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## Project Structure 📂
```
├── .github # GitHub configuration for repository management
│ ├── FUNDING.yml
│ ├── ISSUE_TEMPLATE # Templates for issues and pull requests
│ │ ├── bug_report.md
│ │ ├── feature_request.md
│ │ └── PULL_REQUEST_TEMPLATE.md
│ └── workflows # GitHub Actions workflows for automation
│ ├── auto-comment-on-close.yml # Auto-comment on closed issues/PRs
│ ├── check_screenshot.yml # Checks for screenshots in issues/PRs
│ ├── greetings.yml # Sends greetings on issues/PRs
│ ├── pr-checker.yaml # Validates pull requests for required fields
│ ├── pr_merge_comment.yaml # Comments on PRs when merged
│ ├── python-package.yml # Workflow for Python package testing
│ └── python-publish.yml # Workflow for publishing Python packages
├── .gitignore # Lists files and directories to ignore in Git
├── Code_Of_Conduct.md # Guidelines for community behavior
├── Contributing.md # Contribution guidelines
├── LICENSE.md # License information for the project
├── README.md # Main README file with project overview
├── datasets
│ ├── cancer.csv
│ ├── cosmetics.csv
│ ├── data.csv
│ └── hotstar.csv
├── docs
│ ├── api_guide.md
│ └── user_guide.md
├── examples
│ ├── Style Transfer with Neural Networks
│ │ ├── README.md
│ │ ├── Style Transfer with Neural Networks.ipynb # Jupyter notebook
│ │ ├── content image.jpg # Content image for style transfer
│ │ └── style image.jpg # Style image for style transfer
│ ├── Traffic Accident Prediction Model
│ │ ├── README.md
│ │ ├── Traffic Accident Prediction Model.ipynb # Jupyter notebook
│ │ └── TrafficVision - Accident Prediction Model.png
│ ├── explainableai_time_series
│ │ ├── Explainable AI Report.pdf
│ │ ├── explainableai_time_series.ipynb # Jupyter notebook
│ │ ├── learning_curve.png # Learning curve plot
│ │ ├── lime_explanation.png # LIME explanation plot
│ │ ├── partial_dependence.png # Partial dependence plot
│ │ ├── real_sales_per_day.csv # Sample sales data
│ │ └── shap_summary.png # SHAP summary plot
│ ├── model_traning.py # Python script for model training
│ ├── regressionmodelsupport.py # Support script for regression models
│ └── time_series_visualization
│ ├── Time_Series_Report.pdf
│ ├── Time_Series_Visualization.ipynb # Jupyter notebook
│ ├── airline_passengers.csv # Data for time series analysis
│ ├── autocorrelation_plot.png # Autocorrelation plot
│ ├── eda_plot.png # Exploratory Data Analysis plot
│ ├── exponential_smoothing_plot.png # Exponential smoothing plot
│ ├── moving_average_plot.png # Moving average plot
│ ├── seasonal_plot.png # Seasonality plot
│ └── trend_analysis_plot.png # Trend analysis plot
├── explainableai.egg-info # Metadata for the package (internal use)
│ ├── PKG-INFO # Package information file
│ ├── SOURCES.txt # Source file list
│ ├── dependency_links.txt
│ ├── entry_points.txt
│ ├── requires.txt
│ └── top_level.txt
├── explainableai # Main package folder for the explainable AI library
│ ├── __init__.py # Package initialization file
│ ├── anomaly_detection.py # Module for anomaly detection
│ ├── core.py # Core functions and utilities
│ ├── exceptions.py # Custom exception handling
│ ├── fairness.py # Module for AI fairness
│ ├── feature_analysis.py
│ ├── feature_engineering.py
│ ├── feature_interaction.py
│ ├── feature_selection.py
│ ├── llm_explanations.py # Module for LLM explanations
│ ├── logging_config.py # Logging configuration
│ ├── model_comparison.py # Model comparison utilities
│ ├── model_evaluation.py # Model evaluation metrics
│ ├── model_interpretability.py # Interpretability of models
│ ├── model_selection.py # Model selection methods
│ ├── report_generator.py # Report generation for analysis
│ └── visualizations.py # Visualization utilities for explainability
├── funding.json # Funding options for the project
├── main.py # Main script entry point
├── requirements.txt # Python dependencies for the project
├── setup.py # Setup script for packaging
└── tests # Unit tests for various components
├── test_utils.py # Tests for utility functions
└── test_xai_wrapper.py # Tests for explainable AI wrapper functions

```