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GitHub / hsinlichu / Customer-Service-Data-Analysis-with-Machine-Learning-Technique
In this project, I use several machine learning technique both supervised and unsupervised to analyze Cyberlink customer service feedback data.
Stars: 1
Forks: 1
Open Issues: 16
License: None
Language: Jupyter Notebook
Repo Size: 18.9 MB
Dependencies:
132
Created: almost 5 years ago
Updated: over 1 year ago
Last pushed: over 1 year ago
Last synced: about 1 year ago
Topics: bert-model, clustering-algorithm, lda-model, machine-learning, nlp-machine-learning
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Dependencies
requirements.txt
pypi
- CherryPy ==18.1.2
- GPUtil ==1.4.0
- Jinja2 ==2.10.1
- Keras-Applications ==1.0.8
- Keras-Preprocessing ==1.1.0
- Markdown ==3.1.1
- MarkupSafe ==1.1.1
- MulticoreTSNE ==0.1
- Pillow ==6.2.0
- Pygments ==2.4.2
- Send2Trash ==1.5.0
- Werkzeug ==0.15.5
- absl-py ==0.7.1
- astor ==0.8.0
- atomicwrites ==1.3.0
- attrs ==19.1.0
- autocorrect ==0.3.0
- backcall ==0.1.0
- backports.csv ==1.0.7
- backports.functools-lru-cache ==1.5
- beautifulsoup4 ==4.8.0
- bert-serving-client ==1.9.6
- bert-serving-server ==1.9.6
- bleach ==3.1.0
- boto ==2.49.0
- boto3 ==1.9.197
- botocore ==1.12.197
- certifi ==2019.6.16
- cffi ==1.12.3
- chardet ==3.0.4
- cheroot ==6.5.5
- colorama ==0.4.1
- cycler ==0.10.0
- decorator ==4.4.0
- defusedxml ==0.6.0
- docutils ==0.14
- entrypoints ==0.3
- feedparser ==5.2.1
- funcy ==1.13
- future ==0.17.1
- gast ==0.2.2
- gensim ==3.8.0
- google-pasta ==0.1.7
- grpcio ==1.22.0
- h5py ==2.9.0
- idna ==2.8
- importlib-metadata ==0.19
- ipykernel ==5.1.1
- ipython ==7.6.0
- ipython-genutils ==0.2.0
- ipywidgets ==7.5.0
- jaraco.functools ==2.0
- jedi ==0.14.0
- jmespath ==0.9.4
- joblib ==0.13.2
- jsonschema ==3.0.1
- jupyter ==1.0.0
- jupyter-client ==5.2.4
- jupyter-console ==6.0.0
- jupyter-core ==4.5.0
- kiwisolver ==1.1.0
- langid ==1.1.6
- lxml ==4.3.4
- matplotlib ==3.1.1
- mistune ==0.8.4
- more-itertools ==7.2.0
- mysqlclient ==1.4.2.post1
- nbconvert ==5.5.0
- nbformat ==4.4.0
- nltk ==3.4.5
- notebook ==5.7.8
- numexpr ==2.6.9
- numpy ==1.16.4
- packaging ==19.1
- pandas ==0.24.2
- pandocfilters ==1.4.2
- parso ==0.5.0
- pdfminer.six ==20181108
- pexpect ==4.7.0
- pickleshare ==0.7.5
- pluggy ==0.12.0
- portend ==2.5
- prometheus-client ==0.7.1
- prompt-toolkit ==2.0.9
- protobuf ==3.9.0
- ptyprocess ==0.6.0
- py ==1.8.0
- pyLDAvis ==2.1.2
- pycparser ==2.19
- pycryptodome ==3.8.2
- pyparsing ==2.4.0
- pyrsistent ==0.15.2
- pytest ==5.0.1
- python-dateutil ==2.8.0
- python-docx ==0.8.10
- pytils ==0.3
- pytz ==2019.1
- pywinpty ==0.5.5
- pyzmq ==18.0.2
- qtconsole ==4.5.1
- regex ==2019.6.8
- requests ==2.22.0
- s3transfer ==0.2.1
- scikit-learn ==0.21.2
- scipy ==1.3.0
- seaborn ==0.9.0
- six ==1.12.0
- sklearn ==0.0
- smart-open ==1.8.4
- sortedcontainers ==2.1.0
- soupsieve ==1.9.2
- tempora ==1.14.1
- tensorboard ==1.14.0
- tensorboardX ==1.2
- tensorflow-estimator ==1.14.0
- tensorflow-gpu ==1.14.0
- termcolor ==1.1.0
- terminado ==0.8.2
- testpath ==0.4.2
- torch ==1.1.0
- torchvision ==0.3.0
- tornado ==6.0.3
- tqdm ==4.32.2
- traitlets ==4.3.2
- urllib3 ==1.25.3
- wcwidth ==0.1.7
- webencodings ==0.5.1
- widgetsnbextension ==3.5.0
- wrapt ==1.11.2
- xlrd ==1.2.0
- zc.lockfile ==1.4
- zipp ==0.5.2