🐾 Image Classification — Cats vs Dogs

This project focuses on binary image classification to differentiate between cats and dogs using supervised learning.

📌 Overview

• Goal: Classify images as either a cat or a dog.
• Dataset: Labeled images of cats and dogs.
• Approach:
  • Extract relevant features from images.
  • Build and train a binary classifier.
  • Evaluate performance using standard metrics.

✅ Tasks

• Feature Extraction: Capture key visual features for accurate classification.
• Model Training: Develop a reliable binary classifier.
• Evaluation: Measure accuracy, precision, recall, F1-score, and analyze the confusion matrix.

🚀 Result

A well-trained model that can accurately predict whether an image shows a cat or a dog, with clear evaluation reports.

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Feel free to adapt this as needed for your project!