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GitHub / meszdav / human-activity-recognition

This project was created to challenge myself. My goal was to create a machine learning model, which can classify human activities based on smartphone sensors.

JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/meszdav%2Fhuman-activity-recognition

Stars: 0
Forks: 0
Open Issues: 11

License: mit
Language: Jupyter Notebook
Repo Size: 37.9 MB
Dependencies: 82

Created: almost 4 years ago
Updated: almost 4 years ago
Last pushed: over 1 year ago
Last synced: about 1 year ago

Files

Readme

Dependencies

Pipfile pypi

Pipfile.lock pypi

attrs ==19.3.0
backcall ==0.1.0
bleach ==3.1.5
certifi ==2020.4.5.1
chardet ==3.0.4
colorama ==0.4.3
colorlover ==0.3.0
cufflinks ==0.17.3
cycler ==0.10.0
decorator ==4.4.2
defusedxml ==0.6.0
entrypoints ==0.3
idna ==2.9
importlib-metadata ==1.6.0
ipykernel ==5.3.0
ipython ==7.14.0
ipython-genutils ==0.2.0
ipywidgets ==7.5.1
jedi ==0.17.0
jinja2 ==2.11.2
joblib ==0.15.1
jsonschema ==3.2.0
jupyter ==1.0.0
jupyter-client ==6.1.3
jupyter-console ==6.1.0
jupyter-core ==4.6.3
kiwisolver ==1.2.0
lxml ==4.5.1
markupsafe ==1.1.1
matplotlib ==3.2.1
mistune ==0.8.4
nbconvert ==5.6.1
nbformat ==5.0.6
notebook ==6.0.3
numpy ==1.18.4
packaging ==20.4
pandas ==1.0.3
pandocfilters ==1.4.2
parso ==0.7.0
pickleshare ==0.7.5
pillow ==7.1.2
plotly ==4.7.1
prometheus-client ==0.7.1
prompt-toolkit ==3.0.5
pygments ==2.6.1
pyparsing ==2.4.7
pyrsistent ==0.16.0
python-dateutil ==2.8.1
python-docx ==0.8.10
pytz ==2020.1
pywin32 ==227
pywinpty ==0.5.7
pyzmq ==19.0.1
qtconsole ==4.7.4
qtpy ==1.9.0
requests ==2.23.0
retrying ==1.3.3
scikit-learn ==0.23.1
scipy ==1.4.1
seaborn ==0.10.1
send2trash ==1.5.0
six ==1.15.0
terminado ==0.8.3
testpath ==0.4.4
threadpoolctl ==2.0.0
tornado ==6.0.4
traitlets ==4.3.3
urllib3 ==1.25.9
wcwidth ==0.1.9
webencodings ==0.5.1
widgetsnbextension ==3.5.1
zipp ==3.1.0