This repo is a data analysis based on data collected by a UCI run survey. In this survey, people were presented with different driving scenarios and asked if they would accept a coupon in the given circumstances. This repo presents findings examining the Bar and Coffee House subset of coupons.
- Python: 3.11.3
- Anaconda: 2.4.0
- Jupyter Notebook: 6.5.4
- Matplotlib: 3.7.1
- Seaborn: 0.12.2
- Pandas: 1.5.3
- Numpy: 1.24.3The easiest way to install Anaconda is from the online download source. See the installation page for more details on installation process. After downloading open Anaconda Navigator to download Jupyter Notebook
Additional dependencies can be installed from the command line using Conda:
conda install -c matplotlib seaborn pandas numpyAfter installing dependencies, run a new Jupyter Notebook instance from root of project:
jupyter notebookSee included Jupyter Notebook for details of analysis.
Findings can roughly be broken down into two sections:
Overall, about 41% of coupons were indicated as "would use" as part of the survey.
Certain groups, such as individuals indicating regular visits to bars, were shown to be more likely to accept a coupon.
Targeting individuals between 25-30 who regularly visit bars would likely lead to an increase in coupon use.
Coffee House Coupons Observations
An analysis of coffee house coupons examining behavior across different age groups was performed.
On average, most age groups indicated a "would use" ratio of about 50%.
The "Below 21" age group indicated a significantly higher ratio, around 70%.
Targetting the "Below 21" age group would likely lead to signicantly higher coupon use.