https://github.com/saba-gul/walmart-sales-analysis
This project involves analyzing Walmart sales data to gain insights into various aspects of the business, such as sales trends, product performance, customer demographics, and more.
https://github.com/saba-gul/walmart-sales-analysis
business-analytics business-intelligence exploratory-data-analysis powerbi query-language retail-ecomm sales sql walmart
Last synced: 8 months ago
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This project involves analyzing Walmart sales data to gain insights into various aspects of the business, such as sales trends, product performance, customer demographics, and more.
- Host: GitHub
- URL: https://github.com/saba-gul/walmart-sales-analysis
- Owner: Saba-Gul
- Created: 2024-07-24T04:40:45.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-24T05:07:50.000Z (about 1 year ago)
- Last Synced: 2025-01-13T16:28:18.787Z (9 months ago)
- Topics: business-analytics, business-intelligence, exploratory-data-analysis, powerbi, query-language, retail-ecomm, sales, sql, walmart
- Homepage:
- Size: 337 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Walmart Sales Data Analysis
## About
We are analyzing Walmart's sales data to identify high-performing branches and products, analyze the sales patterns of various products, and understand customer behavior. The primary objective is to enhance and optimize sales strategies. The dataset utilized in this project is sourced from the Kaggle Walmart Sales Forecasting Competition.
## Purposes of the Project
The main goal of this project is to gain insights from Walmart's sales data, exploring various factors that influence sales across different branches. This analysis aims to:
- Identify high-performing branches and products.
- Analyze sales patterns and trends.
- Understand customer behavior and preferences.
- Optimize sales strategies based on data-driven insights.
## About Data
This project's data was obtained from the [Kaggle Walmart Sales Forecasting Competition](https://www.kaggle.com/datasets/yasserh/walmart-dataset)
and encompasses sales transactions from three Walmart branches situated in Mandalay, Yangon, and Naypyitaw. The dataset contains 17 columns and 1000 rows with information on sales transactions, including:
- Transaction date
- Product details
- Sales amounts
- Customer information
- Branch details
## Business Questions and SQL Queries
### Generic Questions
1. **How many distinct cities are present in the dataset?**
```sql
SELECT DISTINCT city
FROM walmartsales;
```
2. **In which city is each branch situated?**
```sql
SELECT DISTINCT city, branch
FROM walmartsales;
```
### Product Analysis
1. **How many distinct product lines are there in the dataset?**
```sql
SELECT DISTINCT COUNT(DISTINCT product_line) AS num_of_products
FROM walmartsales;
```
2. **What is the most common payment method?**
```sql
SELECT payment_method, COUNT(payment_method) AS common_payment_method
FROM walmartsales
GROUP BY payment_method
ORDER BY common_payment_method DESC;
```
3. **What is the most selling product line?**
```sql
SELECT product_line, COUNT(product_line) AS cnt_product_line
FROM walmartsales
GROUP BY product_line
ORDER BY cnt_product_line DESC;
```
4. **What is the total revenue by month?**
```sql
SELECT month_name, SUM(total) AS revenue
FROM walmartsales
GROUP BY month_name
ORDER BY revenue DESC;
```
5. **Which month recorded the highest Cost of Goods Sold (COGS)?**
```sql
SELECT month_name, SUM(cogs) AS highest_cogs
FROM walmartsales
GROUP BY month_name
ORDER BY highest_cogs DESC
LIMIT 1;
```
6. **Which product line generated the highest revenue?**
```sql
SELECT product_line, SUM(total) AS total_revenue
FROM walmartsales
GROUP BY product_line
ORDER BY total_revenue DESC
LIMIT 1;
```
7. **Which city has the highest revenue?**
```sql
SELECT city, SUM(total) AS revenue
FROM walmartsales
GROUP BY city
ORDER BY revenue DESC
LIMIT 1;
```
8. **Which product line incurred the highest VAT?**
```sql
SELECT product_line, SUM(VAT) AS highest_vat
FROM walmartsales
GROUP BY product_line
ORDER BY highest_vat DESC
LIMIT 1;
```
9. **Retrieve each product line and add a column `product_category`, indicating 'Good' or 'Bad,' based on whether its sales are above the average.**
```sql
WITH ProductSales AS (
SELECT product_line, SUM(total) AS total_sales
FROM walmartsales
GROUP BY product_line
),
AverageSales AS (
SELECT AVG(total_sales) AS avg_sales
FROM ProductSales
)
SELECT product_line,
CASE
WHEN total_sales > (SELECT avg_sales FROM AverageSales) THEN 'Good'
ELSE 'Bad'
END AS product_category
FROM ProductSales;
```
10. **Which branch sold more products than the average product sold?**
```sql
WITH AverageProducts AS (
SELECT AVG(total_quantity) AS avg_quantity
FROM (
SELECT SUM(quantity) AS total_quantity
FROM walmartsales
GROUP BY branch
) AS BranchTotals
)
SELECT branch, SUM(quantity) AS total_quantity
FROM walmartsales
GROUP BY branch
HAVING SUM(quantity) > (
SELECT avg_quantity
FROM AverageProducts
);
```
11. **What is the most common product line by gender?**
```sql
SELECT gender, product_line, COUNT(product_line) AS popular_product
FROM walmartsales
GROUP BY product_line, gender
ORDER BY popular_product DESC;
```
12. **What is the average rating of each product line?**
```sql
SELECT product_line, AVG(rating) AS avg_rating
FROM walmartsales
GROUP BY product_line
ORDER BY avg_rating DESC;
```
### Sales Analysis
1. **Number of sales made in each time of the day per weekday.**
```sql
SELECT time_of_day, day_name, COUNT(invoice_id) AS num_sales
FROM walmartsales
GROUP BY time_of_day, day_name
ORDER BY day_name, time_of_day;
```
2. **Identify the customer type that generates the highest revenue.**
```sql
SELECT customer_type, SUM(total) AS revenue_per_customer_type
FROM walmartsales
GROUP BY customer_type
ORDER BY revenue_per_customer_type DESC;
```
3. **Which city has the largest tax percent/ VAT (Value Added Tax)?**
```sql
SELECT city, ROUND(AVG(VAT), 2) AS vat_per_city
FROM walmartsales
GROUP BY city
ORDER BY vat_per_city DESC;
```
4. **Which customer type pays the most VAT?**
```sql
SELECT customer_type, AVG(VAT) AS vat_customer_type
FROM walmartsales
GROUP BY customer_type
ORDER BY vat_customer_type DESC;
```
### Customer Analysis
1. **How many unique customer types does the data have?**
```sql
SELECT DISTINCT customer_type
FROM walmartsales;
```
2. **How many unique payment methods does the data have?**
```sql
SELECT DISTINCT payment_method
FROM walmartsales;
```
3. **Which is the most common customer type?**
```sql
SELECT customer_type, COUNT(*) AS cnt_customer_type
FROM walmartsales
GROUP BY customer_type;
```
4. **Which customer type buys the most?**
```sql
SELECT customer_type, SUM(total) AS revenue_per_cust_type
FROM walmartsales
GROUP BY customer_type
ORDER BY revenue_per_cust_type DESC;
```
5. **What is the gender of most of the customers?**
```sql
SELECT gender, COUNT(*) AS gender_dist
FROM walmartsales
GROUP BY gender
ORDER BY gender_dist DESC;
```
6. **What is the gender distribution per branch?**
```sql
SELECT branch, gender, COUNT(gender) AS gender_dis
FROM walmartsales
GROUP BY gender, branch
ORDER BY branch;
```
7. **Which time of the day do customers give the most ratings?**
```sql
SELECT time_of_day, COUNT(rating) AS rat_cnt
FROM walmartsales
GROUP BY time_of_day
ORDER BY rat_cnt DESC;
```
8. **Which time of the day do customers give the most ratings per branch?**
```sql
WITH RatingsCount AS (
SELECT time_of_day, branch, COUNT(rating) AS total_ratings
FROM walmartsales
GROUP BY time_of_day, branch
),
RankedRatings AS (
SELECT time_of_day, branch, total_ratings, ROW_NUMBER() OVER(PARTITION BY branch ORDER BY total_ratings DESC) AS rn
FROM RatingsCount
)
SELECT time_of_day, branch, total_ratings
FROM RankedRatings
WHERE rn = 1;
```
9. **Which day of the week has the best average ratings?**
```sql
SELECT day_name, AVG(rating) AS avg_ratings
FROM walmartsales
GROUP BY day_name
ORDER BY avg_ratings DESC;
```
10. **Which day of the week has the best average ratings per branch?**
```sql
WITH DailyAvgRatings AS (
SELECT branch, day_name, AVG(rating) AS avg_ratings
FROM walmartsales
GROUP BY day_name, branch
),
RankedDays AS (
SELECT branch, day_name, avg_ratings, ROW_NUMBER() OVER(PARTITION BY branch ORDER BY avg_ratings DESC) AS rn
FROM DailyAvgRatings
)
SELECT branch, day_name, avg_ratings
FROM RankedDays
WHERE rn = 1;
```
## Revenue and Profit Calculations
1. **Calculate total revenue:**
```sql
SELECT SUM(total) AS total_revenue
FROM walmartsales;
```
2. **Calculate total profit:**
```sql
SELECT SUM(profit) AS total_profit
FROM walmartsales;
```
## Data Visualization
**Note:** For data visualization, we have used PowerBI to visualize sales patterns, revenue trends, and other insights.