For the Deep Learning Challenge, Module 21, I constructed a neural networks tool for the non-profit foundation Alphabet Soup. Specifically, I use the features of a dataset provided, charity_data.csv, to create a binary classifier that can predict whether applicants will be successful in their ventures if granted the requested funding. All programming was done in Python on a Google Colab platform.

In this Deep-Learing-Challenge repository, you will find the following:

The initial neural networks model construction program and model h5 file output:

AlphabetSoupCharity.ipynb

AlphabetSoupCharity.h5

Four optimization model programs and their respective model h5 file output:

AlphabetSoupCharity_Optimization_1.ipynb

AlphabetSoupCharity_Optimization_1.h5

AlphabetSoupCharity_Optimization_2.ipynb

AlphabetSoupCharity_Optimization_2.h5

AlphabetSoupCharity_Optimization_3.ipynb

AlphabetSoupCharity_Optimization_3.h5

AlphabetSoupCharity_Optimization_4.ipynb

AlphabetSoupCharity_Optimization_4.h5

The original dataset utilized in model construction:

charity_data.csv

The final Analysis Report, complete with a chart, bullet points, and even a touch of color and a few smiles sprinkled about:

Analysis_Report.docx

In conclusion, with multiple optimization runs, the accuracy goal of 75% is not only met, but surpassed with a whoppin’ 79% !!!! The complete details of the neural networks journey are provided within the Analysis Report…. not quite your adrenaline pumping thriller, but still, I hope you enjoy the reading 😊.