---
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. 

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## ⚡ 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)

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## 🎬 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

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## 📊 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)


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## 🧠 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**

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## ⚡ Performance

- Parquet-based storage for faster I/O
- Sampling strategy for large datasets

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## 🛠️ System Design

- Command handler
- Dataset registry
- Modular agents (AnalysisAgent, etc.)
- Logger integration

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## 📦 Datasets

- Titanic
- Customer Churn
- Credit Card Fraud

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## 🛠️ Tech Stack

- Python  
- Pandas, NumPy  
- Scikit-learn  
- Parquet  

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## 🚀 Future Enhancements

- RAG-based EDA advisor  
- SQL query assistant  
- Model training pipeline