Skip to content

Latest commit

 

History

History
75 lines (50 loc) · 2.8 KB

README.md

File metadata and controls

75 lines (50 loc) · 2.8 KB
Raw

Coffee Shop Performance Analysis

Project Brief

This project focuses on analyzing sales performance across various geographical locations, product categories, pricing strategies, and quantity sold for a coffee shop over a six-month period. The analysis aims to provide insights into sales trends by hour, day, and month, leveraging a comprehensive dataset.

Dataset Features

The dataset includes the following features:

  • Transaction ID: Unique identifier for each transaction.
  • Transaction Date: Date of the transaction.
  • Transaction Time: Time of the transaction.
  • Transaction Quantity: Quantity of products sold in each transaction.
  • Store ID: Identifier for each store location.
  • Store Location: Geographical location of the store.
  • Sales: Total sales amount for each transaction.
  • Unit Price: Price per unit of the product sold.
  • Product Category: Category to which the product belongs.
  • Product Type: Specific type of the product.
  • Product Description: Detailed description of the product.

Data Preprocessing

Before diving into the analysis, I will handle any data issues, including:

  • Missing Values: Identifying and imputing or removing missing data.
  • Duplicates: Checking for and removing duplicate entries.
  • Inconsistent Formatting: Ensuring uniformity in data formats.

Feature Engineering

I will create additional features to facilitate deeper analysis:

  • Hour Column: Extracted from the transaction time.
  • Weekday, Day, Month Columns: Extracted from the transaction date.

Business Questions

The analysis aims to answer the following business questions:

  1. What are the total sales and quantity sold for the 6-month period?
  2. Which product category is top-selling?
  3. What is the percentage total for each product category towards sales?
  4. What product name is top selling?
  5. What is the sales distribution by store location?
  6. How is each product category performing by store location?
  7. What are the hourly sales trends?
  8. Are there any trends in sales by weekday?
  9. How do sales vary by day?
  10. Are there any seasonal patterns in sales?

Visualization and Insights

To answer these questions, I will utilize various charts and visualizations, including:

  • Bar charts for product category performance.
  • Line graphs for sales trends over time.

Recommendations

At the end of the analysis, I will provide insights and actionable recommendations based on the findings, aimed at enhancing sales performance and optimizing inventory management.

Getting Started

To run this project:

  1. Clone the repository.
  2. Install the required libraries (e.g., pandas, matplotlib, seaborn).
  3. Load the dataset and execute the analysis scripts.

License

This project is licensed under the MIT License. See the LICENSE file for details.