Coffee Vending Machine Sales Analysis

📌 Project Overview

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.

📂 Dataset

• Source: Kaggle - Coffee Sales Dataset
• Description: Includes transactional data such as date, product sold, payment type, card ID, transaction amount, and time of purchase.

🔍 Key Insights

• 💳 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.

📊 Visual Highlights

Selected visualizations from the analysis:

1. 📈 Daily Sales Trend
   
2. ☕ Best-Selling Products
   
3. ⏰ Peak Sales Hours
   

🎯 Next Steps / Recommendations

• 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.

🛠️ Tools and Technologies

• Programming: Python 🐍
• Libraries: Pandas, Matplotlib, Seaborn
• Environment: Jupyter Notebook

⚙️ Installation

To replicate this project, install the dependencies by running:

git clone https://github.com/AdemCE-eng/CoffeeSales.git
cd CoffeeSales
pip install -r requirements.txt

📘 Notebook

You can view the full Jupyter Notebook here for detailed code, visualizations, and analysis.

🔮 Future Work

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.
• Apply clustering techniques to segment customers based on behavior and purchase history.

📝 License

This project is licensed under the MIT License - see the LICENSE file for more details.