👾 Data Visualization using Streamlit

Content

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

Attaching Files to the Page
• Image
• Text
• Headers
• Videos
• Sounds
• Plots (i.e Matplotlib)

2. Full Project Showcase

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

Custom user input interaction to test on 
1) Iris Dataset
2) Breast Cancer Dataset
3) Wine Dataset
datasets

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

In collaboration with:

Global AI Hub	Teens in AI

License:

MIT License