🛒 Walmart Sales Analysis – SQL | Python | Power BI

A complete end-to-end data analytics project exploring Walmart’s sales performance using SQL, Python (Jupyter Notebook), and Power BI. This project uncovers key insights like top-performing categories, customer behavior trends, branch performance, and revenue patterns.

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📂 Project Files Include:

File	Description
walmart.ipynb	Exploratory Data Analysis, Cleaning, Transformations
walmart_clean_data.csv	Final cleaned dataset used for SQL + Power BI
walmart_project.sql	All SQL queries + 7 business problem solutions
Walmart_Dashboard.pbix	Complete Power BI interactive dashboard

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🚀 Project Workflow

1️⃣ Data Cleaning (Python – Jupyter Notebook)

Performed inside walmart.ipynb:

• Removed missing values

• Standardized categories

• Converted date and time columns

• Added new columns:

  • DayType, MonthSort, Month
  • Shift (Morning/Afternoon/Evening)

• Created total → total = unit_price * quantity

• Final dataset saved as: walmart_clean_data.csv

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🧠 2️⃣ SQL Analysis (walmart_project.sql)

All SQL queries + insights are included

Business Problems Solved

1. Most Profitable Category in Each Branch

Insight: Home & Lifestyle is the highest-revenue category consistently across branches.

2. Branches With Declining Month-Over-Month Sales

Insight: Branch WALM001 has multiple declining months → performance instability.

3. Customer Spending Pattern by Time of Day

Insight: Evening has the highest revenue and maximum invoices.

4. Categories With High Sales but Low Ratings

Insight:

• Fashion Accessories
• Home & Lifestyle
• Electronic Accessories These generate high revenue but have ratings below 6 → customer dissatisfaction.

5. Payment Method With Highest Average Order Value

Insight: Cash users have the highest average order value.

6. Branch With Highest Customer Satisfaction

Insight: WALM004 has the highest average rating (≈7.0).

7. Weekend vs Weekday Revenue

Insight: Weekdays outperform weekends in both sales volume and revenue.

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📊 3️⃣ Power BI Dashboard

The Walmart Power BI Dashboard:

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🔍 Key Insights Summary

Insight Category	Key Finding
🔝 Highest Revenue Category	Home & Lifestyle
🏬 Most Inconsistent Branch	WALM001
⏰ Busiest Time of Day	Evening
😕 High Sales but Low Ratings	Fashion, Electronics, Home & Lifestyle
💰 Highest Avg Order	Cash payment
😀 Best Customer Satisfaction	WALM004
📅 Higher Revenue	Weekdays

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🛠️ Tech Stack Used

🔹 Python

Pandas, NumPy

🔹 SQL

Window functions, Aggregations, CTEs, Rankings

🔹 Power BI

Interactive visuals, slicers, KPIs, DAX basics

🔹 Tools

VS Code, Jupyter Notebook, MySQL Workbench

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▶️ How to Run the Project Locally

1. Clone the repo

git clone https://github.com/yourusername/Walmart-Sales-Analysis.git
cd Walmart-Sales-Analysis

2. Run the Notebook

Open walmart.ipynb in Jupyter Notebook or VS Code.

3. Execute SQL

Open walmart_project.sql in MySQL Workbench Use database:

USE walmart_db;

4. Open Power BI Dashboard

Download Power BI Desktop, then open:

Walmart_Dashboard.pbix

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🌟 Future Enhancements

• Add sales forecasting using ML
• Convert insights into a Streamlit dashboard
• Deploy dashboard online
• Build automated ETL pipelines

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👩‍💻 Author

Harshita Pandey | SQL | Python | Power BI 📧 harshitapandey2910@gmail.com