This project performs an Exploratory Data Analysis (EDA) on a global coffee chain dataset using Python (pandas, matplotlib, seaborn).
The goal is to uncover insights about sales, profit, product performance, and regional trends.
- Analyze total sales and profit across markets
- Identify top-performing products and categories
- Visualize monthly sales trends
- Explore the relationship between sales and profit
- Python 3
- pandas
- numpy
- matplotlib
- seaborn
- Jupyter Notebook
- 📈 Sales show steady growth across months.
- 🏆 Espresso and Latte are among the top-selling products.
- 🌍 The U.S. and Canada markets drive the majority of profit.
- 💸 There’s a clear positive correlation between Sales and Profit.
- 📦 Product Type analysis shows key segments contributing to revenue.
(All plots are saved in the images/ folder and rendered below.)
1️⃣ Monthly Sales Trend
2️⃣ Profit by Country
3️⃣ Top Products by Sales
4️⃣ Sales by Category
5️⃣ Profit by Category
6️⃣ Sales vs Profit
Coffee_Chain_Analysis/ │── data/ │ └── coffee_chains.csv │── images/ │── notebooks/ │ └── coffee_analysis.ipynb │── requirements.txt └── README.md
1️⃣ Clone the repository
git clone https://github.com/Blladerunner/coffee-chain-analysis.git
cd coffee-chain-analysis