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🐾 Cat vs Dog Image Classifier

A simple Machine Learning project that predicts whether an image contains a Cat or a Dog 🐱🐶

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📘 Overview

This project demonstrates how a trained classification model can identify whether an input image is of a cat or a dog. It includes:

• Image loading and preprocessing
• Prediction using a pre-trained model
• Visualization of the input image and classification probabilities

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🗂️ Project Structure

├── SCT_ML_3.py               # Main Python script for prediction
├── cat_image_display.png     # Input image used for testing
├── cat_prediction_result.png # Graph showing predicted probabilities
└── dataset/
    └── test_set/
        ├── cats/
        └── dogs/

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⚙️ Installation & Requirements

Install all dependencies using pip:

pip install numpy matplotlib scikit-image scikit-learn

You’ll also need a trained model file and a category list such as:

model = trained_model  # Pre-trained model
Categories = ['Cat', 'Dog']

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🧩 How It Works

🖼 1. Load and Display the Image

path = 'dataset/test_set/dogs/dog.4001.jpg'
img = imread(path)
plt.imshow(img)
plt.axis('off')
plt.show()

🧮 2. Preprocess the Image

Resize and flatten the image for model input.

img_resize = resize(img, (150, 150, 3))
l = [img_resize.flatten()]

🤖 3. Predict Using the Model

probability = model.predict_proba(l)

for ind, val in enumerate(Categories):
    print(f'{val} = {probability[0][ind] * 100:.2f}%')

predicted_label = model.predict(l)[0]
print("The predicted image is : " + Categories[predicted_label])

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📊 Results

🐱 Input Image

📈 Prediction Result

✅ Prediction: Cat (90%) ❌ Dog: 10%

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🚀 Future Enhancements

• 🔁 Add more animal categories
• 🧠 Use Convolutional Neural Networks (CNNs) for higher accuracy
• 📸 Add real-time webcam image detection
• 🌐 Create a web app using Flask or Streamlit

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👨‍💻 Author

Kamalhiiny Gopi 💼 Passionate about Machine Learning & AI 📧 gkamalhiiny07@gmail.com

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⭐ Show Your Support

If you like this project, give it a ⭐ on GitHub and share it!

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