Supervised Machine Learning, Classification
AlphabetSoup
2021
Project Description
Alphabet Soup, is a nonprofit philanthropic foundation dedicated to help various organizations around the world. By building a deep learning neural network with an at least 75% predictive accuracy we will attempt to determine the success or failure of charitable donations to non-profits.
This neural network is built to help Alphabet Soup make future decisions on who should receive charitable grants based on historical data. The dataset contains information from 34,000 organizations which will be used to build a model with high predictive accuracy.
Related Work
Happy Mutts
Happy Mutts needed an online store designed with full e-commerce capabilities.
Sunny's
Sunny's Babysitting Services needed a fully functional website designed to provide information to clients and to serve as the main point for booking services.
Release and Unleash
Release and Unleash needed an overhaul of their outdated website. The team redesigned and developed a multi-page e-commerce site with a orimary goal of telling the story of Release and Unleash and providing an online store where customers can find their products.
D3 Trucking
D3 Trucking needed a website designed to help with the application process, booking consultations, and selling their digital booklets.
Crypto Analysis
Unsupervised Machine Learning: Clustering
Credit Risk Analysis
The Easy Ensemble AdaBoost Classifier was best at predicting high risk credit applications in comparison to the other models. With a 0.925 accuracy and a 0.91 recall score, both Easy Ensemble AdaBoost and Random Forest Classifiers outperformed the Resampling techniques in accurately predicting high-risk credit card applicants.
Neural Network and Deep Learning
Overall results of the deep learning classification problem was 73% accuracy.
