📝 Overview

This project builds a Convolutional Neural Network (CNN) to classify images into two classes: cats (0) and dogs (1).
The workflow covers data preprocessing, model training, and evaluation with clear documentation of the architecture and findings.

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🎯 Goals

• Preprocess and explore the dataset.
• Build and train a CNN classification model.
• Evaluate using accuracy, precision, recall, F1-score, and a confusion matrix.
• Record observations under the Findings section.

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📦 Dataset

Dogs vs. Cats (Kaggle)

• Content: JPEG images of cats and dogs
• Labels: 0 → cat, 1 → dog
• Format: RGB images (will be resized to 128×128)

Download the dataset from Kaggle and organize it as: Kaggle Dataset - Dog vs. Cat

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Findings

• Accuracy: [accuracy: 0.9882 ]
• Loss: [loss: 0.0430]