☕ Coffee Chain Sales Analysis

Overview

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.

Dataset:

Coffee Chains Dataset

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Objectives

• Analyze total sales and profit across markets
• Identify top-performing products and categories
• Visualize monthly sales trends
• Explore the relationship between sales and profit

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Tools & Libraries

• Python 3
• pandas
• numpy
• matplotlib
• seaborn
• Jupyter Notebook

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Key Insights

• 📈 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.

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Visualizations

(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

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Project Structure

Coffee_Chain_Analysis/ │── data/ │ └── coffee_chains.csv │── images/ │── notebooks/ │ └── coffee_analysis.ipynb │── requirements.txt └── README.md

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How to Run

1️⃣ Clone the repository

git clone https://github.com/Blladerunner/coffee-chain-analysis.git
cd coffee-chain-analysis

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