A lightweight, educational machine learning library reimplementing core algorithms from scratch, inspired by scikit-learn. Developed by IFRI AI students for the Concepts & Applications of Machine Learning course.

• Core machine learning algorithms for:
  • Classification (Decision Trees, KNN, Logistic Regression)
  • Regression (Linear, Polynomial, SVR)
  • Clustering (K-means, DBSCAN, Hierarchical)
  • Association Rules (Apriori, Eclat, FP-Growth)
  • Neural Networks (MLP)
• Model selection tools (Cross-validation, Grid Search, etc.)
• Preprocessing utilities (scalers, encoders, missing value handlers, etc.)
• Focus on transparency and understanding of ML model internals

Install from PyPI:

pip install ifri-mini-ml-lib

Or install from source:

git clone https://github.com/IFRI-AI-Classes/ifri_mini_ml_lib.git
cd ifri_mini_ml_lib
pip install -e .

Here's a simple example using the KNN classifier:

from ifri_mini_ml_lib.classification import KNN

# Example data
data = [[0, 0], [1, 1], [0, 1], [1, 0]]
labels = [0, 1, 1, 0]

# Initialize and fit the model
knn = KNN(k=3)
knn.fit(data, labels)

# Predict
prediction = knn.predict([[0.9, 0.8]])
print(prediction)

Full documentation is available at: ifri_mini_ml_lib.github.io

Contributions are welcome! Please open an issue or submit a pull request.

This project is licensed under the MIT License.

Thanks to the IFRI AI students and faculty who contributed to this project.