- author: Dongchun Chen, Ismail 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.
Here we attempt to build a classification model using the Logistic Regression algorithm which can predict whether an individual belongs to the senior (≥65 years) age group or the non-senior (<65 years) age group based on specific features. The model utilizes a supervised machine learning algorithm to identify patterns and relationships within the dataset to make accurate predictions.
The dataset used in this project is a subset of the National Health and Nutrition Examination Survey (NHANES) 2013-2014, created by the Centers for Disease Control and Prevention (CDC). The subset was donated on September 21, 2023, and is designed for predicting respondents' age. The dataset can be found here. The NHANES dataset collects extensive health and nutritional information from a diverse U.S. population, and this particular subset narrows the focus to selected features hypothesized to correlate strongly with age.
First time running the project, run the following from the root of this repository:
conda-lock install --name 522-group31 conda-lock.ymlTo run the analysis, run the following from the root of this repository:
jupyter lab Open age_prediction_report.ipynb in Jupyter Lab
and under Switch/Select Kernel choose
"Python [conda env:522-group31]".
Next, under the "Kernel" menu click "Restart Kernel and Run All Cells...".
conda(version 23.9.0 or higher)conda-lock(version 2.5.7 or higher)jupyterlab(version 4.0.0 or higher)nb_conda_kernels(version 2.3.1 or higher)- Python and packages listed in
environment.yml
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.
