🧠 AI Autonomous Data Science Agent 🤖📊
🚀 Automating the Entire Machine Learning Pipeline with AI Agents
---🧩 Tech Badges
🚀 Live Demo
👉 Try the App Here
🔗 Frontend UI (Streamlit): https://ai-data-science-agent-ark4rgu5wc4qvnnwnzgkgx.streamlit.app/
🔗 Backend API (FastAPI): https://ai-data-science-agent-1.onrender.com/
📌 Project Overview
The AI Autonomous Data Science Agent is an intelligent system designed to automate the complete Machine Learning lifecycle.
In traditional workflows, data scientists manually perform tasks such as data cleaning, preprocessing, model selection, training, and evaluation. This process is time-consuming and repetitive.
This project introduces an AI-powered autonomous agent that handles the entire pipeline automatically, making data science faster, smarter, and more efficient.
It demonstrates how AI agents can reduce manual effort and enable developers to focus more on insights and decision-making.
✨ Key Features
🧠 AI-Powered Data Analysis Automatically generates insights from datasets
📂 Dataset Upload (CSV Support) Upload datasets easily for processing
⚙ Automated Data Preprocessing Handles missing values, encoding, scaling
🤖 Auto Model Selection & Training Chooses best models and trains automatically
📊 Model Evaluation Evaluates models using appropriate metrics
🏆 Best Model Selection Displays the top-performing model
🌐 Interactive Dashboard User-friendly UI with Streamlit
🔗 FastAPI Backend Integration Smooth frontend-backend communication
🛠️ Tech Stack
Technology| Purpose 🐍 Python| Core Programming ⚡ FastAPI| Backend API & Processing 🎨 Streamlit| Frontend UI 🤖 Generative AI| Intelligent Insights 📊 Scikit-Learn| ML Algorithms 🚀 XGBoost| High-performance Boosting Model
🏗️ Project Architecture
AI-DS-Agent │ ├── app.py # Streamlit Frontend ├── main.py # FastAPI Backend ├── requirements.txt ├── .env └── README.md
⚙️ Installation Guide
1️⃣ Clone Repository
git clone https://github.com/hari9618/AI-ds-agent.git cd ai-ds-agent
2️⃣ Install Dependencies
pip install -r requirements.txt
3️⃣ Setup Environment Variables
Create a ".env" file:
API_KEY=your_api_key_here
4️⃣ Run Backend
uvicorn main:app --reload
5️⃣ Run Frontend
streamlit run app.py
🧠 How It Works
1️⃣ Upload dataset (CSV) 2️⃣ AI analyzes the data 3️⃣ Preprocessing is applied automatically 4️⃣ Multiple ML models are trained 5️⃣ Models are evaluated 6️⃣ Best model is selected 7️⃣ Results displayed in UI
📷 Application Preview
![App Screenshot]
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📚 What I Learned
✔ Building Autonomous AI Systems ✔ End-to-End Machine Learning Pipelines ✔ FastAPI Backend Development ✔ Streamlit UI Development ✔ Generative AI Integration ✔ Real-world ML Automation
🎯 Future Improvements
🔹 Hyperparameter tuning (AutoML advanced) 🔹 Vector database integration 🔹 RAG-based insights 🔹 Real-time data streaming 🔹 Model explainability (SHAP, LIME) 🔹 Authentication system
🙏 Acknowledgements
🏫 Innomatics Research Labs For providing a strong learning environment
👨🏫 Manohar Chary V. Sir For guidance and continuous support
Special thanks to:
Raghu Ram Aduri Sir Kanav Bansal Sir Vishwanath Nyathani Sir Kalpana Katiki Reddy Ma’am
👨💻 Author
Hari Krishna AI Enthusiast | Gen AI Engineer | AI Builder
🔗 GitHub https://github.com/hari9618
⭐ Support
If you like this project:
⭐ Star the repository 📢 Share with others
📢 Tags
AI • Machine Learning • Data Science • FastAPI • Streamlit • Python • Generative AI • XGBoost • AI Agents