AI Analysis
Final verdict: SUSPICIOUS
The package shows no immediate signs of malicious activity, but the unavailability of its repository and the newness of its maintainer increase the risk.
- Repository not found
- Maintainer has limited history
Per-check LLM notes
- Network: No network calls detected, which is normal for a package focused on local data analysis.
- Shell: No shell execution patterns detected, suggesting the package does not execute external commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The repository is not found and the maintainer seems to be new with limited history, raising suspicion.
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: example.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Το Όνομά Σου" 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 Regression-Report-library-agr
Develop a user-friendly web application that allows users to upload datasets and train linear regression models using scikit-learn. The application should utilize the 'Regression-Report-library-agr' package to generate detailed reports on model performance. Here are the steps and features you need to implement: 1. **User Interface**: Design a clean and intuitive interface where users can upload CSV files containing their dataset. Ensure the UI supports file validation to ensure only CSV files are uploaded. 2. **Data Processing**: Implement functionality to handle missing values and categorical data within the uploaded datasets. Provide options for users to select which columns to use as features and which as the target variable. 3. **Model Training**: Use scikit-learn's Linear Regression model to train the dataset provided by the user. Display progress updates during the training process. 4. **Performance Report**: After training, utilize the 'Regression-Report-library-agr' package to generate a comprehensive report including metrics such as R^2, MSE, MAE, and RMSE. This report should be presented in a visually appealing format on the web app. 5. **Visualization**: Incorporate visualizations of the model's predictions versus actual values. Include scatter plots and line graphs to help users understand the model's accuracy. 6. **Export Options**: Allow users to export the generated report and visualizations as PDF or PNG files. 7. **Error Handling**: Implement robust error handling to manage issues like unsupported file formats, missing data, or model training failures. Provide clear error messages to guide users. 8. **Documentation**: Create comprehensive documentation explaining how to use the application, including examples and best practices for preparing datasets. The goal is to create a tool that not only trains models but also educates users about the importance of evaluating regression models thoroughly.