https://github.com/krithikahs/ml_visualizer

Interactive Machine Learning Playground An interactive Streamlit-based tool to train, visualize, and interpret machine learning models in real time.
https://github.com/krithikahs/ml_visualizer

matplotlib ml regression sklearn streamlit

Last synced: about 2 months ago
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Interactive Machine Learning Playground An interactive Streamlit-based tool to train, visualize, and interpret machine learning models in real time.

• Host: GitHub
• URL: https://github.com/krithikahs/ml_visualizer
• Owner: KrithikaHS
• Created: 2025-04-11T08:23:38.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-11T08:42:22.000Z (about 1 year ago)
• Last Synced: 2025-06-15T02:40:27.172Z (about 1 year ago)
• Topics: matplotlib, ml, regression, sklearn, streamlit
• Language: Python
• Homepage:
• Size: 3.91 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Interactive ML Model Visualizer

This project is a **Streamlit-based interactive machine learning visualizer** that allows users to explore different regression models with custom datasets. It's designed for learning, experimentation, and demonstration of how ML models behave with various inputs.

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## Features

- **Interactive Data Input**

  - Add (x, y) points manually

  - Upload CSV file with 'x' and 'y' columns

- **Regression Models Supported**

  - Linear Regression

  - Polynomial Regression

  - Decision Tree Regression

  - Random Forest Regression

  - Support Vector Regression (SVR)

  - K-Nearest Neighbors (KNN) Regression

- **Auto Train/Test Splitting**

  - Adjustable train/test ratio

- **Evaluation Metrics**

  - Mean Squared Error (MSE)

  - R² Score

- **Visual Output**

  - Scatter plot of training and test data

  - Model prediction curve

  - Residual plot (to analyze error distribution)

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## Technologies Used

- **Python**

- **Streamlit** for the frontend web interface

- **scikit-learn** for ML models

- **Matplotlib** for plotting