🛒 Walmart_Python-SQL_Business_Answers

Analyze Walmart's retail sales data end-to-end: from cleaning raw data using Python to answering business-critical questions using MySQL. This project provides actionable insights into product performance, sales volume trends, customer ratings, payment behavior, and branch efficiency.

---

🧠 About the Project

This is a hands-on SQL + Python analytics project using real-world Walmart data.

• Data is cleaned and transformed using Python (pandas)
• Key fields like sales volume, customer behavior, and profitability margins are standardized
• Cleaned data is stored as walmart_clean.csv
• SQL queries (MySQL) explore retail KPIs, customer trends, and branch performance

---

🛠️ Technologies Used

• Python (pandas) – Data cleaning and preprocessing
• MySQL – SQL-based analytics and business intelligence
• Jupyter Notebook – For step-by-step Python execution
• Kaggle Dataset – Walmart Sales Dataset

---

📦 Installation & Setup

1. Clone the repository

git clone https://github.com/your-username/walmart-sales-analysis.git
cd walmart-sales-analysis

2. Install Python requirements

pip install pandas

3. Clean the dataset with Python

Run the provided Jupyter Notebook:

• walmart_data_cleaning.ipynb This notebook performs data cleaning steps — handling missing values, correcting types, removing duplicates — and exports a clean version of the dataset as walmart_clean.csv.

4. Load data into MySQL

Create the table schema in MySQL:

CREATE TABLE walmart_clean (
    invoice_id VARCHAR(20),
    branch VARCHAR(10),
    city VARCHAR(50),
    category VARCHAR(50),
    unit_price FLOAT,
    quantity INT,
    date DATE,
    time TIME,
    payment_method VARCHAR(20),
    rating FLOAT,
    profit_margin FLOAT
);

Then import the walmart_clean.csv file using MySQL Workbench or your preferred import tool.

---

📊 Business Questions Answered

This project answers real-world analytics questions relevant to Walmart and retail operations:

1. 📦 What are the top-selling categories by units sold?
2. 🏪 Which categories perform best at each branch?
3. 🕒 What are the busiest times of day and days of the week?
4. ⭐ Do higher-rated categories correlate with higher sales?
5. 💰 Which branches are most efficient in terms of sales volume and profit margin?
6. 💳 What are the most popular payment methods and average basket size?
7. ⚠️ Which categories are underperforming in both units and customer ratings?
8. 📆 What are the monthly sales trends (seasonality)?
9. 🧾 What is the average basket size per branch?
10. 🔁 How are unit sales changing year-over-year by branch?

---

📈 Sample Insights

• 🔝 Food & Beverages consistently leads in total units sold.
• 🕒 Sales peak during evenings and weekends, especially in Branch B.
• ⭐ Higher-rated categories tend to sell more units, showing a positive link between customer satisfaction and demand.
• 💳 Ewallet is the most preferred payment method across branches.
• ⚠️ Fashion Accessories shows both low units and low ratings — a clear candidate for product review or promotions.
• 📆 Branch C experienced a year-over-year decline in unit sales, highlighting areas needing operational focus.

---

📁 Project Structure

📦 walmart-sales-analysis/
├── walmart_data_cleaning.ipynb   # Python notebook for cleaning and exporting data
├── walmart_clean.csv             # Cleaned dataset exported from notebook
├── queries.sql                   # MySQL queries answering business questions
└── README.md                     # This documentation

---

🤝 Contributing

Contributions are welcome! Fork the repo, make your changes, and open a pull request.