ml-explain-preprocess 0.1.1

Explainable preprocessing for ML pipelines with reports and visualizations.

ml-explain-preprocess

Transparent preprocessing for machine learning with beginner-friendly reports and visualizations.

ml-explain-preprocess simplifies data preprocessing for machine learning by providing modular functions to handle missing values, encoding, scaling, outlier detection, and feature selection. Each step generates detailed, easy-to-understand reports (text or JSON) and optional visualizations, making it ideal for beginners and teams needing auditable pipelines.

Installation

Install from PyPI with:

pip install ml-explain-preprocess

Requires Python 3.8+ and dependencies: pandas, numpy, scikit-learn, matplotlib, seaborn.

Why ml-explain-preprocess?

Preprocessing is a critical but often opaque step in machine learning. This package makes it transparent by:

• Explaining every transformation to clarify what each step does.
• Generating detailed reports with before/after stats, impacts, and beginner tips.
• Visualizing changes with plots (e.g., missing value heatmaps, histograms).
• Seamless integration with pandas DataFrames, compatible with scikit-learn pipelines.

This package is designed for beginners learning data preparation and teams requiring auditable workflows.

Key Features

• Missing Value Handling: Impute with mean, median, mode, or constant; reports percentage of missing data handled.
• Encoding: One-hot or label encoding for categorical features, with unique value counts.
• Scaling: Min-max, standard, or robust scaling, with detailed before/after statistics.
• Outlier Detection: IQR or z-score methods, with options to remove, clip, or report outliers.
• Feature Selection: Drop low-variance features, with variance reports.
• Explainable Reports: Beginner-friendly explanations, parameters, impacts, stats, and visual descriptions in text or JSON.
• Visualizations: Save plots (e.g., histograms, boxplots) to a reports/ folder when visual=True.
• Pandas Integration: Input/output as DataFrames for easy use in ML workflows.

Quickstart

import pandas as pd
from ml_explain_preprocess import explain_preprocess

# Sample dataset
df = pd.DataFrame({
    'Age': [25, 30, None, 40],
    'Gender': ['M', 'F', 'M', 'F'],
    'Income': [50000, 60000, 55000, None]
})

# Run preprocessing pipeline
processed_df, report = explain_preprocess(
    df,
    steps=['fill', 'encode', 'scale'],
    report_format='text',
    visual=True
)

# View report
print(report)

Example text report output:

Preprocessing Report (Beginner-Friendly)
====================================

Step: FILL
Explanation: Missing value handling (imputation) fills in gaps in the data to ensure machine learning models can process it. Missing data can occur due to errors or incomplete collection.
Parameters Used: Strategy: auto, Columns: ['Age', 'Income']
Impact: Handled 2 missing values (16.7% of data).
Statistics:
  missing_before: {'Age': '1 missing (25.0%)', 'Income': '1 missing (25.0%)'}
  missing_after: {'Age': '0 missing (0%)', 'Income': '0 missing (0%)'}
  strategies: {'Age': 'median', 'Income': 'median'}
Visuals: reports/missing_before.png, reports/missing_after.png
Visual Descriptions:
  - Heatmap (before): Red/yellow shows missing values, white is non-missing.
  - Heatmap (after): Should be all white if all missing values were filled.
Tips for Beginners: Choose 'median' for numerical data with outliers, 'mean' for normally distributed data, or 'mode' for categorical data. Always check how much data is missing before imputing.
----------------------------------------
[... more steps ...]

Visualizations (when visual=True) are saved to the reports/ folder.

Available Functions

• explain_fill_missing(df, strategy='auto', columns=None, visual=False): Impute missing values.
• explain_encode(df, method='auto', columns=None, visual=False): Encode categorical features.
• explain_scale(df, method='minmax', columns=None, visual=False): Scale numerical features.
• explain_outliers(df, method='iqr', threshold=1.5, action='remove', columns=None, visual=False): Handle outliers.
• explain_select_features(df, threshold=0.01, columns=None, visual=False): Drop low-variance features.
• explain_preprocess(df, steps=['fill', 'encode', 'scale', 'outliers', 'select'], target=None, report_format='json', visual=False): Full pipeline with combined report.

Each function returns: processed_df, report_dict.

Project Structure

ml-explain-preprocess/
├── ml_explain_preprocess/
│   ├── __init__.py
│   ├── preprocess.py      # Core preprocessing functions
│   └── reports.py         # Report generator
├── tests/                 # Unit tests
├── examples/              # Example Jupyter notebook
├── pyproject.toml         # Package configuration
├── LICENSE                # MIT License
└── README.md

Contributing

We welcome contributions! To get started:

• Fork the repository on GitHub.
• Submit issues or pull requests for bugs, features, or improvements.
• Contact: [e-mail].

License

Released under the MIT License.