modelml 0.1.1

A Collection of Machine Learning Algorithms implemented from Scratch

Py-modelml

modelml is a collection of machine learning models implemented from scratch. This library provides simple and easy-to-use implementations of various machine learning algorithms, including linear regression, multiple regression, logistic regression, k-nearest neighbors (KNN), decision trees,random forests,XGB classifier and SVM.

Installation

You can install modelml using pip:

pip install modelml

Usage

Here an examples of how to use the models provided by modelml:

Linear Regression

from modelml import LinearRegression
import numpy as np

# Example data
X = np.array([[1], [2], [3], [4]])
y = np.array([2, 4, 6, 8])

# Create and train the model
model = LinearRegression(learning_rate=0.01, epochs=1000)
model.fit(X, y)

# Make predictions
predictions = model.predict(X)
print("Predictions:", predictions)

similarly implement all other algorithms

Algorithms

includes the following machine learning algorithms:

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

Contributing

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

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For any questions or feedback, please contact Karthikeyan.