Neural Network Project: Cat vs. Dog Classification and Medical Side Effects Prediction

Overview

This project contains two separate neural networks developed using Keras:

1. Cat vs. Dog Classification: A convolutional neural network (CNN) to differentiate between images of cats and dogs.
2. Medical Side Effects Prediction: A neural network to predict medical side effects for young and old individuals.

Project Structure

• cnn_cat_dog.ipynb: Jupyter notebook containing the implementation of the cat vs. dog classification model.
• med_neural_network.ipynb: Jupyter notebook containing the implementation of the medical side effects prediction model.
• data/: Directory where datasets for both models are stored.
• models/: Directory to save the trained models.
• README.md: Project documentation.

Requirements

• Python 3.7+
• TensorFlow 2.x
• Keras
• NumPy
• Pandas
• Matplotlib
• Jupyter Notebook

You can install the required packages using:

• bash

pip install tensorflow keras numpy pandas matplotlib jupyter

Running the Notebooks

1. Cat vs. Dog Classification:

   • Open the cnn_cat_dog.ipynb notebook.
   • Ensure you have the dataset in the data/ directory.
   • Data can be download from kaggle
   • Run all cells to train and evaluate the model.

   [!NOTE] The cat vs. dog classification model is likely overfitting and should not be used in a production environment. It is intended for educational purposes only to demonstrate the construction and training of a CNN.

2. Medical Side Effects Prediction:

   • Open the med_neural_network.ipynb notebook.
   • Ensure you have the dataset in the data/ directory.
   • Run all cells to train and evaluate the model.

Credits

The implementation of the Cat vs. Dog Classification model is based on the tutorial by Nicholas Renotte. You can watch the tutorial here.