ML Data Analysis Platform

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A demo for a machine learning data analysis platform – not overly powerful, but designed to allow users to train and evaluate common ML models without any coding. Simply upload your data, select a model, and get professional visualizations and metrics analysis in seconds. This demo isn't exhaustive, and we encourage users to modify it to suit their specific needs.

Features

• 🚀 No-code Interface: User-friendly interface for model selection and configuration
• 📊 Data Visualization: Automatically generates model performance visualizations
• 📈 Feature Analysis: Provides feature importance analysis
• 🔄 Multiple ML Models: Supports various classification and regression models
• 📁 Data Flexibility: Works with Excel (.xlsx, .xls) and CSV files
• 📱 Responsive Design: Works on both desktop and mobile devices

Getting Started

Prerequisites

• Developed with Python 3.12 and pip (other environments not tested)
• Modern web browser (Chrome, Firefox, Edge, etc.)

Installation

1. Clone the repository

   git clone https://github.com/somnifex/ml-data-analysis.git
   cd ml-data-analysis

2. Install the required Python packages

   pip install -r requirements.txt

3. Start the backend server

   cd backend
   python main.py

4. Open the frontend in your browser

   • Navigate to frontend/index.html in your file explorer and open it with your browser

   • Or run a simple HTTP server:

     cd frontend
     python -m http.server 8000

     Then visit http://localhost:8000 in your browser

Usage

1. Upload Data: Use the file upload button to upload your Excel or CSV data file
2. Select Model: Choose from various ML models depending on your task type
3. Configure Features: Select the feature columns and target variable
4. Train Model: Click the "Start Training" button and wait for results
5. Review Results: Explore the generated metrics and visualizations

Supported Models

Classification Models

• Neural Network Classifier
• Logistic Regression
• Random Forest Classifier
• Decision Tree Classifier
• Gradient Boosting Classifier
• Support Vector Machine Classifier
• K-Nearest Neighbors Classifier
• XGBoost Classifier (if installed)
• LightGBM Classifier (if installed)

Regression Models

• Neural Network Regressor
• Linear Regression
• Random Forest Regressor
• Decision Tree Regressor
• Gradient Boosting Regressor
• Support Vector Machine Regressor
• K-Nearest Neighbors Regressor
• Elastic Net Regression
• XGBoost Regressor (if installed)
• LightGBM Regressor (if installed)

Project Structure

ml-data-analysis/
├── backend/               # Backend API server
│   ├── main.py            # FastAPI server entry point
│   ├── models.py          # ML model implementations
│   └── processing.py      # Data processing utilities
├── frontend/              # Frontend web interface
│   ├── index.html         # Main HTML page
│   ├── script.js          # JavaScript logic
│   └── assets/            # Images and resources
└── gendata.py             # Data generation utility script

Optional: Generate Test Data

You can generate test data for various ML scenarios using the included script:

python gendata.py

This will create Excel files with synthetic data for classification, regression, and clustering tasks in a ml_test_data directory.

License

This project is licensed under the GNU General Public License v3.0 - see LICENSE file for details.

Acknowledgments

• Built with FastAPI
• Uses scikit-learn and PyTorch for machine learning
• Visualization powered by Matplotlib and Seaborn