Retail_Sales_Analysis.sql

select* from ['sales_data'];

---RETAIL_SALES_ANALYSIS---

SELECT 
    COUNT(*) 
FROM ['sales_data'];

-- Data Cleaning
SELECT * FROM ['sales_data']
WHERE transaction_id IS NULL

SELECT * FROM ['sales_data']
WHERE sale_date IS NULL

SELECT * FROM ['sales_data']
WHERE sale_time IS NULL

SELECT * FROM ['sales_data']
WHERE 
    transaction_id IS NULL
    OR
    sale_date IS NULL
    OR 
    sale_time IS NULL
    OR
    gender IS NULL
    OR
    category IS NULL
    OR
    quantity IS NULL
    OR
    cogs IS NULL
    OR
    total_sale IS NULL;
    
-- 
DELETE FROM ['sales_data']
WHERE 
    transaction_id IS NULL
    OR
    sale_date IS NULL
    OR 
    sale_time IS NULL
    OR
    gender IS NULL
    OR
    category IS NULL
    OR
    quantity IS NULL
    OR
    cogs IS NULL
    OR
    total_sale IS NULL;
    
-- Data Exploration

-- How many sales we have?
SELECT COUNT(*) as total_sale FROM ['sales_data']

-- How many uniuque customers we have ?

SELECT COUNT(DISTINCT customer_id) as total_sale FROM ['sales_data']



SELECT DISTINCT category FROM ['sales_data']


-- Data Analysis & Business Key Problems & Answers

-- My Analysis & Findings
-- Q.1 Write a SQL query to retrieve all columns for sales made on '2022-11-05
-- Q.2 Write a SQL query to retrieve all transactions where the category is 'Clothing' and the quantity sold is more than 10 in the month of Nov-2022
-- Q.3 Write a SQL query to calculate the total sales (total_sale) for each category.
-- Q.4 Write a SQL query to find the average age of customers who purchased items from the 'Beauty' category.
-- Q.5 Write a SQL query to find all transactions where the total_sale is greater than 1000.
-- Q.6 Write a SQL query to find the total number of transactions (transaction_id) made by each gender in each category.
-- Q.7 Write a SQL query to calculate the average sale for each month. Find out best selling month in each year
-- Q.8 Write a SQL query to find the top 5 customers based on the highest total sales 
-- Q.9 Write a SQL query to find the number of unique customers who purchased items from each category.
-- Q.10 Write a SQL query to create each shift and number of orders (Example Morning <=12, Afternoon Between 12 & 17, Evening >17)



 -- Q.1 Write a SQL query to retrieve all columns for sales made on '2022-11-05

SELECT *
FROM ['sales_data']
WHERE sale_date = '2022-11-05';


-- Q.2 Write a SQL query to retrieve all transactions where the category is 'Clothing' and the quantity sold is more than 4 in the month of Nov-2022

SELECT 
    *
FROM ['sales_data']
WHERE 
    category = 'Clothing'
    AND 
    FORMAT(sale_date, 'yyyy-MM') = '2022-11'
    AND 
    quantity >= 4;



-- Q.3 Write a SQL query to calculate the total sales (total_sale) for each category.

SELECT 
    category,
    SUM(total_sale) as net_sale,
    COUNT(*) as total_orders
FROM ['sales_data']
GROUP BY category

-- Q.4 Write a SQL query to find the average age of customers who purchased items from the 'Beauty' category.

SELECT
    ROUND(AVG(age), 2) as avg_age
FROM ['sales_data']
WHERE category = 'Beauty'


-- Q.5 Write a SQL query to find all transactions where the total_sale is greater than 1000.

SELECT * FROM ['sales_data']
WHERE total_sale > 1000


-- Q.6 Write a SQL query to find the total number of transactions (transaction_id) made by each gender in each category.

SELECT 
    category,
    gender,
    COUNT(*) as total_trans
FROM ['sales_data']
GROUP BY 
    category,
    gender
ORDER BY 1


-- Q.7 Write a SQL query to calculate the average sale for each month. Find out best selling month in each year

SELECT 
       year,
       month,
    avg_sale
FROM 
(    
SELECT 
    YEAR (sale_date) as year,
    MONTH (sale_date) as month,
    AVG(total_sale) as avg_sale,
    RANK() OVER(PARTITION BY YEAR ( sale_date) ORDER BY AVG(total_sale) DESC) as rank
FROM ['sales_data']
GROUP BY YEAR(sale_date), MONTH(sale_date)
) as t1
WHERE rank = 1
    
-- ORDER BY 1, 3 DESC

-- Q.8 Write a SQL query to find the top 5 customers based on the highest total sales 

SELECT top 5
    customer_id,
    SUM(total_sale) as total_sales
FROM ['sales_data']
GROUP BY customer_id
ORDER BY total_sales DESC

-- Q.9 Write a SQL query to find the number of unique customers who purchased items from each category.


SELECT 
    category,    
    COUNT(DISTINCT customer_id) as cnt_unique_cs
FROM ['sales_data']
GROUP BY category;



-- Q.10 Write a SQL query to create each shift and number of orders (Example Morning <12, Afternoon Between 12 & 17, Evening >17)

WITH hourly_sale
AS
(
SELECT *,
    CASE
        WHEN DATEPART(HOUR, sale_time) < 12 THEN 'Morning'
            WHEN DATEPART(HOUR, sale_time) BETWEEN 12 AND 17 THEN 'Afternoon'
            ELSE 'Evening'
    END as shift
FROM ['sales_data']
)
SELECT 
    shift,
    COUNT(*) as total_orders    
FROM hourly_sale
GROUP BY shift

-- End of project