Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: This project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Diabetes 130-US Hospitals dataset is a binary classification situation where we attempt to predict one of the two possible outcomes.
INTRODUCTION: The data set is the Diabetes 130-US Hospitals for years 1999-2008 donated to the University of California, Irvine (UCI) Machine Learning Repository. The dataset represents ten years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes.
ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 63.72%. After a series of tuning trials, the refined XGBoost model processed the training dataset with a final accuracy score of 64.94%. When we processed the test dataset with the final model, the model achieved an accuracy score of 65.39%.
CONCLUSION: In this iteration, the XGBoost model appeared to be a suitable algorithm for modeling this dataset.
Dataset Used: Diabetes 130-US Hospitals for years 1999-2008 Dataset
Dataset ML Model: Binary classification with numerical and categorical attributes
Dataset Reference: https://archive-beta.ics.uci.edu/ml/datasets/296
One potential source of performance benchmarks: http://www.hindawi.com/journals/bmri/2014/781670/
The HTML formatted report can be found here on GitHub.