Streamlit App for Data Visualization and Machine Learning

Features

1. Upload CSV File (Done)
2. Data Visualization (Ongoing)
3. Data Cleanup
4. Feature Selection (Basic Version Done)
5. Classification (Basic Version Done)

Initial Set Up

Using Docker

• Build the Container using Docker

  $ docker image build -t streamlit:app .
  

• Now, run the docker image on port 8501 (default for Streamlit Apps)

  $ docker container run -p 8501:8501 -d streamlit:app
  

Without Docker

• Create and Activate Virtual Environment using Conda

  $ conda create --name streamlit_app python=3.7
  $ source activate streamlit_app
  

  or venv

  $ python3 -m venv streamlit_app
  $ source streamlit_app/bin/activate
  

• Install the requirements.txt file

  $ pip install -r requirements.txt
  

• Run the app

  $ streamlit run app.py
  

Related Resources

• awesome-streamlit: List of Awesome Streamlit Apps
• Streamlit App Gallery: Official Streamlit Apps Gallery
• Best-of: Best-of lists with Python and other languages
• Best-of Streamlit: Best-of list of Streamlit Apps

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