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.
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.
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.
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.
The analysis aims to answer the following business questions:
- What are the total sales and quantity sold for the 6-month period?
- Which product category is top-selling?
- What is the percentage total for each product category towards sales?
- What product name is top selling?
- What is the sales distribution by store location?
- How is each product category performing by store location?
- What are the hourly sales trends?
- Are there any trends in sales by weekday?
- How do sales vary by day?
- Are there any seasonal patterns in sales?
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.
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.
To run this project:
- Clone the repository.
- Install the required libraries (e.g., pandas, matplotlib, seaborn).
- Load the dataset and execute the analysis scripts.
This project is licensed under the MIT License. See the LICENSE file for details.