Main question: What is the age group of players that are likely to contribute the most to the datasets?

This project explores how player age relates to engagement in video games, using real-world datasets from the Pacific Laboratory for Artificial Intelligence (PLAI) at the University of British Columbia (UBC). Specifically, it aims to identify which age group of players is more likely to contribute a larger volume of data—measured by total playtime—to support ongoing AI research in gaming environments for PLAICraft Research Team.

The analysis is conducted in R using a tidy modelling workflow that includes data wrangling, exploratory data analysis (EDA), and a K-Nearest Neighbours (KNN) regression model. The model is evaluated using 10-fold cross-validation and Root Mean Squared Prediction Error (RMSPE).

• Investigated age-related trends in total playtime using KNN regression
• Cleaned and merged two datasets: players.csv and sessions.csv
• Preprocessed data using recipes, and modelled using parsnip and workflows
• Applied 10-fold cross-validation and reported RMSPE for model accuracy
• Discussed the impact of outliers, dataset size, and audio activity exclusions

• Players aged 17–20 (post-secondary students) had the highest total playtime
• Despite expectations, younger players contributed less overall playtime
• Model RMSPE was 38.26 hours, largely influenced by extreme outliers
• The correlation between age and playtime was positive but weak
• Suggests university-age players may be ideal candidates for targeted research or product development

• R, Jupyter Notebook (via JupyterLab)
• repr, tidymodels, tidyverse, ggplot2

Internal anonymized datasets provided by PLAI:

• players.csv: Contains hashed emails and age
• sessions.csv: Contains session start/end times in UNIX format
  (Only players.csv was used for modelling due to its relevance to the research question)

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