Welcome to the TAMU Datathon 2023 GitHub repository for Team: 45.
This repository contains all the resources, code, and data for our team's participation in the TAMU Datathon for 2023. Our project is training an ai model using a dataset to determine the accuracy of a patient surviving given the specifics of their condition.
Debrief
- The dataset given to us is in the form of a csv file, the first row having the titles of the columns, and each subsequent row will containing information for a patient given by the hospital with one row representing one patient.
- Provided with a dataset with inherent flaws, we had to adjust, scrape through, and clean up the data.
- Afterwards, we used recursive feature elimination to find what features contributed most to survival.
- This allowed us to train the Keras Sequential Model to predict data.
- The predictions from this model were submitted to get a score on how accurate it was.
- Bao
- Taylor
- Arul
- code: All our code and scripts for data analysis, modeling, and visualization.
- docs: Any additional documentation or reports/outline of our project.
- resources: Useful resources related to the Datathon including Python documentation, programming library resources, etc.
If you have any questions do not hesitate to reach out!
Copyright (c) 2023 Taylor Smith, Arul Dhar, and Bao Nguyen