Data Science Platform

A comprehensive platform for data analysis, visualization, and machine learning with an Excel-inspired user interface.

Features

• Data Upload: Upload CSV or Excel files for analysis
• Data Profiling: Explore and understand your dataset with detailed statistics
• Data Cleaning: Clean and prepare your data with advanced tools
• Machine Learning: Train and evaluate machine learning models
• Data Visualization: Create interactive charts and visualizations
• Excel-Inspired UI: Familiar interface with Excel-like styling and interactions

Tech Stack

• Backend: FastAPI, Python, scikit-learn, pandas, numpy
• Frontend: Next.js, React, TypeScript, Tailwind CSS
• UI Design: Excel-inspired design system with custom CSS variables

Project Structure

datascience/
├── backend/               # FastAPI backend
│   ├── app/               # Application code
│   │   ├── routers/       # API routes
│   │   └── utils/         # Utility functions
│   ├── main.py            # Main application entry point
│   └── requirements.txt   # Python dependencies
│
└── frontend/              # Next.js frontend
    ├── public/            # Static assets
    └── src/               # Source code
        ├── app/           # Next.js app router
        ├── components/    # React components
        └── lib/           # Utility functions

Getting Started

Prerequisites

• Python 3.8+
• Node.js 18+
• npm or yarn

Backend Setup

1. Navigate to the backend directory:

   cd backend
   

2. Create a virtual environment:

   python -m venv venv
   

3. Activate the virtual environment:

   • Windows: venv\Scripts\activate
   • macOS/Linux: source venv/bin/activate

4. Install dependencies:

   pip install -r requirements.txt
   

5. Start the backend server:

   uvicorn main:app --reload
   

The API will be available at http://localhost:8000.

Frontend Setup

1. Navigate to the frontend directory:

   cd frontend
   

2. Install dependencies:

   npm install
   

3. Start the development server:

   npm run dev
   

The frontend will be available at http://localhost:3000.

Usage

1. Open the application in your browser at http://localhost:3000
2. Upload a CSV or Excel file on the home page
3. Navigate through the different sections to analyze and visualize your data
4. Clean your data and train machine learning models

Interactive Features

• Tooltips for additional information
• Hover effects on tables and interactive elements
• Excel-like data presentation and formatting

Note: Screenshots are placeholders. Replace with actual application screenshots.

License

This project is licensed under the MIT License - see the LICENSE file for details.