This project uses a Convolutional Neural Network (CNN) to classify images as either a dog or a cat, based on the Dogs vs Cats dataset from Kaggle.

• The dataset is preprocessed by scaling images to 256x256 pixels.
• A CNN model with 3 convolutional layers is implemented using TensorFlow and Keras.
• The model uses Conv2D, MaxPooling2D, BatchNormalization, and Dropout layers, followed by dense layers for classification.
• The output is a binary classification (dog or cat) with a sigmoid activation function.

1. Dataset: Download the dataset from Kaggle.
2. Preprocessing: Images are scaled to [0,1] range for training.
3. Model: The CNN is built and trained on the preprocessed images, achieving around 81% accuracy on validation data.
4. Prediction: The model can predict whether a given image is a dog or a cat.

• Python
• TensorFlow / Keras
• OpenCV for image handling
• Matplotlib for plotting training results