Instatcart Grocery Basket Analysis Project

Project Breif:

Instacart, an online grocery store that operates through an app wants to uncover more information about their sales patterns. The Instacart stakeholders are most interested in the variety of customers in their database along with their purchasing behaviors. They want to target different customers with applicable marketing campaigns to see whether they have an effect on the sale of their products.

Key Questions:

1. The sales team needs to know what the busiest days of the week and hours of the day are (i.e., the days and times with the most orders) in order to schedule ads at times when there are fewer orders.
2. They also want to know whether there are particular times of the day when people spend the most money, as this might inform the type of products they advertise at these times.
3. Instacart has a lot of products with different price tags. Marketing and sales want to use simpler price range groupings to help direct their efforts.
4. Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders
5. The marketing and sales teams are particularly interested in the different types of customers in their system and how their ordering behaviors differ

Data Set:

Customer Data Set: contains customer information such as user id, first name, last name, gender state, age, data joined, dependents, family status and income Departments Data Set: contains department id and the department name Products Data Set: contains product information such as product id, product name, aisle id, department id and prices Orders Data Set: contains order id, user id, evaluation set, order number, order day of week, order hour of day, days since prior order

Contents of Folder uploaded:

1. Title Page
2. Data Citation
3. Population Flow
4. Consistency Checks
5. Wrangling Steps
6. Columns Derived
7. Visualizations
8. Recommendations

Tools:

For this project the following python libraries were used:

• Pandas and Numpy for data analysis
• Os for interacting with operating system
• Seaborn, Matplotlib.pyplot and scipy for visualizations

Executing the code:

The code is available as jupyter notbook available under/scripts/