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The dataset consists of labeled images of cats and non-cats, preprocessed and used to train a binary classification model.\n\n## 📌 Features\n\n- Loads and preprocesses the dataset (resizing, normalizing, and flattening images)\n\n- Implements Logistic Regression from scratch\n\n- Includes training, optimization, and prediction functions\n\n- Provides test evaluations on new images\n\n## 📂 Dataset Information\n\n- 209 training images (64x64 RGB images)\n\n- 50 test images (64x64 RGB images)\n\n- Labels: 1 for cat, 0 for non-cat\n\n## 💻 Tech Stack\n\n- Python: Primary programming language\n- NumPy: For numerical computations\n- Matplotlib: For data visualization\n- PIL (Pillow): For image handling\n- SciPy: For scientific computing\n- h5py: For handling dataset storage in HDF5 format\n\n## 📊 Model Performance\n\n- Achieves high accuracy in detecting cats using a basic logistic regression approach.\n\n- You can improve the model by implementing deep learning using neural networks (e.g., TensorFlow/Keras).\n\n## 🎯 Future Enhancements\n\n- Implementing deep learning with a convolutional neural network (CNN)\n\n- Expanding dataset for better generalization\n\n- Integrating deployment via Flask or FastAPI\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FKhushi130404%2FCatNet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FKhushi130404%2FCatNet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FKhushi130404%2FCatNet/lists"}