Overview This project demonstrates how to recognize handwritten digits using a machine learning approach with scikit-learn and K-means clustering. It uses the digits dataset from sklearn, which contains 8x8 images of handwritten digits (0-9), to classify digits and apply unsupervised learning techniques.

Key Concepts: Handwritten Digit Recognition: Classify digits using supervised learning algorithms. K-means Clustering: Apply unsupervised learning to cluster handwritten digits into distinct groups. Features Digits Dataset: Load and visualize the digits dataset containing images of handwritten digits. Classification: Use supervised learning techniques for digit recognition. Clustering: Apply K-means clustering to group similar digit images. Visualization: Display a grid of digits and their corresponding clusters.