Link to Newspaper Article: https://dailynexus.com/2023-06-01/flex-your-schedule-the-best-time-to-hit-the-recreation-center/
Outsourced Excel files from my school's recreation center including the Fall 2019 and Fall 2022 quarter. Had to reformat the data in Excel to be able to input it into the Python Pandas library. Once the data was reformatted I did some data preprocessing to get the data into some tables used for data visualizations.
Once the data was formatted correctly I now can go through with exploring the dataset to see any trends I see as well as comparisons between Fall 2019 and Fall 2022. Using Matplotlib and Plotly I was able to spot the similarities between Fall 2019 and Fall 2022 data meaning it would be a solid idea to combine the dataset so we have more data to work with. Once the datasets were combined I made some charts showing charts such as the average amount of people coming into the Recreation Center per hour/day throughout the quarter. One final chart I created compares the average amount of students who come into the Recreation Center during midterm/finals week and weeks that are not midterm/finals week.
Some charts that I created in the exploratory analysis were directly used for the article, but for some others, I utilized Canva and Photoshop to create a much more visually appealing chart for the journalism article which was published online and in print and featured on the Daily Nexus and UCSB Recreation Center social media page.
I looked at using an ARIMA (AutoRegressive Integrated Moving Average Model), a model that works very well with time forecasting. ARIMA leverages historical data. By considering values from previous times, the ARIMA model did a good job of predicting future values.