autogluon.tabular

v1.5.0 safe
2.0
Low Risk

Fast and Accurate ML in 3 Lines of Code

🤖 AI Analysis

Final verdict: SAFE

The package autogluon.tabular v1.5.0 presents a very low risk based on the provided analysis notes. There are no indications of malicious activities such as network calls, shell executions, obfuscation, or credential harvesting.

  • No network calls detected.
  • No shell executions detected.
  • No signs of obfuscation or credential harvesting.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a typical machine learning library like autogluon.tabular.
  • Shell: No shell executions detected, consistent with the expected behavior of a legitimate machine learning package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account but does not necessarily imply malicious intent.

📦 Package Quality Overall: Medium (6.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://auto.gluon.ai
  • Detailed PyPI description (10024 chars)
◈ Medium Contributing Guide 7.0

Some contribution signals present

  • Contributing link: "Contribute!" -> https://github.com/autogluon/autogluon/blob/master/CONTRIBUT
  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 116 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 18 unique contributor(s) across 100 commits in autogluon/autogluon
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository autogluon/autogluon appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AutoGluon Community" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with autogluon.tabular 
Create a predictive maintenance tool using the 'autogluon.tabular' package in Python. This tool will help predict potential failures in machinery based on historical data. The application should allow users to upload a CSV file containing various sensor readings and other operational data from machines over time. The tool will then use 'autogluon.tabular' to train a machine learning model to predict whether a machine is likely to fail within a given timeframe. Here are the key steps and features for this project:

1. **Data Upload**: Implement a simple UI or command-line interface where users can upload their dataset.
2. **Data Preprocessing**: Automatically handle missing values, categorical encoding, and feature scaling using 'autogluon.tabular'.
3. **Model Training**: Train multiple models using 'autogluon.tabular' with minimal code, focusing on accuracy and speed.
4. **Prediction Interface**: Develop a user-friendly interface or API endpoint to input new data points and receive predictions about potential failures.
5. **Visualization**: Include visualizations showing the model's performance metrics and prediction results.
6. **Documentation and Deployment**: Provide clear instructions for deploying the tool as a web app or locally. Use 'autogluon.tabular' documentation as a reference to ensure the project is self-contained and easy to understand.

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