DataInsight

Transform Data into Decisions.

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🚀 DataInsight AI

AI-powered data cleaning, exploratory data analysis, intelligent insights, and report generation platform built with React, TypeScript, FastAPI, and Python.

React TypeScript FastAPI Python Vite Vercel Railway

🌐 Live Demo

Application:
👉 https://data-insight-jade.vercel.app

Backend API:
👉 https://datainsight-production.up.railway.app

API Documentation:
👉 https://datainsight-production.up.railway.app/docs

📖 Overview

DataInsight AI is a full-stack intelligent data analytics platform designed to simplify the process of exploring, cleaning, and understanding CSV datasets.

Users can upload datasets, perform automated data cleaning operations, generate exploratory data analysis (EDA), receive AI-powered insights, interact with their datasets through an AI assistant, and export professional reports.

The application is built with a modern React frontend and a FastAPI backend, providing a fast and scalable analytics experience.

✨ Features

📂 Dataset Management

  • Upload CSV datasets
  • Dataset versioning
  • Multiple dataset support
  • Dataset preview
  • Metadata management

🧹 Data Cleaning

  • Missing value imputation
  • Duplicate removal
  • Data type correction
  • Text cleaning
  • Outlier detection
  • Column operations
    • Rename
    • Delete
    • Create computed columns

📊 Exploratory Data Analysis

  • Dataset overview
  • Shape & memory usage
  • Missing value analysis
  • Data type summary
  • Descriptive statistics
  • Correlation matrix
  • Categorical analysis
  • Time-series detection
  • Data quality score

🤖 AI Insights

Generate intelligent insights including:

  • Trends
  • Correlations
  • Anomalies
  • Recommendations

💬 AI Chat Assistant

Interact with your dataset using natural language.

Example questions:

  • Which columns contain missing values?
  • What are the strongest correlations?
  • Explain the quality score.
  • Summarize the dataset.

📑 Report Generation

Export reports in:

  • CSV
  • PDF

🏗️ Tech Stack

Frontend

  • React 19
  • TypeScript
  • Vite
  • Tailwind CSS
  • Lucide React
  • Recharts

Backend

  • FastAPI
  • Python
  • SQLAlchemy
  • Pandas
  • SQLite
  • Uvicorn

Deployment

  • Vercel (Frontend)
  • Railway (Backend)

📁 Project Structure

DataInsight/
│
├── frontend/
│   ├── src/
│   ├── public/
│   └── package.json
│
├── backend/
│   ├── app/
│   │   ├── routers/
│   │   ├── services/
│   │   ├── models.py
│   │   └── main.py
│   │
│   ├── requirements.txt
│   └── Dockerfile
│
└── README.md

⚡ Getting Started

Clone Repository

git clone https://github.com/AyushSrivastava1818/DataInsight.git

cd DataInsight

Backend Setup

cd backend

python -m venv .venv

source .venv/bin/activate

Windows

.venv\Scripts\activate

Install dependencies

pip install -r requirements.txt

Run backend

uvicorn app.main:app --reload

Backend runs at

http://localhost:8000

Frontend Setup

cd frontend

npm install

npm run dev

Frontend runs at

http://localhost:5173

Environment Variables

Frontend

Create .env

VITE_API_URL=http://localhost:8000/api

Production

VITE_API_URL=https://datainsight-production.up.railway.app

Backend

Example .env

SECRET_KEY=your_secret_key

DATABASE_URL=sqlite:///./datainsight.db

SUPABASE_URL=your_supabase_url

SUPABASE_KEY=your_supabase_key

API Documentation

Interactive Swagger documentation is available at:

https://datainsight-production.up.railway.app/docs

Future Improvements

  • User workspaces
  • Dashboard analytics
  • Team collaboration
  • More AI models
  • Cloud storage integration
  • Advanced visualizations
  • Scheduled reports
  • Dark mode enhancements
  • User authentication with private workspaces
  • User-specific dataset history
  • One-click "Clear Analysis History"
  • Cloud storage for uploaded datasets
  • Export reports in multiple formats

Deployment

Frontend

  • Vercel

Backend

  • Railway

Contributing

Contributions are welcome.

  1. Fork the repository
  2. Create a feature branch
git checkout -b feature/amazing-feature
  1. Commit your changes
git commit -m "Add amazing feature"
  1. Push
git push origin feature/amazing-feature
  1. Open a Pull Request

License

This project is licensed under the MIT License.

Author

Ayush Srivastava

GitHub

https://github.com/AyushSrivastava1818

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