Alphabet Soup Funding Application Classification

Project Overview

This project focuses on developing a deep learning model to predict the success of funding applications submitted to Alphabet Soup, a charitable organization. The goal is to create a binary classification model that efficiently classifies applications as either successful or unsuccessful based on various features.

Project Structure

Files

• AlphabetSoupCharity.ipynb: Jupyter Notebook containing the code for data preprocessing, model development, and evaluation.
• AlphabetSoupCharityOpt.ipynb: Jupyter Notebook containing the code for data preprocessing, model development, and evaluation of the optmized model.
• checkpoints/: Directory to save model checkpoints during training.
• AlphabetSoupCharity.h5: Saved model file in HDF5 format.

Dependencies

• pandas: Data manipulation and analysis.
• tensorflow: Deep learning library for building and training neural networks.
• scikit-learn: Tools for machine learning tasks.
• numpy: Mathematical operations.

Instructions

1. Setup:

   • Install the required dependencies.

2. Data Preparation:

   • Obtain the dataset from the provided link.
   • Run the data preprocessing steps in AlphabetSoupCharity.ipynb.

3. Model Development:

   • Adjust model architecture and hyperparameters as needed.
   • Train the model using the provided data.

4. Model Evaluation:

   • Evaluate the model performance on a separate test set.
   • Save the trained model to AlphabetSoupCharity.h5.

Additional Notes

• The model architecture, preprocessing steps, and results are documented in the Jupyter Notebook.

References

• TensorFlow Documentation
• Scikit-learn Documentation
• IRS Tax Exempt Organization Search Bulk Data Downloads