transparentml

Transparent ML is a lightweight, educational machine learning library built from scratch with NumPy. It provides simple implementations of common supervised and unsupervised learning algorithms for experimentation, learning, and small projects.

Why choose transparentml?

• Simple, readable implementations designed for learning and teaching.
• Built with NumPy only, with no heavy dependencies.
• Includes both classical ML algorithms and basic clustering methods.
• Easy to inspect and modify for educational purposes.

Features

The package currently includes:

• Linear regression
• Logistic regression
• SGD regression
• K-nearest neighbors
• K-means clustering
• Decision trees

Installation

Install from PyPI:

pip install transparentml

Quick start

import numpy as np
from transparentml.linear_models.linear_regression import LinearRegression

X = np.array([[1.0], [2.0], [3.0], [4.0]])
y = np.array([2.0, 4.0, 6.0, 8.0])

model = LinearRegression()
model.fit(X, y)

predictions = model.predict(np.array([[5.0]]))
print(predictions)

Example usage

Linear regression

from transparentml.linear_models.linear_regression import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)
preds = model.predict(X_test)

Logistic regression

from transparentml.linear_models.logistic_regression import LogisticRegression

model = LogisticRegression(learning_rate=0.1, epochs=100)
model.fit(X_train, y_train)
probabilities = model.predict_proba(X_test)

K-means clustering

from transparentml.clustering.kmeans import KMeans

model = KMeans(n_clusters=3, random_state=42)
labels = model.fit_predict(X)

K-nearest neighbors

from transparentml.neighbors.knn import KNN

model = KNN(n_neighbours=5)
model.fit(X_train, y_train)
preds = model.predict(X_test)

Decision tree

from transparentml.tree.decision_tree import DecisionTree

model = DecisionTree(min_samples_split=2, max_depth=3)
model.fit(X_train, y_train)
preds = model.predict(X_test)

Project status

This project is currently in early development and is best suited for learning, experimentation, and educational use. It is not intended to be a full replacement for production-grade libraries such as scikit-learn.

License

This project is licensed under the MIT License.

Contributing

Contributions are welcome. If you want to improve the package, add examples, or fix issues, feel free to open a pull request or issue on GitHub.