autogluon.features

v1.5.0 safe
2.0
Low Risk

Fast and Accurate ML in 3 Lines of Code

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential risks detected. The metadata risk is slightly elevated due to the maintainer having only one package, but this alone is insufficient to conclude any malicious intent.

  • No network calls detected.
  • No shell execution patterns detected.
  • Maintainer has only one package.
Per-check LLM notes
  • Network: No network calls detected, which is normal for most data processing libraries.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
  • 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 could indicate a new or less active account but does not necessarily imply malicious intent.

📦 Package Quality Overall: Medium (7.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_check_style.py)
◈ 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

  • 194 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.features 
Create a small but impactful machine learning application using the 'autogluon.features' package. Your goal is to develop a tool that predicts house prices based on various features such as the number of bedrooms, bathrooms, square footage, and location. This application should be user-friendly and capable of handling both training and prediction phases seamlessly.

Steps to follow:
1. **Data Collection**: Use a publicly available dataset like the California Housing Prices dataset from Kaggle or UCI Machine Learning Repository. Ensure the data is preprocessed and cleaned before feeding it into your model.
2. **Feature Engineering**: Utilize 'autogluon.features' to automatically handle feature engineering tasks such as encoding categorical variables, scaling numerical features, and handling missing values. Explain how you leverage specific functionalities of the package for these tasks.
3. **Model Training**: Train your model using 'autogluon.features'. Discuss how you configure the model to optimize for accuracy while keeping computation time reasonable.
4. **Prediction Module**: Implement a simple user interface where users can input house attributes, and the app returns the predicted price. Ensure this module integrates smoothly with the trained model.
5. **Evaluation**: Provide a brief evaluation of your model's performance using metrics like RMSE (Root Mean Squared Error).

Suggested Features:
- Interactive data visualization to help understand the distribution and correlation between different features.
- A detailed explanation of each step in the process, including feature engineering and model training.
- User feedback on prediction accuracy and suggestions for improving the model.

How 'autogluon.features' is utilized:
- For preprocessing steps such as automatic detection and conversion of categorical variables, normalization of numerical data, and imputation of missing values.
- For simplifying the model training process by providing default configurations that work well across a variety of datasets.

💬 Discussion Feed

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