Configure the notebook environment by enabling the %matplotlib inline backend, allowing all visualizations to render directly within the notebook. This establishes a streamlined workflow for exploratory analysis and model evaluation.

Import the Scikit-Learn libraries required for the project, including dataset utilities, model selection tools, the Support Vector Classifier, visualization libraries, and evaluation metrics. These dependencies form the foundation of the machine learning pipeline.

Load the Scikit-Learn handwritten digits dataset, consisting of 8 × 8 grayscale images labeled from 0–9. Initial visual inspection confirms the structure of the dataset and validates the correspondence between image samples and their ground-truth labels

Convert each 8 × 8 image into a one-dimensional feature vector of 64 numerical values suitable for machine learning. The dataset is partitioned into training and testing subsets before fitting a Support Vector Classifier (SVC) to the training data.

Apply the trained classifier to previously unseen test images using the .predict() method. Predicted labels are displayed alongside the handwritten digit samples, providing an immediate qualitative assessment of model performance.

Generate a classification report summarizing Precision, Recall, F1-Score, and overall accuracy for each digit class. These metrics provide a comprehensive quantitative evaluation of the classifier's predictive performance.

Visualize model predictions using a confusion matrix that compares predicted labels against the true classes. This diagnostic view highlights classification strengths, identifies recurring misclassifications, and supports iterative model refinement.