Walmart Sales Data Analysis

This project involves end-to-end analysis of a Walmart sales dataset, including data cleaning, exporting to a relational database (MySQL), and executing SQL queries for deriving business insights.

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Project Overview

The key goals of this project are to:

• Perform data cleaning and transformation using Pandas in Jupyter Notebook
• Export the cleaned dataset to a MySQL relational database using SQLAlchemy and PyMySQL
• Use SQL queries to derive meaningful business insights, such as:
  • Top-performing product categories
  • Peak sales times and shifts
  • Year-over-year revenue changes per branch
  • Revenue trends and customer behavior
  • Identifying best-selling product categories.
  • Sales performance by time, city, and payment method.
  • Profit margin analysis by branch and category.

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Dataset

• Source: Kaggle Walmart sales data (~10,000 rows)
• Format: CSV
• Columns include:
  • invoice_id, branch, city, category, unit_price, quantity
  • date, time, payment_method, rating, profit_margin, total

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Tools & Technologies Used

Tool	Purpose
pandas	Data cleaning and manipulation
JupyterLab	Interactive development environment
SQLAlchemy	Database abstraction/connection layer
PyMySQL	MySQL database connector
MySQL	Relational database and SQL querying

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Setup

# Clone the repo
git clone https://github.com/sushruta19/Walmart-Sales-Analysis.git
cd Walmart-Sales-Analysis

# Create virtual environment
python -m venv venv
venv\Scripts\activate.bat   # On Windows(Command Prompt)
source venv/bin/activate    # On Linux(Bash)

# Install dependencies
pip install -r requirements.txt

# Run Jupyter-lab or notebook
jupyter-lab