🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some suspicious behaviors related to obfuscation and metadata, which raise concerns about its legitimacy and purpose. However, there is no concrete evidence of malicious intent.

Obfuscation risk of 5/10
Metadata risk of 3/10 due to a new or inactive maintainer account

Per-check LLM notes

Network: The package makes HTTP requests to external URLs which could be for legitimate purposes like fetching metadata or catalog information.
Shell: No shell execution patterns were detected.
Obfuscation: The obfuscation pattern is suspicious but may be used for legitimate purposes such as data serialization.
Credentials: No clear patterns indicative of credential harvesting were detected.
Metadata: The maintainer has a new or inactive account and lacks a proper author name, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Low (4.2/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (7819 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

267 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 29 commits in worldbank/ai4data
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

) as tmp: r = requests.get(fname_or_url, stream=True) r.raise_for_statu
} response = httpx.get( f"{METADATA_CATALOG_URL}/api/catalog/json/{idno
parameters.""" response = httpx.get( f"{METADATA_CATALOG_URL}/api/catalog/search",
be a string" response = httpx.get( url, follow_redirects=True, timeout

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

tput_path.write_text( __import__("json").dumps(payload, indent=2, default=str), encoding="ut

✓ 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: worldbank.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository worldbank/ai4data appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 ai4data

Develop a data analysis and visualization tool called 'DataInsight' using the Python package 'ai4data'. This tool will allow users to upload datasets, perform basic statistical analyses, visualize data trends, and apply machine learning models to predict future trends based on historical data.

Steps to follow:
1. **Setup**: Create a Python environment and install necessary packages including 'ai4data', pandas, matplotlib, seaborn, and scikit-learn.
2. **User Interface**: Design a simple web interface using Flask or Django where users can upload CSV files.
3. **Data Handling**: Use 'ai4data' to clean and preprocess the uploaded dataset, handling missing values and outliers effectively.
4. **Statistical Analysis**: Implement functions to calculate basic statistics like mean, median, mode, standard deviation, etc., for numerical columns.
5. **Visualization**: Provide interactive visualizations using libraries like Plotly or Bokeh to show distributions, correlations, and trends within the data.
6. **Machine Learning Predictions**: Integrate 'ai4data' functionalities to train simple regression models on time-series data and predict future values.
7. **Results Presentation**: Display results of statistical analysis and predictions in a user-friendly manner, along with visual aids.
8. **Documentation**: Write comprehensive documentation explaining how to use each feature of 'DataInsight' and provide examples.

Suggested Features:
- Support for multiple file formats (CSV, Excel)
- Customizable visualization options (color schemes, chart types)
- Real-time data processing updates
- Export options for analysis results (PDF, PNG, CSV)

How 'ai4data' is Utilized:
- For data cleaning and preprocessing, 'ai4data' will be used to automatically identify and handle missing values, detect and manage outliers, and normalize data.
- In the machine learning section, 'ai4data' will provide pre-built models for regression and classification tasks, making it easier for users to apply these models without deep knowledge of machine learning algorithms.