This project involves conducting an initial data and exploratory analysis of Instacart's sales patterns to gain insights and suggest strategies for better segmentation. The goal is to help Instacart target different customer profiles with appropriate marketing campaigns and products. Key questions addressed in this analysis include determining the busiest days and hours, identifying high spending times, simplifying price point groupings, assessing popular product categories, and understanding customer behavior based on loyalty, region, demographics, and shopping habits.
- Setup and basic Python programming
- Pandas for data import and basic exploratory tasks
- Data wrangling and subsetting
- Checking data consistency
- Combining and exporting data
- Deriving new variables
- Grouping and aggregating data
- Visualizing data with Python
- Coding etiquette, Excel reporting, and final analysis
The following data sets from "The Instacart Online Grocery Shopping Dataset 2017" were accessed via Kaggle:
- orders
- orders_products_prior
- products
- departments
Note: Data on customers and the 'prices' column in the 'products' dataset were fabricated for this analysis.
- customers
- 01 Project Management: Project brief
- 02 Data: Original and prepared dataframes (note: data files are not uploaded to GitHub due to their size)
- 03 Scripts: Jupyter notebooks containing Python scripts for analysis
- 04 Analysis: Visualizations used to develop and explain insights
- 05 Sent_to_client: The final report presented in Excel
- Uses Jupyter notebooks and the following Python libraries for analysis:
- Pandas: for data analysis
- Numpy: for mathematical equations
- Seaborn: for data visualizations
- Matplotlib: for data visualizations
- SciPy: for mathematical equations
The Instacart Grocery Basket Analysis project aims to provide actionable insights for targeted marketing strategies and product recommendations based on comprehensive data analysis. By leveraging Python programming and data analysis techniques, this project aims to optimize Instacart's sales patterns and improve the customer experience.