Age Group Prediction

Can the health and nutritional status of Americans be used to classify their age group?

Authors:

• Forgive Agbesi
• Jason Lee
• Michael Hewlett

Summary:

In this analysis we explored the use of several classification models to predict whether a respondent is an adult or senior (essentially below or above age 65) based on their health and nutritional data. Our most promising model used Logistic Regression. While it appeared promising, much of the model’s accuracy was achieved by classifying most respondents as adults, since this was the majority class. Precision and recall for predicting the senior class was quite low. This suggests that the model has considerable room for improvement, which could be achieved through optimizing the hyperparameters and selecting models based on precision, recall, or f1 scores, rather than general accuracy. With the goal of correctly classifying each group, false positive and false negative errors were both equally important for our analysis, and applying class weighting is worth exploring in future research. Once the model performs better on those metrics, it would be worth exploring which health and nutritional features are most predictive of age, which could provide suggestions for strategic public health programs.

Report

The final report can be found here

Usage

Setup

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

1. Clone this GitHub Repository

git clone https://github.com/UBC-MDS/DSCI522-2425-39-FMJ

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= . Copy and paste that URL into your browser.

3. Navigate to the root of this project on your computer using the command line and enter 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-all

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

make all

5. To render the final report, enter the following command in the terminal in the project root:

quarto render reports/age_group_classification.qmd --to pdf

Clean up

1. To shut down the container and clean up the resources, type Ctrl + 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)
• Docker
• For a full list of dependencies, please refer to environment.yml

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.

License

This project is licensed under a Massachusetts Institute of Technology (MIT) License. See the license filefor more information.

References

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