MLRadar

v1.0.1 suspicious
6.0
Medium Risk

ML Data Intelligence Suite — Readiness scoring, leakage detection, drift analysis, and pipeline generation

🤖 AI Analysis

Final verdict: SUSPICIOUS

The MLRadar package exhibits low individual risk factors such as no network calls, shell executions, obfuscation, or credential harvesting. However, the metadata risk score is elevated due to the lack of maintainer details and minimal repository activity, raising concerns about the legitimacy and future maintenance of the package.

  • metadata risk due to sparse maintainer information
  • repository appears inactive
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
  • Metadata: The repository shows signs of being a throwaway account with minimal activity, and the maintainer information is sparse, indicating potential risk.

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Single contributor with only 4 commit(s) — possibly throwaway account
⚠ 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 MLRadar 
Create a comprehensive data health dashboard using the MLRadar package. This dashboard will serve as a tool for data scientists and analysts to quickly assess the quality and readiness of their datasets before initiating machine learning projects. Here’s how the application will function:

1. **Data Upload**: Users should be able to upload their datasets in CSV or Excel formats. The application will support both single file uploads and batch uploads.
2. **Readiness Scoring**: Utilize MLRadar's readiness scoring feature to evaluate the dataset based on various criteria such as completeness, consistency, and relevance. Display the score along with a detailed breakdown of the evaluation metrics.
3. **Leakage Detection**: Implement MLRadar's leakage detection capabilities to identify any potential issues where target variables might be inadvertently leaking into the training set, which could lead to overly optimistic model performance estimates.
4. **Drift Analysis**: Use MLRadar to perform drift analysis on datasets over time, identifying if there are significant changes in the distribution of data that could impact model performance.
5. **Pipeline Generation**: Based on the findings from the previous steps, generate a preliminary machine learning pipeline that includes preprocessing steps tailored to the dataset's characteristics, such as handling missing values, encoding categorical variables, and feature scaling.
6. **Interactive Dashboard**: Develop an interactive web-based dashboard using a framework like Streamlit or Flask to visualize the results of each step. Ensure the dashboard is user-friendly and allows users to explore different aspects of their data and pipeline recommendations.
7. **Export Options**: Provide options for users to export the generated pipeline, drift analysis reports, and other key outputs in formats such as JSON, CSV, or PDF.

The goal is to create a versatile tool that not only assists in identifying potential issues within datasets but also provides actionable insights and recommendations for improving data quality and preparing it for effective machine learning modeling.