AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity based on the analysis of network, shell, obfuscation, and credential risks. The metadata suggests it might be from a less active or newer maintainer but does not indicate any suspicious behavior.
- No network calls detected
- No shell execution patterns
- No obfuscation patterns
- No credential harvesting patterns
Per-check LLM notes
- Network: No network calls detected, which is unusual but not necessarily indicative of malicious activity for a package like azureml-automl-core that might typically communicate with Azure services.
- Shell: No shell execution patterns detected, aligning with expectations for a standard Python library.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
- Metadata: The maintainer has only one other package, suggesting it may be a new or less active account, but no clear indicators of malicious activity.
Package Quality Overall: Low (2.4/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Brief PyPI description (224 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
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
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Microsoft Corp" 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 azureml-automl-core
Create a simple yet powerful data analysis and prediction tool using the Azure Machine Learning AutoML Core package in Python. This tool will allow users to upload datasets, perform exploratory data analysis (EDA), and automatically generate machine learning models to predict outcomes based on the uploaded data. Here’s a step-by-step guide on how to build this tool: 1. **Setup**: Start by setting up your development environment. Install necessary packages including `azureml-automl-core`, `pandas`, `numpy`, and `matplotlib` for data manipulation and visualization. 2. **Data Upload Interface**: Implement a feature where users can upload their CSV files containing datasets. Ensure the interface supports basic validation checks such as file type and size. 3. **Exploratory Data Analysis (EDA)**: Once the dataset is uploaded, provide basic EDA functionalities. This includes generating summary statistics, plotting histograms, scatter plots, and correlation matrices. Use `matplotlib` and `pandas` for these tasks. 4. **AutoML Model Generation**: Utilize the `azureml-automl-core` package to automate the process of model generation. Users should be able to select the target variable and specify whether they want to perform regression or classification tasks. The tool should then automatically preprocess the data, split it into training and testing sets, and train multiple models. 5. **Model Evaluation**: After model training, evaluate each model based on appropriate metrics (e.g., accuracy, AUC-ROC for classification; RMSE, MAE for regression). Display these evaluations in a user-friendly format. 6. **Prediction Interface**: Allow users to input new data points and use the best performing model from the previous step to predict outcomes. Provide visual feedback on the predictions made. 7. **Documentation and User Guide**: Finally, ensure all functionalities are well-documented. Create a user guide explaining how to use each feature effectively. By following these steps, you’ll develop a comprehensive tool that leverages the power of `azureml-automl-core` for automating complex tasks in machine learning, making it accessible even to those without deep expertise in ML.
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