Python_Instacart_Grocery_Basket_Analysis

Used Python to wrangle and analyze multiple datasets for Instacart. Created new columns and flags for customer profiling, analyzed order behavior across customer groups, and identified key data connections. Compiled findings and recommendations into a comprehensive report for Instacart stakeholders.

Project Overview:

For this project I took the role of a data analyst at a grocery-delivery company, Instacart. Instacart: a tech company that operates as a same-day-grocery-pick-up-and-delivery service in the U.S. and Canada. I was provided with sales, product, and customer data sets, which I needed to prepare and manipulate for analysis using Python. I finished up by presenting my findings to the stakeholders at Instacart.

Key Questions and Objectives:

● 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.

● 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.

● 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.

● 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.

● The marketing and sales teams are particularly interested in the different types of customers in their system and how their ordering behaviors differ. For example:

• What’s the distribution among users in regards to their brand loyalty (i.e., how often do they return to Instacart)?

• Are there differences in ordering habits based on a customer’s loyalty status?

• Are there differences in ordering habits based on a customer’s region?

• Is there a connection between age and family status in terms of ordering habits?

• What different classifications does the demographic information suggest? Age? Income? Certain types of goods? Family status?

• What differences can you find in ordering habits of different customer profiles? Consider the price of orders, the frequency of orders, the products customers are ordering, and anything else you can think of.

Data

• Customer Data set

• Data Dictionary

• Citation: “The Instacart Online Grocery Shopping Dataset 2017”, Accessed from www.instacart.com/datasets/grocery-shopping-2017 via Kaggle on .

Tools used:

macOS | Excel | Python |

The data was analyzed using Python and the following libraries:

• Pandas: for data analysis
• Numpy: for mathematical equations
• Seaborn: for data visualizations
• Matplotlib: for data visualizations
• SciPy: for mathematical equations

Project Deliverables

The final report presented in Excel.

Documents provided:

The project files are divided between the following folders:

● 01 Project Management:

• Project Brief
• Data Dictionary.

● 02 Data:

Separated into two subfolders Original and Prepared Data. These contain the original data frames and the data frames after they have been cleaned and prepared for analysis respectively. (Data files not uploaded to GitHub due to size.)

• Original Data
• Prepared Data

● 03 Scripts:

The Jupyter notebooks containing the coding for the analysis.

● 04 Analysis:

The Visualizations subfolder contains the visualizations used for developing and explaining insights.

• Visualizations

● 05 Sent to Client:

The final report presented in Excel, including:

• Population Flow
• Consistency checks
• Wrangling steps
• Column derivations
• Visualizations
• Recommendations