AutoXEDA - Automated Exploratory Data Analysis (EDA)

🚀 Introduction

AutoXEDA is an advanced Python library designed for automated exploratory data analysis (EDA). It streamlines the data analysis process by providing detailed insights, business intelligence summaries, and AI-powered suggestions. Whether you are a data scientist, analyst, or business professional, AutoXEDA helps you make data-driven decisions with ease.

✨ Features

✅ Automated Statistical Analysis - Get key descriptive statistics with one function call.
✅ Business Insights - Generate structured insights tailored for decision-making.
✅ AI-Powered EDA - Leverages LLM-based models (via API key) for intelligent data summaries.
✅ Customizable Detail Levels - Choose from basic, intermediate, or detailed reports.
✅ Data Cleaning & Missing Values Handling - Detects and suggests fixes for data inconsistencies.
✅ Correlation & Distribution Analysis - Understand relationships between variables.
✅ Seamless Pandas Integration - Works with pandas DataFrames effortlessly.

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📌 Installation

AutoXEDA is available on PyPI and can be installed easily using pip:

pip install autoxeda

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🔥 Quick Start

Here's a quick example to get started with AutoXEDA:

import pandas as pd
from autoxeda.core import autoeda

# Create a sample dataset
data = {"x": [10, 20, 30, 40, 50], "y": [5, 15, 25, 35, 45]}
df = pd.DataFrame(data)

# Run AutoXEDA
result = autoeda(df, analysis_type="business", api_key=None, detail_level='basic')

# Print the output
print(result)

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📊 Example Output

{
    "status": "success",
    "summary": {
        "total_rows": 1000,
        "total_columns": 5,
        "missing_values": 0,
        "correlation_matrix": {...},
        "business_insights": "The data shows a positive trend..."
    }
}

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🛠️ Advanced Usage

1️⃣ Using AI-Powered Analysis

If you want to leverage AI-powered insights, provide an API key:

result = autoeda(df, analysis_type="business", api_key="your-api-key", temperature=0.5)

2️⃣ Custom Detail Levels

result = autoeda(df, analysis_type="business", detail_level='detailed')

3️⃣ Handling Large Datasets

If your dataset is large, you can optimize performance by sampling:

result = autoeda(df.sample(500), analysis_type="business", detail_level='basic')

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Hyperparameters

AutoXEDA allows customization through hyperparameters:

Parameter	Type	Default	Description
data	data	Required	Input data (DataFrame, CSV, or SQL query)
analysis_type	str	'business'	Type of analysis ('business' or 'prediction')
api_key	str	None	API key (Your API key from Groq)
max_retries	int	2	Number of retries for failed actions
columns	int	None (all columns)	Subset of columns to analyze
detail_level	str	'basic'	Level of detail ('basic', 'advanced' or 'intermediate')
temperature	float	1.0	LLM creativity level (0.0-1.0)

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📝 Contributing

We welcome contributions from the community! To contribute:

1. Fork the repository.
2. Clone it: git clone https://github.com/Jahanzeb-git/autoxeda.git
3. Create a new branch: git checkout -b feature-branch
4. Make your changes and commit: git commit -m "Add new feature"
5. Push the changes: git push origin feature-branch
6. Submit a pull request with a clear description of your contribution.

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📄 License

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for details.

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🌎 Connect with Me

• 📂 GitHub: Jahanzeb-git
• 🌐 Website: jahanzebahmed.com
• 💼 LinkedIn: Jahanzeb Ahmed

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⭐ If you like this project, don't forget to star it on GitHub! ⭐