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 - A basic regression model that fits a linear relationship between independent and dependent variables.
2. Multiple Linear Regression - An extension of linear regression that handles multiple input features.
3. Logistic Regression - A classification algorithm based on the sigmoid function for binary classification problems.
4. K-Nearest Neighbors (KNN) - A non-parametric method used for classification and regression based on distance metrics.
5. Decision Tree - A tree-based model that splits data based on feature importance to make decisions.
6. Random Forest - An ensemble method using multiple decision trees to improve prediction accuracy and reduce overfitting.
7. Support Vector Machine (SVM) - A powerful classification algorithm that finds the optimal hyperplane for separating classes.
8. XGBoost - An optimized gradient boosting algorithm that builds trees sequentially to minimize errors.

Usage

Example: Linear Regression

from axonml 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)

Similarly, you can use other models like Logistic Regression, KNN, Decision Tree, Random Forest, SVM, and XGBoost.

License

AxonML is licensed under the MIT License.

Happy Coding with AxonML! 🚀