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Issue: Improve README Documentation on K-Means Implementation

Description:

The current README lacks detailed information on how the K-Means clustering algorithm is implemented from scratch. This issue is created to enhance the README by providing a comprehensive explanation of the implementation process, including the formulas and equations used.

Implementation Details:

To improve the README, we should include the following information:

1. Introduction to K-Means: Provide a brief introduction to K-Means clustering, explaining its purpose and how it works.

2. Algorithm Implementation: Describe the step-by-step process of implementing K-Means from scratch. This should include:

   • Initialization: Explain how initial cluster centroids are selected.
   • Assignment: Describe how data points are assigned to clusters based on centroid proximity.
   • Update: Explain how cluster centroids are updated based on the assigned data points.
   • Convergence: Describe the stopping criteria for the algorithm.

3. Formulas and Equations: Include the mathematical formulas used in the algorithm. Please format these equations in Markdown to ensure readability. Here are the equations we need:

   • Euclidean Distance: The formula to calculate the Euclidean distance between two points.

     Euclidean Distance (d) = √((x2 - x1)^2 + (y2 - y1)^2)

   • Centroid Update: The formula to update the centroid of a cluster.

     New Centroid (C_new) = (1 / n) * Σ(All Points in Cluster)

4. Code Examples: Include code snippets or pseudocode to illustrate how the algorithm is implemented in code.

Additional Notes:

• Please ensure that the equations and code examples are well-documented and easy to understand.
• Use clear and concise language to make the README accessible to developers of all levels.
• Consider including visualizations or diagrams to aid in understanding the algorithm.

Your contributions to improving the README will greatly benefit the project and help users understand the implementation of the K-Means clustering algorithm from scratch. Thank you for your help!

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