altastata

v0.1.35 suspicious
6.0
Medium Risk

A Python package for Altastata data processing and machine learning integration

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network calls, shell execution, and obfuscation. However, the presence of a suspicious non-HTTPS link and low activity on the repository and maintainer history raise concerns about its legitimacy.

  • Suspicious non-HTTPS link in metadata
  • Low activity on repository and maintainer history
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Suspicious non-HTTPS link and low activity on repository and maintainer history suggest potential risk.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (6392 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in sergevil/altastata-python-package
  • Single author but highly active (100 commits)

🔬 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

Email domain looks legitimate: altastata.com

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:9877
⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Serge Vilvovsky" 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 altastata 
Your task is to create a fully-functional mini-application called 'Altastata Data Explorer' that leverages the capabilities of the 'altastata' Python package. This tool will serve as a bridge between raw data from various sources and advanced machine learning models, providing users with an intuitive interface to explore, preprocess, and analyze their data.

### Step-by-Step Guide:
1. **Setup**: Begin by setting up your development environment with Python, pip, and the 'altastata' package installed. Ensure you have access to sample datasets relevant to common use cases such as finance, healthcare, or social media analysis.
2. **Data Importation**: Implement functionality within your app to import data from multiple sources, including CSV files, SQL databases, and APIs. Use 'altastata' functions to handle data loading efficiently and manage large datasets.
3. **Data Preprocessing**: Utilize 'altastata' preprocessing tools to clean and transform the imported data. This includes handling missing values, normalizing data, encoding categorical variables, and more. Ensure the process is user-friendly, allowing users to choose from predefined preprocessing steps or customize their own.
4. **Feature Engineering**: Integrate 'altastata' feature engineering capabilities to allow users to create new features based on existing data. This could involve time-series analysis, text processing, or any other relevant transformations. The goal is to enhance the predictive power of the data.
5. **Model Integration**: Incorporate machine learning model integration using 'altastata'. Users should be able to select from a variety of pre-trained models or upload their own custom models. The app should support common ML tasks like classification, regression, clustering, etc.
6. **Visualization**: Leverage 'altastata' visualization tools to display the results of data preprocessing and model predictions. Create interactive charts and graphs that help users understand the insights derived from their data.
7. **Reporting**: Allow users to generate reports summarizing their findings. Reports should include key metrics, visualizations, and explanations of the data analysis process. Enable users to save these reports in PDF or HTML formats.
8. **User Interface**: Develop a simple yet effective user interface where users can interact with all the functionalities mentioned above. Consider both web-based and command-line interfaces depending on the target audience.

### Suggested Features:
- **Customizable Workflow**: Allow users to define their own workflows for data processing and analysis.
- **Real-time Feedback**: Provide real-time feedback during the data preprocessing phase.
- **Collaboration Tools**: Enable users to share their projects and collaborate with others.
- **Model Versioning**: Support version control for models and datasets to track changes over time.

By following these steps and incorporating the suggested features, you'll create a powerful yet accessible tool for data exploration and analysis using the 'altastata' package.

💬 Discussion Feed

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