- This project presents a comprehensive analysis of sales data from a coffee vending machine. The goal is to uncover actionable insights regarding customer purchasing behavior, sales performance, payment preferences, and product popularity.
- The findings are intended to support data-driven decision-making and optimize the vending machine's operational efficiency and profitability.
💳 Preferred Payment Method: Card payments dominate over cash transactions.
☕ Top-Selling Beverages: Latte and Americano with Milk lead as the most popular items.
🕒 Peak Sales Hours: Morning hours, particularly between 9 AM and 11 AM, record the highest transaction volume.
📅 Sales Distribution: Sales are stronger on weekdays, with distinct patterns observed across different months.
🎯 Customer Retention: A small subset of loyal customers is responsible for a significant proportion of repeat purchases.
- Consider offering promotions during non-peak hours to balance the sales load.
- Analyze customer cards further to identify demographics behind loyal customers.
- Monitor the least popular products to assess if they should be replaced or promoted differently.
Programming: Python 🐍
Libraries: Pandas, Matplotlib, Seaborn
Environment: Jupyter Notebook
To replicate this project install the dependencies by running:
- https://github.com/vishuuu3/Coffee-Sales-Analysis-Report.git
- cd CoffeeSales
- pip install -r Requirements.txt
This project focused on data cleaning, exploratory data analysis (EDA), and generating actionable business insights. In future iterations, I plan to:
-Explore machine learning models to forecast future sales trends.
This project is licensed under the Apache2 License - see the License file for more details.