Exploratory Data Analysis of Coffee Shop Sales

This repository contains the Python code and analysis conducted to explore and understand the sales data of a coffee shop.

Project Objectives

The following objectives were addressed in this exploratory data analysis:

1. Revenue Analysis:

   • Determine the total revenue generated by each store location over a specific period.
   • Compare the sales performance of different store locations.
   • Determine the average transaction value for each store.

2. Product Performance:

   • Identify the top-selling products in each store location.

3. Sales Trends:

   • Identify the peak sales hours and days of the week.

4. Customer Behavior:

   • Analyze the purchasing patterns of customers over time.
   • Determine the average number of items purchased per transaction.

5. Predictive Analysis (Optional):

   • Predict future demand for specific products based on historical sales data. (If applicable)

Dataset

The dataset, Coffee Shop Sales.xlsx (or .csv), contains transaction-level sales data for a coffee shop, with the following columns:

• transaction_id: Unique transaction identifier.
• transaction_date: Date of the transaction.
• transaction_time: Time of the transaction.
• transaction_day: Day of the week.
• store_id: Store location ID.
• store_location: Name of the store location.
• product_id: Product ID.
• unit_price: Price per unit.
• product_category: General product category (e.g., Coffee, Bakery).
• product_type: Specific product type (e.g., Brewed Coffee, Scone).
• product_detail: Detailed product description.

Tools and Libraries Used

• pandas: For data manipulation and analysis.
• numpy: For numerical computations.
• matplotlib: For creating visualizations.
• seaborn: For advanced statistical visualizations.

Project Structure

• INT375 CA2.ipynb: Jupyter Notebook containing the EDA code and analysis.
• Coffee Shop Sales.xlsx (or .csv): Dataset file.
• ..