autopreprocess-lite

v0.1.1 safe
3.0
Low Risk

Automatic Data Preprocessing Library

🤖 AI Analysis

Final verdict: SAFE

The package autopreprocess-lite v0.1.1 shows minimal risk indicators with no network calls, shell executions, or obfuscation patterns detected. Although the metadata suggests a recently created package with low effort, there are no clear signs of malicious activity.

  • No network calls
  • No shell execution patterns
  • No obfuscation or credential harvesting
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate threat to secrets or credentials.
  • Metadata: The package shows signs of being newly created with low metadata effort, but there are no direct indicators of malicious intent.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 11 test file(s) found

  • 11 test file(s) detected (e.g. test_ames_housing.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1229 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

  • 49 type-annotated function signatures detected in source
○ 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

Email domain looks legitimate: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Ayush Gupta" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with autopreprocess-lite 
Your task is to develop a user-friendly data preprocessing tool using Python's 'autopreprocess-lite' library. This tool will simplify the process of preparing datasets for machine learning models by automating common preprocessing tasks. Your goal is to create a command-line interface (CLI) application that allows users to upload their dataset, select from a variety of preprocessing methods, and automatically generate a cleaned and preprocessed version of their dataset ready for analysis or modeling.

### Key Features:
1. **Dataset Upload**: Users should be able to upload a CSV file containing their dataset.
2. **Automatic Feature Selection**: Implement automatic feature selection based on statistical tests or other criteria.
3. **Data Cleaning**: Automatically handle missing values, remove duplicates, and correct inconsistencies.
4. **Feature Engineering**: Create new features based on existing ones, such as interaction terms or polynomial features.
5. **Normalization/Standardization**: Normalize or standardize numerical features to ensure they contribute equally to the model.
6. **Output Generation**: Provide an option to save the processed dataset back to a CSV file.
7. **Visualization**: Include basic visualizations of the dataset before and after preprocessing to help users understand the impact of each step.
8. **Configuration Options**: Allow users to customize certain aspects of the preprocessing pipeline, such as choosing between different imputation strategies for handling missing data.

### How to Use 'autopreprocess-lite':
- Utilize 'autopreprocess-lite' for its built-in functions that automate the data cleaning and transformation steps.
- Explore the library's documentation to find the most suitable preprocessing techniques for your application.
- Consider integrating 'autopreprocess-lite' with additional Python libraries like pandas for data manipulation and matplotlib/seaborn for visualization to enhance the functionality of your CLI tool.

Your final product should demonstrate proficiency in using 'autopreprocess-lite' to streamline the data preprocessing workflow, making it accessible and efficient for non-experts.

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