👾 Data Visualization using Streamlit Workshop by Global AI Hub

Syllabus:

1. Basics and General API construction of Streamlit Using Applied Samples & Examples

• Where, why, and how Streamlit is being used?
• Downloading and installing Streamlit on local devices
• Creating the first Streamlit environment
• Following the Streamlit Docs API to learn the basics on applied code and project

Attaching Various Following File Types to the Page:
• Image
• Text
• Headers
• Videos
• Sounds
• Plots (i.e Matplotlib)

2. Full Project Showcase

Using Following Classifiers:
1) KNN
2) SVM
3) Random Forest

Custom user input interaction to test on the following Datasets: 
1) Iris Dataset
2) Breast Cancer Dataset
3) Wine Dataset

to find the most optimal classifier arguments, which would get the best trained model output as a result.

How to Run

1. Creating the environment

1) Either create a virtual environment for your workspace
2) MacOS/Linux: $pip3 install -r requirements.txt
   Windows: $pip install -r requirements.txt

or

# MacOS/Linux:
$pip3 install -r requirements.txt

# Windows:
$pip install -r requirements.txt

2. Make sure you are in the correct path

Get into the according folder where the main.py is located in.

3. Run the app in localhost

streamlit run main.py

Certification

The workshop is certified.