ML Lab

A Python Machine Learning Library

A Python library that provides implementations of various machine learning algorithms for tasks such as regression, classification, clustering, and more.

Supported Algorithms

1. Linear Regression
2. Logistic Regression
3. Naive Bayes
4. Decision Trees
5. Ensembles (Random Forest, Gradient Boosting)
6. K-Means
7. Support Vector Machines (SVM)
8. Artificial Neural Networks (Multi-layer Perceptron)
9. Convolutional Neural Networks (CNNs)

Usage

Create a virtual environment(optional)

python -m venv venv
source venv/bin/activate

Install requirements

pip install -r requirements.txt

Example

Here's a simple example of how to use ML-Lib to train a linear regression model:

from models.linear_regression.regression_sgd import SGDRegression
import numpy as np

# Create some sample data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])

# Instantiate and train the linear regression model
model = SGDRegression()
model.fit(X, y)

# Make predictions
X_test = np.array([[6], [7], [8]])
predictions = model.predict(X_test)
print(predictions)

License

This project is licensed under the GNU AFFERO GENERAL PUBLIC LICENSE - see the License file for details.