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---

title: EDA Explorer
emoji: 📊
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.16.0
python_version: "3.10"
app_file: app.py
pinned: false
---

# 🚀 EDA Explorer – AI-Powered Data Analysis CLI

A lightweight CLI tool that automates exploratory data analysis (EDA) with intelligent insights, feature importance detection, and data quality checks.

Designed to simulate how an **AI Data Analyst** works on real-world datasets used in EDA. 

---

## ⚡ Key Highlights

- 🔍 One-command analysis → `analyze <dataset>`
- 🧠 Auto target detection for ML-based insights
- 📈 Feature importance (no manual setup)
- ⚠️ Smart data warnings (missing, ID columns, constants)
- 📊 Correlation & outlier detection
- 📁 Auto report generation (.txt)
- ⚡ Efficient handling of large datasets (Parquet + sampling)

---

## 🎬 Demo

👉 Full demo: https://github.com/user-attachments/assets/7dff8329-71e8-4bca-ad01-404e75df8314

https://github.com/user-attachments/assets/7dff8329-71e8-4bca-ad01-404e75df8314

---

## 📊 Example Output

Top Correlations:
- age ↔ income: 0.72
- tenure ↔ balance: 0.65

⚠️ Data Warnings:
- customer_id → likely ID column

- income → 52% missing values



📈 Feature Importance:

- age: 0.41 (strong signal)

- tenure: 0.32 (strong signal)





---



## 🧠 What Makes It Stand Out



- Automatically identifies **useful vs irrelevant features**

- No manual preprocessing required

- Mimics real-world **data analyst reasoning**

- Built using a **modular agent-based system**



---



## ⚡ Performance



- Parquet-based storage for faster I/O

- Sampling strategy for large datasets



---



## 🛠️ System Design



- Command handler

- Dataset registry

- Modular agents (AnalysisAgent, etc.)

- Logger integration



---



## 📦 Datasets



- Titanic

- Customer Churn

- Credit Card Fraud



---



## 🛠️ Tech Stack



- Python  

- Pandas, NumPy  

- Scikit-learn  

- Parquet  



---



## 🚀 Future Enhancements



- RAG-based EDA advisor  

- SQL query assistant  

- Model training pipeline