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An interactive Streamlit-based ML Model Simulator with modular pages for Linear Regression, Logistic Regression, and more. Adjust parameters, visualize results, and contribute new models easily.

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🧠 ML Simulator

ML Simulator is an interactive web application built with Streamlit that allows users to visualize, train, and understand popular Machine Learning algorithms in a simple and intuitive way.

This project aims to make machine learning hands-on and visual for students and developers. Users can adjust model parameters, visualize decision boundaries, and compare results — all within a browser.


🚀 Features

✅ Interactive simulation of ML algorithms
✅ Adjustable hyperparameters for each model
✅ Visualization of predictions and decision boundaries
✅ Evaluation metrics including Confusion Matrix and ROC Curve
✅ Clean and modular code structure
✅ Docker support for easy deployment


🧩 Supported Algorithms

Category Algorithms
Classification Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN)
Regression Linear Regression, Polynomial Regression
Clustering K-Means
Metrics Confusion Matrix, ROC Curve, Accuracy, AUC

🧰 Tech Stack

  • Python 3.x
  • Streamlit
  • Scikit-learn
  • Matplotlib
  • Seaborn
  • Pandas & NumPy
  • Docker (optional)

🏗️ Project Structure


⚙️ Installation & Setup

🔹 Prerequisites

  • Python 3.8+
  • pip installed

🔹 Steps to run locally

# Clone your forked repo
git clone https://github.com/<your-username>/ML-Simulator.git

# Move into the project folder
cd ML-Simulator

# Install dependencies
pip install -r requirements.txt

# Run the app
streamlit run app.py


## 🤝 How to Contribute

We welcome all contributors to make **ML-Simulator** better! 💻✨  
Follow these simple steps to contribute:

### 🪜 Steps to Contribute

1. **Fork this repository**  
   - Click the “Fork” button at the top-right of the page to create your copy of the repo.

2. **Clone your forked repository**
   ```bash
   git clone https://github.com/<your-username>/ML-Simulator.git

3. **Move into the project folder**
   ```bash
   cd ML-Simulator

4. **Create a new branch for your feature or fix**
   ```bash
   git checkout -b your-branch-name

5. **Make your Changes**

6. **Stage and Commit your Changes**
   ```bash
   git add .
   git commit -m "describe your change here"

7. **Push your branch to GitHub**
   ```bash
   git push origin your-branch-name

6. **Open a Pull Request (PR)**
   Go to your fork on GitHub → Click “Compare & Pull Request”
   Add a clear title and description of what you changed.
   Submit the PR for review ✅

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An interactive Streamlit-based ML Model Simulator with modular pages for Linear Regression, Logistic Regression, and more. Adjust parameters, visualize results, and contribute new models easily.

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