AI Analysis
Final verdict: SUSPICIOUS
The package shows low individual risks across various categories, but the metadata risk raises some concern due to the maintainer's single package history.
- Low individual risks in network, shell, obfuscation, and credential handling.
- Metadata risk due to the maintainer having only one package, indicating potential supply-chain concerns.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell execution patterns detected, indicating no immediate risk of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.
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
Repository Abhijeet777ui/ADIS-ML-Pipeline-App appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Abhijeet Baug" 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 adis-autoresearch
Create a fully-functional mini-app that leverages the Automated Data Intelligence System (ADIS) - Explainable ML Pipeline and AI Critic package called 'adis-autoresearch'. Your task is to develop a simple yet powerful tool that can help users understand their dataset better through an automated machine learning pipeline. This app will take a CSV file as input, perform exploratory data analysis, automatically select the best machine learning model based on performance metrics, and provide explainability of the model's predictions using SHAP values. Steps to build this mini-app: 1. Allow the user to upload a CSV file containing tabular data. 2. Use the 'adis-autoresearch' package to load and preprocess the data, including handling missing values and categorical variables. 3. Perform exploratory data analysis (EDA) to visualize distributions, correlations, and identify outliers. 4. Implement an automated machine learning pipeline that includes feature selection, model training, and hyperparameter tuning. 5. Evaluate the trained models using appropriate metrics such as accuracy, precision, recall, and F1-score. 6. Select the best performing model and use 'adis-autoresearch' to generate an explanation of its predictions using SHAP values. 7. Display the results in an interactive dashboard that shows EDA visuals, model performance metrics, and SHAP explanations. 8. Ensure the application is user-friendly, with clear instructions and error messages for common issues like incorrect file formats or missing columns. Suggested Features: - Support for both classification and regression tasks. - Option to manually specify target variable and other parameters. - Real-time updates on the progress of each step (e.g., data loading, model training). - Export functionality to save the results as a PDF report. - Integration with popular visualization libraries like Matplotlib or Seaborn for EDA. How 'adis-autoresearch' is utilized: - For data preprocessing and cleaning operations. - To automatically select the most relevant features from the dataset. - In the creation of an automated ML pipeline that includes model selection and hyperparameter optimization. - To interpret the black-box model predictions and provide explainability through SHAP values. Your goal is to create a tool that not only automates the process of building and evaluating machine learning models but also enhances transparency and understanding of the model's decisions.