https://github.com/andrew2077/streamlit_ml

ML Classification using Sklearn and Streamlit
https://github.com/andrew2077/streamlit_ml

Last synced: about 5 hours ago
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ML Classification using Sklearn and Streamlit

• Host: GitHub
• URL: https://github.com/andrew2077/streamlit_ml
• Owner: Andrew2077
• Created: 2022-08-07T12:14:10.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-08-27T23:07:59.000Z (about 2 years ago)
• Last Synced: 2023-03-08T19:39:42.823Z (over 1 year ago)
• Language: Python
• Size: 4.19 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## **Streamlit ML Classification**

**[Streamlit](https://streamlit.io)** is an open source app framework in Python language. It helps us create web apps for data science and machine learning in a short time

Here is my Web App.V1.0

-- *open in a new tap [**Preview**](#preview)* --

give it a visit and explore how Machine learning classification algorithms works 

- Shared on & Hosted by Streamlit Cloud

---

## **Table Of Contents**

- [**Streamlit ML Classification**](#streamlit-ml-classification)

- [**Table Of Contents**](#table-of-contents)

- [**The Idea of the App**](#the-idea-of-the-app)

- [**Preview**](#preview)

- [**Deploying without Streamlit Cloud**](#deploying-without-streamlit-cloud)

- [**Features**](#features)

- [**Future improvements**](#future-improvements)

---

## **The Idea of the App**

Demonstrate the effectiveness of a machine learning model to classify data, while visualizing the results.

---

## **Preview**

![preview](preview.gif)

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## **Deploying without Streamlit Cloud**

- open **[mlwebapp](mlwebapp.py)** and run in terminal

- you will have to run it through Streamlit 

- write the following command in terminal after running 

    ```

    streamlit run mlwebapp.py

    ``` 

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## **Features**

- Select a model from a dropdown menu provided from a list of models that were trained using the [sklearn](https://scikit-learn.org/) library.

- Select a dataset from a dropdown menu provided from a list of datasets that were imported using sklearn.

- plot the the classified data using a scatter plot on a 2D plane, using the PCA algorithm. to reduce the dimensionality of the data.

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## **Future improvements**

- Adding more Parameters for Tuning the model _[under development]_

- Adding decision plot _[under development]_

- Adding a heatmap _[under development]_

- Adding an option for 3D plot _[under development]_

- Interactivity

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