axonml 0.1.0

A lightweight machine learning library implementing fundamental ML algorithms

AxonML

AxonML is a lightweight machine learning package that provides easy-to-use implementations of fundamental ML algorithms. It is designed for beginners and practitioners who want to understand and experiment with ML models without relying on heavy dependencies.

Features

• Simple and efficient implementations of core ML algorithms.
• No heavy dependencies—built using NumPy.
• Easy-to-use API for training and predictions.

Installation

pip install axonml

Supported Algorithms

AxonML includes the following machine learning algorithms:

1. Linear Regression – Predicts continuous values using a linear relationship.
2. Multiple Linear Regression – Extends linear regression to multiple features.
3. Logistic Regression – Used for binary classification tasks.
4. K-Nearest Neighbors (KNN) – A non-parametric classification algorithm.
5. Decision Tree – A tree-based model for classification and regression.
6. Random Forest – An ensemble learning method using multiple decision trees.
7. Support Vector Machine (SVM) – A powerful classification model.
8. XGBoost – Gradient boosting algorithm for improved accuracy.

Usage

Example: Linear Regression

from axonml.linear_regression import LinearRegression
import numpy as np

# Sample dataset
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])

# Model training
model = LinearRegression()
model.fit(X, y)

# Prediction
predictions = model.predict(X)
print(predictions)