Project Overview
Used Python to analyse customer demographics and transactional behaviour for an online grocery store. The project identified distinct customer segments to support targeted product placement and personalised marketing strategies.
Tools Used
- Python (Jupyter | Anaconda) – Scripting Environment
- pandas | numpy | os – Data Processing
- matplotlib | seaborn – Plotting | Visualisation
- Excel – Reporting
Skills Applied
- Script Writing
- Exploratory Analysis | Data Wrangling | Merging | Subsetting | Grouping | Aggregating | Deriving new Variables
- Descriptive Statistics | Segmentation & Profiling | Trend Analysis
- Visualisation | Communicating Insights | Reporting
Data Sourced
This analysis uses publicly available data originally sourced from Instacart via Kaggle. The links as well as an additional customers dataset was provided by CareerFoundry as part of their Data Analytics Course.
- Customers – Customer ID, Name, Surname, Gender, State, Age, Date Joined, Dependants, Family Status, and Income
- Dataset
– Departments – Department id and name
– OrdersProducts – Order id, product id, add to cart order, and reorder indicator.
– Orders – Order is, order number, order day of week, order hour of day, days since prior order.
– Products – Product id, name, aisle, department, and price.
Key Insights
Insight.
Visual — Description
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Visual — Description
Abc.
Key Takeaways
Recommendations
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- Abc
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- Abc
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Links & Deliverables
GitHub Repository — Python Notebooks | Excel Report
Case Study — Read the detailed case study
David Griesel 