🚀 Prism AI

An Intelligent Data Science Copilot for automated dataset profiling, quality analysis, and interactive data exploration.

Prism AI is a full-stack data analysis platform that helps users upload datasets, automatically profile data, identify quality issues, and explore insights through an intuitive web interface.

Built using FastAPI, React, and TypeScript, Prism AI simplifies exploratory data analysis by automating repetitive tasks such as profiling, missing value detection, duplicate analysis, and statistical summaries.

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✨ Features

📂 Dataset Management

• Upload CSV datasets
• Automatic dataset loading
• Dataset preview
• Dataset summary generation

📊 Data Profiling

• Automatic column profiling
• Dataset dimensions
• Data type detection
• Numerical and categorical feature identification
• Memory usage analysis

📈 Statistical Analysis

• Descriptive statistics
• Mean
• Median
• Standard deviation
• Minimum & Maximum values
• Unique value counts

🧹 Data Quality Analysis

• Missing value detection
• Missing value percentage
• Duplicate row detection
• Duplicate statistics
• Basic data quality recommendations

📊 Interactive Dashboard

• Dataset overview
• Summary cards
• Data quality metrics
• Profiling results
• Organized analytics interface

⚙ Backend

• REST API using FastAPI
• Modular backend architecture
• API documentation with Swagger (/docs)
• Structured service layer
• Scalable project organization

💻 Frontend

• React + TypeScript
• Vite
• Responsive interface
• Component-based architecture
• API integration with FastAPI

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🏗 Project Structure

prism-ai/
│
├── apps/
│   ├── api/          # FastAPI backend
│   └── web/          # React frontend
│
├── docker/
├── docs/
├── packages/
├── scripts/
│
├── LICENSE
└── README.md

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🛠 Tech Stack

Frontend

• React
• TypeScript
• Vite

Backend

• FastAPI
• Python

Data Analysis

• Pandas
• NumPy

Development Tools

• Git
• GitHub
• GitHub Actions (CI)

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/chsushen/PrismAI.git

cd PrismAI

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2. Backend Setup

cd apps/api

Install dependencies

pip install -r requirements.txt

Start the backend

uvicorn app.main:app --reload

Backend will run at

http://127.0.0.1:8000

API Documentation

http://127.0.0.1:8000/docs

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3. Frontend Setup

cd apps/web

Install packages

npm install

Run development server

npm run dev

Frontend will run at

http://localhost:5173

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📌 Current Capabilities

✔ Upload CSV datasets

✔ Automatic dataset profiling

✔ Statistical summaries

✔ Missing value analysis

✔ Duplicate detection

✔ Dataset overview dashboard

✔ FastAPI REST APIs

✔ React + TypeScript frontend

✔ GitHub Actions CI

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🎯 Roadmap

Future versions will include:

• User Authentication
• AI-powered conversational dataset assistant
• Automated report generation
• Interactive visualizations
• AutoML recommendations
• Model training workflows
• Cloud deployment
• Multi-file dataset support

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👨‍💻 Author

Chunduri Sushen

GitHub: https://github.com/chsushen

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⭐ Support

If you found this project useful, consider giving it a ⭐ on GitHub.