A joint project to explore Big Data, Machine Learning Algorithms, Data Science tools and visualization in Python. Utilized a diabetes dataset from UCI ML Repository to explore patient readmittance rates. Data was cleaned and transformed in Python before deploying various classification algorithms to predict patient readmittance. A presentation was created to highlight findings and suggestions for the industry.
This project requires you to install Anaconda 3.7 and make use of Jupyter Notebooks for Python. All packages should be installed based on the import statements at the top of the IPYNB file. All Python work is saved in Diabetes_Project2_dlesser_xiangxia.iypnb and final presentation is available within Group3_Presentation_29APR19.pptx as well as on https://youtu.be/v8eSGUC5M40 .
- Anaconda 3.7
- Python Jupyter Notebooks
Daniel Lesser, Carol Xiang Carnegie Mellon University Big Data and Data Science Spring 2019
- UCI Machine Learning Repository