🐱 CatNet - Cat Image Classifier

CatNet is a simple machine learning project that classifies images as either a cat or not a cat using logistic regression. The dataset consists of labeled images of cats and non-cats, preprocessed and used to train a binary classification model.

📌 Features

• Loads and preprocesses the dataset (resizing, normalizing, and flattening images)

• Implements Logistic Regression from scratch

• Includes training, optimization, and prediction functions

• Provides test evaluations on new images

📂 Dataset Information

• 209 training images (64x64 RGB images)

• 50 test images (64x64 RGB images)

• Labels: 1 for cat, 0 for non-cat

💻 Tech Stack

• Python: Primary programming language
• NumPy: For numerical computations
• Matplotlib: For data visualization
• PIL (Pillow): For image handling
• SciPy: For scientific computing
• h5py: For handling dataset storage in HDF5 format

📊 Model Performance

• Achieves high accuracy in detecting cats using a basic logistic regression approach.

• You can improve the model by implementing deep learning using neural networks (e.g., TensorFlow/Keras).

🎯 Future Enhancements

• Implementing deep learning with a convolutional neural network (CNN)

• Expanding dataset for better generalization

• Integrating deployment via Flask or FastAPI