ghazi-data-lib 0.1.0

datalib for machine learning

Here is a structured and comprehensive README file for your project:

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GHAZI_DATA_LIB

GHAZI_DATA_LIB is a Python library for data analysis, machine learning, and statistical operations. It provides various modules for tasks such as data preprocessing, regression, supervised learning, unsupervised learning, statistical analysis, and visualization. The library is designed to be modular, clean, and easy to test.

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Project Structure

The project is organized into the following folders:

GHAZI_DATA_LIB/
│
├── datalib/                    # Core library modules
│   ├── __init__.py             # Package initializer
│   ├── data_processing.py      # Module for data loading and preprocessing
│   ├── regression.py           # Module for linear and polynomial regression
│   ├── statistics.py           # Module for statistical analysis
│   ├── supervised.py           # Module for supervised learning (classification and regression)
│   ├── unsupervised.py         # Module for clustering and dimensionality reduction
│   └── visualization.py        # Module for data visualization (to be implemented)
│
├── example/                    # Example scripts
│   ├── __init__.py
│   └── iris_example.py         # Example usage of the library with the Iris dataset
│
├── tests/                      # Unit tests for the library modules
│   ├── __init__.py
│   ├── test_data_processing.py
│   ├── test_regression.py
│   ├── test_statistics.py
│   ├── test_supervised.py
│   ├── test_unsupervised.py
│   └── test_visualization.py   # Visualization tests (to be implemented)
│
├── .gitignore                  # Git ignore file
├── LICENSE                     # License for the project
├── pyproject.toml              # Project configuration for uv
├── README.md                   # Project documentation
└── uv.lock                     # uv lockfile for package management

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Features

• Data Processing
  Load, clean, preprocess, and split datasets for machine learning tasks.

• Regression Models
  Perform linear and polynomial regression using scikit-learn.

• Supervised Learning
  Train classification and regression models with Random Forest.

• Unsupervised Learning
  Perform K-Means clustering and PCA for dimensionality reduction.

• Statistical Analysis
  Calculate measures such as mean, median, standard deviation, correlation, and run statistical tests (t-test and chi-square).

• Unit Testing
  Each module has corresponding unit tests under the tests/ directory using pytest.

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Requirements

This project uses uv, a modern Python package manager. The dependencies are managed in the pyproject.toml file.

To install uv:

pip install uv

To install project dependencies:

uv pip install -r pyproject.toml

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Installation

1. Clone the repository:

   git clone https://github.com/yourusername/GHAZI_DATA_LIB.git
   cd GHAZI_DATA_LIB
   

2. Install dependencies:

   uv pip install -r pyproject.toml
   

3. Run tests to verify installation:

   pytest tests/
   

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Usage

1. Data Preprocessing

Load and preprocess a dataset:

from datalib.data_processing import load_dataset, preprocess_dataset

# Load data
df = load_dataset("data.csv")

# Preprocess data
X, y = preprocess_dataset(df, target_column="target", drop_columns=["id"], fill_missing="mean")

2. Regression Models

Train a linear regression model:

from datalib.regression import linear_regression

# Prepare data
X = [[1], [2], [3], [4]]
y = [2, 4, 6, 8]

# Train model
model = linear_regression(X, y)
print("Coefficients:", model.coef_)

3. Supervised Learning

Train a supervised model:

from datalib.supervised import train_supervised_model

# Train classification model
model = train_supervised_model(X, y, model_type="classification")

4. Unsupervised Learning

Perform clustering:

from datalib.unsupervised import perform_clustering

# Perform K-Means clustering
clusters = perform_clustering(X, n_clusters=3)
print(clusters)

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Testing

The project includes unit tests for all modules. To run the tests, execute the following command:

pytest tests/

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contributing

Contributions are welcome! If you would like to contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/your-feature-name
   

3. Commit your changes:

   git commit -m "Add your feature"
   

4. Push the branch:

   git push origin feature/your-feature-name
   

5. Create a Pull Request.

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Acknowledgments

• Scikit-learn: For machine learning algorithms.
• Pandas and NumPy: For data manipulation and mathematical operations.
• Pytest: For unit testing.

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Contact

For questions or collaboration requests, contact Ghazi Chaftar at [e-mail].

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