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README.md

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+# Image Classification Project
+
+This project is a deep learning-based image classification system built with TensorFlow and Keras. It classifies images into two categories: "happy" and "sad". The workflow includes data preprocessing, model training, evaluation, and prediction.
+
+## Project Structure
+
+```
+├── data/
+│   ├── happy/
+│   └── sad/
+├── logs/
+│   ├── train/
+│   └── validation/
+├── models/
+│   └── imageclassifier.h5
+├── notebook.ipynb
+├── requirements.txt
+└── venv311/
+```
+
+- **data/**: Contains subfolders for each class ("happy", "sad") with images for training and validation.
+- **logs/**: Stores TensorBoard logs for training and validation.
+- **models/**: Contains the saved Keras model.
+- **notebook.ipynb**: Jupyter notebook with step-by-step code for data processing, model building, training, evaluation, and testing.
+- **requirements.txt**: List of required Python packages.
+- **venv311/**: Python virtual environment.
+
+## Main Steps
+
+1. **Install Dependencies**
+   - TensorFlow, OpenCV, Matplotlib, and other required packages.
+2. **Data Cleaning**
+   - Remove corrupted or unsupported images.
+3. **Data Loading & Preprocessing**
+   - Load images using `tf.keras.utils.image_dataset_from_directory`.
+   - Scale pixel values to [0, 1].
+   - Split data into training, validation, and test sets.
+4. **Model Building**
+   - Build a CNN using Keras Sequential API.
+5. **Training**
+   - Train the model with TensorBoard logging.
+6. **Evaluation**
+   - Evaluate model performance using accuracy, precision, and recall.
+7. **Testing**
+   - Test the model on new images.
+8. **Saving & Loading Model**
+   - Save the trained model and demonstrate loading for inference.
+
+## Usage
+
+1. Clone the repository and navigate to the project directory.
+2. (Optional) Create and activate a Python virtual environment.
+3. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+4. Place your images in the `data/happy/` and `data/sad/` folders.
+5. Open and run the `Getting Started.ipynb` notebook to train and test the model.
+
+## Requirements
+- Python 3.11+
+- TensorFlow
+- OpenCV
+- Matplotlib
+- NumPy
+
+All dependencies are listed in `requirements.txt`.
+
+## Model
+- The model is a simple CNN with Conv2D, MaxPooling2D, Flatten, and Dense layers.
+- Trained model is saved as `models/imageclassifier.h5`.
+
+## Logs
+- Training and validation logs are saved in the `logs/` directory for visualization with TensorBoard.
+
+## License
+This project is for educational purposes.