Machine Learning Algorithms in Python

This repository contains Python implementations of fundamental machine learning algorithms, including K-Means Clustering (kmeans.py), Linear Regression (linear.ipynb), Logistic Regression (logistic.ipynb), and Support Vector Machine (SVM) (svm.ipynb).

Algorithms Included

1. K-Means Clustering (kmeans.py)

• Description: K-Means is an unsupervised clustering algorithm used to partition data into K clusters based on similarity.
• Implementation: The kmeans.py file contains the implementation of the K-Means clustering algorithm to group data points into clusters.

2. Linear Regression (linear.ipynb)

• Description: Linear Regression is a supervised learning algorithm used to model the relationship between a dependent variable and one or more independent variables.
• Implementation: The linear.ipynb Jupyter Notebook demonstrates the implementation of Linear Regression for predictive modeling.

3. Logistic Regression (logistic.ipynb)

• Description: Logistic Regression is a classification algorithm used for binary and multi-class classification tasks.
• Implementation: The logistic.ipynb Jupyter Notebook showcases Logistic Regression's implementation for classification problems.

4. Support Vector Machine (SVM) (svm.ipynb)

• Description: SVM is a supervised learning algorithm used for classification and regression analysis by creating a hyperplane that best separates classes.
• Implementation: The svm.ipynb Jupyter Notebook provides an implementation of SVM for classification tasks.

How to Use

1. Clone the Repository:

   https://github.com/MTank76/Machine-Learning-Algorithm.git
   

2. Explore the Algorithms:

   • Access the respective files or Jupyter Notebooks within their corresponding directories (e.g., kmeans.py, linear.ipynb, logistic.ipynb, svm.ipynb).
   • Each file/notebook contains Python code demonstrating the respective algorithm's functionality, usage, and potential applications.

Contributing

Contributions are welcome! If you'd like to contribute to this project, feel free to open issues for suggestions or submit pull requests with proposed enhancements.