Age Group Predictor

• author: Dongchun Chen, Ismail (Husain) Bhinderwala, Rashid Mammadov & Sienko Ikhabi

A data analysis project for DSCI 522 (Data Science workflows); a course in the Master of Data Science program at the University of British Columbia.

Project Summary

In this project, we set out to classify individuals into two age groups—Seniors (65 years and older) and Adults (under 65 years)—using data from the NHANES 2013-2014 survey. The dataset, consisting of 2,278 entries, was carefully preprocessed to ensure it was clean, well-structured, and balanced for analysis. The dataset can be found here.

We developed a logistic regression model that achieved moderate success, with an accuracy of around 73% and a macro average F1 score of 61% (final metrics pending). While the model performed well in classifying many Seniors and Adults, it left room for improvement, particularly in handling edge cases. Moving forward, we plan to refine the model by engineering new features, experimenting with classification thresholds, and exploring alternative algorithms like K-Nearest Neighbors, SVC, and Naive Bayes. This work provides a solid foundation for using machine learning to support smarter healthcare planning and resource allocation.

Report

The final report can be found here.

Dependencies

• Docker
• VS Code
• VS Code Jupyter Extension

Usage

Setup

If you are using Windows or Mac, make sure Docker Desktop is running.

1. Clone this GitHub repository by pasting the following command in the terminal:

git clone https://github.com/UBC-MDS/DSCI522-2425-group31_age-group-prediction.git

Running the analysis

1. Navigate to the root of this project on your computer using the command line and enter the following command:

docker compose up

2. In the terminal, look for a URL that starts with http://127.0.0.1:8888/lab?token= (for an example, see the highlighted text in the terminal below). Copy and paste that URL into your browser.

3. Once the Jupyter Lab is running in your browser, open a terminal within the Jupyter Lab and execute the following command to reset the project to a clean state (i.e., remove all files generated by previous runs of the analysis):

make clean

4. To run the analysis in its entirety, enter the following command in the terminal:

make all

5. To view the analysis report navigate to reports directory in the root folder and then select age_prediction_report.pdf.

Clean up

1. To shut down the container and clean up the resources, type Cntrl + C in the terminal where you launched the container, and then type docker compose rm

Developer notes

Developer dependencies

• conda (version 23.9.0 or higher)
• conda-lock (version 2.5.7 or higher)

Adding a new dependency

1. Add the dependency to the environment.yml file on a new branch.

2. Run conda-lock -k explicit --file environment.yml -p linux-64 to update the conda-linux-64.lock file.

3. Re-build the Docker image locally to ensure it builds and runs properly.

4. Push the changes to GitHub. A new Docker image will be built and pushed to Docker Hub automatically. It will be tagged with the SHA for the commit that changed the file.

5. Update the docker-compose.yml file on your branch to use the new container image (make sure to update the tag specifically).

6. Send a pull request to merge the changes into the main branch.

Running the test suite

Use the same docker compose up command as described in the Running the analysis section above to launch Jupyter lab. Tests are run using the pytest command in the root of the project. More details about the test suite can be found in the test directory.

License

The analysis report contained herein are licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License. See the license file for more information. If re-using/re-mixing please provide attribution and link to this webpage. The software code contained within this repository is licensed under the MIT license. See the license file for more information.