This project covers the extraction of data-driven insights for a marketing team in Toronto in order to inform the design of one of their marketing campaigns aimed at improving enrollment in a local continuing education (SCS) program for one of their clients (a local university). The client wants to reach and increase awareness of the program among working professionals and the marketing team wants target bike share users as the target audience. Bike Share Toronto members have been historically known to predominantly use bike share for commuting to and from work, which is a good match to the desired target audience. With bike share ridership in Toronto reaching 4.6 million trips in 2022 and digital ad space available for purchase directly at bike share stations themselves, the marketing team wants to make use of this unique opportunity to reach bike share members. So, this use-case is focused around extracting insights about how and when bike share is used in Toronto. Full details are explained later in this documentation.
🤖 Data Cleaning and Processing
🔬 Exploratory Data Analysis (EDA)