🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe with no detected network calls, shell executions, or credential risks. The metadata risk is slightly elevated due to the maintainer's lack of activity and sparse package information.

No network calls or shell executions detected
Maintainer has only one package and lacks detailed metadata

Per-check LLM notes

Network: No network calls detected, which is normal for many packages that don't require internet access.
Shell: No shell execution patterns detected, indicating the package does not execute system commands.
Obfuscation: The use of declare_namespace is likely for legitimate namespacing and not obfuscation.
Credentials: No credential harvesting patterns detected.
Metadata: The maintainer has a single package and lacks PyPI classifiers, indicating low effort or inactivity.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

__import__("pkg_resources").declare_namespace(__name__) # orangecontrib is a namespace

✓ 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: unizar.es

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Julio J. Melero" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with Orange3-Tools

Create a data analysis mini-app using the 'Orange3-Tools' Python package tailored for educational purposes in a Big Data course. Your app should enable users to load datasets, perform basic exploratory data analysis (EDA), apply machine learning models, and visualize the results. Here’s a detailed breakdown of the steps and features you need to implement:

1. **Setup**: Begin by setting up a Python environment with the necessary packages installed, including Orange3 and Orange3-Tools.
2. **Data Importation**: Allow users to upload CSV files as datasets into your app. Ensure that the app can handle different types of data formats commonly used in big data analysis.
3. **Exploratory Data Analysis (EDA)**: Implement functions that allow users to explore their data through descriptive statistics, visualizations such as histograms, scatter plots, and box plots. Use Orange3-Tools to enhance these visualizations and analyses with additional tools and widgets specifically designed for big data tasks.
4. **Feature Engineering**: Provide options for users to preprocess their data, which includes handling missing values, scaling numerical data, encoding categorical variables, etc., using functionalities provided by Orange3-Tools.
5. **Model Training & Evaluation**: Integrate machine learning capabilities where users can choose from a variety of models available in Orange3-Tools (e.g., classification, regression). Allow them to train these models on their preprocessed datasets and evaluate model performance using metrics like accuracy, precision, recall, F1-score, etc.
6. **Visualization of Results**: After training models, display predictions and model evaluation results in an easy-to-understand format. Utilize Orange3-Tools to generate interactive visualizations that help users interpret the results better.
7. **Documentation & User Interface**: Develop a user-friendly interface for your app, ensuring that all functionalities are accessible and clearly documented. Consider adding tooltips, help sections, or tutorials within the app to guide users through the process.
8. **Testing & Validation**: Finally, test your application thoroughly with different datasets to ensure reliability and accuracy of results. Validate the functionality of each feature against expected outcomes.

Your goal is to create a comprehensive tool that not only serves as an educational resource but also provides practical insights into big data analysis and machine learning. By leveraging the advanced features of Orange3-Tools, your app should stand out as a valuable asset for students and professionals alike.