AI Analysis
Final verdict: SUSPICIOUS
The package is flagged as suspicious due to the unavailability of its repository and the maintainer having only one package, which could suggest a less established or potentially risky source.
- Repository not found
- Maintainer has a single package
Per-check LLM notes
- Network: The network call to pypi.org is likely for package metadata and version checking, which is common and not inherently suspicious.
- Shell: No shell execution patterns were detected, indicating no immediate risk from this aspect.
- Obfuscation: No obfuscation patterns detected in the package.
- Credentials: No credential harvesting patterns detected in the package.
- Metadata: The repository is not found, and the maintainer has a single package which may indicate a new or less active account.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
assert ( requests.get( "https://pypi.org/pypi/StratifiedGroupKFold
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: maximz.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 2.0
1 maintainer concern(s) found
Author "Maxim Zaslavsky" 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 StratifiedGroupKFoldRequiresGroups
Create a Python-based data science mini-application that predicts customer churn using telecom data. Your application should include several key components: 1. **Data Preprocessing**: Begin by loading a dataset of telecom customers, which includes features like customer ID, service usage details, contract type, payment method, and whether the customer churned or not. Clean the data by handling missing values and encoding categorical variables. 2. **Exploratory Data Analysis (EDA)**: Perform EDA to understand the distribution of churn across different segments of your data. Identify any patterns or correlations that might help in predicting churn. 3. **Model Training**: Use machine learning models such as Logistic Regression, Decision Trees, and Random Forests to predict churn. To ensure that your model training process is robust and avoids data leakage, utilize the 'StratifiedGroupKFoldRequiresGroups' package for cross-validation. This package will help you split your data into training and validation sets while preserving the stratification based on customer groups and ensuring that no customer appears in both the training and validation sets simultaneously. 4. **Feature Importance Analysis**: After training your models, analyze the importance of each feature in predicting churn. This could be done using feature importance scores from tree-based models or coefficients from logistic regression. 5. **Model Evaluation**: Evaluate your models using appropriate metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Also, generate confusion matrices and ROC curves to visualize the performance of your models. 6. **Deployment Considerations**: Although this is a mini-application, consider discussing how your final model could be deployed in a real-world scenario. Think about the infrastructure needed, API creation for predictions, and continuous monitoring of the model's performance. In your application, make sure to utilize the 'StratifiedGroupKFoldRequiresGroups' package effectively during the model training phase to ensure that the cross-validation process respects the group structure of the data and maintains stratification. This will lead to more reliable estimates of model performance.