCR-Time-Series-classification
To find the accuracy of the the UCR time series datasets, using kNN algorithm.Python
4
2
SAX-Implementation-on-UCR-TS
Implementing Symbolic Aggregate approXimation to find the error rate and accuracy on the UCR timeseries datasets (https://www.cs.ucr.edu/~eamonn/time_series_data/).Python
4
3
Time-Series-classification-using-feature-extraction
Using Dynamic time warping distances as features for improved time series classification. We use DTW distances between time series as features and then predict the labels.Python
3
4
Indian_liver_patient
The data set consists of the data of liver diseased patients from the Indian subcontinent, and this helps building a ML model using Random Forest and Logistic regression to predict the diseased.Jupyter Notebook
1
5
DTW-Example
Example of DTW algorithm's working.Jupyter Notebook
1
6
Sensor_SVM
Time series classification using DTW distances as a feature, LIBSVM and WEASEL methods, on the Robosensor data.Jupyter Notebook
1
7
Text-Analytics
Just a revision and my small experiments in Text Analytics, taught by Warwick Business School.
1
8
Error-rates-on-UCR-TS
To compute error rates on UCR time series Data sets and to compare them within. This has 1NN ED, 1NN DTW, 1NN DTW(r),BOSS,SAX,BOSSVS and WEASEL Algorithms.Python
1
9
OpenAI_PDF_reader
but this basically provides a way to access PDF(s). This also has some drawbacks for the unpaid version of the openai version of this API. So the code snippet can read the PDFs (Can get the text from Graphs too). Further, it cleans the text by following the classic sentiment analysis way to clean itJupyter Notebook
1