Neural Network Training and Evaluation for Binary Classification: This GitHub repository contains a project that focuses on experimenting with various callback functions in TensorFlow for enhancing model training and performance in a neural network-based binary classification task. The project involves constructing multiple neural network architectures, optimizing hyperparameters, and assessing model performance using metrics like F1 score and AUC score.

Project Structure: data.csv: Input data for the binary classification task. Model-1.ipynb: Jupyter Notebook containing the code for constructing and training the first neural network architecture. Model-2.ipynb: Jupyter Notebook containing the code for constructing and training the second neural network architecture. Model-3.ipynb: Jupyter Notebook containing the code for constructing and training the third neural network architecture. logs/: Directory containing TensorBoard logs for each architecture. checkpoints/: Directory containing saved model checkpoints. README.md: This file, providing an overview of the project and instructions on usage.

Requirements: To run the code in the Jupyter Notebooks, you need the following dependencies: TensorFlow Keras NumPy pandas tqdm scikit-learn Google Colab (for notebook execution)

Usage: Open the Jupyter Notebooks (Model-1.ipynb, Model-2.ipynb, Model-3.ipynb) in Google Colab or any compatible environment. Follow the code in the notebooks to construct, train, and evaluate the neural network architectures. Adjust hyperparameters and configurations as needed. The trained models are saved in the checkpoints/ directory.

License: No specific license for this project.