Portfolio of Python code written for data analysis projects
This python project uses the sample dataset for Instacart to perform data and exploratory analysis of customer ordering and spending habits. This work was conducted for educational purposes only.
The python analysis was conducted in Jupyter notebooks. The entire project folder has been included here, however the data has not been uploaded due to file size restrictions.
Key questions answered in this analysis are:
- What are the busiest days of the week and hours of the day (in order to schedule ads at times when there are fewer orders)?
- Are there particular times of the day when people spend the most money?
- Create simpler price range groupings to help marketing and sales advertise products based on price range.
- What types of products are more popular than others? Which departments have the highest frequency of product orders?
- What is the dustribution among users in terms of loyalty to Instacart (i.e. ordering frequency)? Are there differences in ordering habits based on a customers loyalty status? Are there differences in ordering habits based on a customers region? Is there a connection between age and family status in terms of ordering habits?
- What different classifications does the demographic information suggest?
- What differences in ordering habits are there based on customer profiles?
Data citation: "The Instacart Online Grocery Shopping Dataset 2017", accessed from https://www.instacart.com/datsets/grocery-shopping-2017 on May 12, 2022.
Note that customer data and the prices of products were fabricated for the purposes of this analysis, and are not included in the original Instacart data.