# data[].attributes.body
1) which region has the highest sales or profit?

2) which products contribute the most and least to overall profit?

3)what are the trends, sales or profits over time

# data[].attributes.body
1)how does the amount of quantity affect the profit?

2)How do the sales affect the profit?

3)How does the state ur in affect ur sales?

4)How does the discount affect the amount of profit you get?

5)Does the shipping mode affect the sales?

# data[].attributes.body
1. Which region has the highest sales?

2. Which product category has the highest profit?

3. Which state has the most orders?

4. How does discount affect profit?

5. Which product ID has the highest quantity sold?

# data[].attributes.body
1) What is the average sales that a region have in a day?

2) What religion has the lowest amount of sales/profit

3)How do you analyse the products ID?

# data[].attributes.headline
5 question on sample data. Taim and Hashem 

# data[].attributes.subject
5 question on sample data. Taim and Hashem 

# data[].attributes.body
-Which state, incomes higher net profeit

-which category acquires the highest sale values.

-what city, has the highest quantity’s of all categories shipped.

-how does the shipping mode effect the sales.

-how does the shipping class, effect the quantity of the object

# data[].attributes.body
1) which products cost the most

2) how does the cost of the product affect the profit

3) which region has the lowest sales

4) what is the average sales in a year

5) Which category acquires the highest sale values

# data[].attributes.body
1. Which city has the highest sales?

2. Which subcategory has the most sales?

3. In which state are chairs sold most?

4. What is the correlation between product number and category?

5. Which product has the highest discount profit?

# data[].attributes.headline
omran alshamsi - saeed beyat - abdulla alghurair - Ali abdulla

# data[].attributes.subject
omran alshamsi - saeed beyat - abdulla alghurair - Ali abdulla

# data[].attributes.body
1. What patterns or trends are evident in the sample data over time?

2. Are there any outliers or anomalies present in the data, and what might be causing them?

3. What correlations exist between different variables in the sample data?

4. How does the sample data compare to the expected or average values within a similar dataset?

5. What predictions or forecasts can be made based on the sample data trends?

# data[].attributes.body
1) What is the total number of orders placed across all states?

2) Which city has the highest number of orders, and what is the percentage share of that city in the total orders?

3) What is the distribution of orders across different segments (e.g., consumer, corporate)?

4) How many orders were shipped using "Standard Class" versus other shipping modes?

5) What is the trend of orders over time (based on the Order Date column)?

# data[].attributes.body
Which customer generates the highest total profit, and in which product category?

What is the most frequently ordered product category in each region?

What is the impact of discounts on profitability for each product category?

Which shipping mode results in the highest overall profit?

What is the trend of total sales across different regions over time?

# data[].attributes.body
1) what region, state and county makes the most sales?

2) what category vs sub category makes the most profit?

3) whats the correlation between the region and category in making the most profit?

# data[].attributes.body
1) Which state makes the highest profit?

2) Which category contributes to the least amount of sales in Florida?

3) Doss the number of sales affect the percentage of the discount?

4) What is the average sales per year?

5) What is the correlation between the postal code and the salws?

6) What is the correlation between the region and categories in terms of shipment per year?

7) Which product costs the most in terms of Product ID?

8) Which product has the least value considering both quantity and discounts?

# data[].attributes.body
1) How does the quantity of the product affect the discount and sales?

2) What is the correlation between the ship mode and the state, region, and city of the customer?

3) How does segment of the customer affect the sales and quantity?

4) What is the correlation between the segment of the customer and their ship mode?

5) How does the state, region, and city of the customer affect the category/subcategory of the product?

# data[].attributes.body
1) In which date, did they receive the highest amount of profit from orders?

2) In which weekday, did they receive the highest amount of profit from orders?

3) What's the average amount of discount? And how is it correlated to the profit and quantity?

4) Evenly distribute the "segment" and tell us which segment is most relevant to the sample data/places the most orders (Consumer, Corporate, Home Office) and give a brief explanation with evidence to back up your answer.

5) What's the most placed order? *Category* (Office Supplies or Technology)

# data[].attributes.body
1. Which items have the biggest and smallest impact on sales

2. Which state has placed the most orders

3. What is the daily average revenue for a region?

4. Which product are priced the highest?

5. Which region reports the lowest revenue

# data[].attributes.body
1. Which segment provided higher profits?

2. Which discounts provided higher profits in the consumer segment compared to other segments, and which discounts in the furniture category provide higher profits compared to the other categories?

3. Can the prices be in 2 decimal places, and how will that impact the original prices? (degree of accuracy)

4. Which ship mode provided the most sales?

5. What is the total price of the discounts applied, and what would've been the sales without discounts applied?

# data[].attributes.body
1. What was the most common category of items ordered.

2. What is the most common shipping class requested?

3. Which region has the lowest amount of shipping orders when compared to others.

4. What is the correlation between shipping modes and regions? Do certain regions prefer faster/slower shipping.

5. Who is the biggest recurring customer?

# data[].attributes.body
1. Which month in which year has the most sales?

2. Which customer had the most sales?

3. What was the average profit from each state?

4. Which category had the most sales and how much sales each subcategory have?

5. How many houses had over 10 sales?

# data[].attributes.body
1- Identify the to three sales representative in terms of total sales volume. What were their respective sales figures?

2- Calculate the average order value for each region. Which region had the highest average and what was the value?

3- what is the contribution of each region to the overall sales revenue ( as percentages)?

# data[].attributes.body
What is the total number of orders shipped to each state?

Which shipping mode is the most frequently used, and how does its usage vary across customer segments?

What is the most common city for customer orders, and which customer segment is most prevalent in that city?

How many orders were placed in each year, and which year had the highest number of orders?

Are there any patterns or trends in the shipping dates based on the customer segments?

# data[].attributes.body
Which region has the highest overall profit, and how is it distributed across product categories?

How does the discount rate affect the profit margin for different product categories?

Which category contributes the most and least to total sales?

Which state has the highest sales and how do the shipping modes impact those sales?

Which segment has the highest sales and profit?

# data[].attributes.body
1. What types of data fields are included in the sample data file?

2. What is the time range of the data?

3. Where does the data come from?

4. What are the maximum/minimum values ​​in the data file?

5. Does the data have missing values? If so, what is the proportion of missing values?

# data[].attributes.body
1. What regions have the highest amount of sales

2. How does GDP growth affect sales in different countries

3. How do exchange rates impact international sales

4. What regions have the lowest amount of sales

5. What are the average amount of sales that a country or region has in a day

# data[].attributes.body
2.How does the amount of quantity affect the profit?

1.Which items have the largest and the smallest impact on sales?

3.which region reports the lowest revenue?

4.what is the most common shipping class requested?

5.in which date, did they receive the highest amount of profit from orders?

# data[].attributes.body
What state/city has the most sales?

And what is the wanted product.

# data[].attributes.body
1) how does the different cities affect the profit

2) how does the ship date affect the recurrence of customers

3) how does the ship mode affect the ship date in comparison in relation to the order date

4) how does the postal code affect the ship date

5) how does the sales affect the profit

# data[].attributes.body
1- What are the top 5 states sells the most office supplies?

2- Which category in general provides the highest discounts?

3- South or West? Who makes the most profit?

4- Is the discount higher or lower if you buy office supplies before or after 1/1/2015?

5- If the ship mode is standard does that mean the sales are higher?

# data[].attributes.headline
Mohammad Almheiri and Mohammad Alawadhi 

# data[].attributes.subject
Mohammad Almheiri and Mohammad Alawadhi 

# data[].attributes.body
1. Which state has the most profit?

2. Which state offers the most discounts?

3. Which category has the most sales?

4. Which product has the most sales?

5. Which city has the most profit?

# data[].attributes.body
1. What is the most common category?

2. What is the max quantity of product ship can hold?

3. Which region receives the most?

4. The highest discount that customer received?

5. In what year were the highest sales?

# data[].attributes.body
1. What is the total sales revenue generated from each product category ?

2. Which costumer ( Consumer vs. Corporate ) has the highest average profit per order

3. Which state has the most customers ?

4. How many orders from the south were in the furniture category ?

5. What is the most preferred used shipping way ?

# data[].attributes.body
1)what is the average sales in chairs?

2)What is the average discount offered in the state of California?

3)What is the average profits earned by the west region of Washington?

4) which sub-category got the most sales?

5) What is the average quantity of appliances sold in each sale in California?

# data[].attributes.body
1. What are the total sales for the South region?

2. Which category has the highest total sales across all regions?

3. How many transactions in the dataset resulted in a negative profit?

4. What is the average discount provided across all transactions?

5.What is the total profit for the "Furniture" category in the West region?

6.Which sub-category in "Office Supplies" has the highest number of items sold?

7.What is the average profit per unit in the "Technology" category?

8.Compare the total sales between the East and Central regions— which region has higher sales?

9.Which sub-category has the highest negative profit, and what is the total loss?

10.How does the discount percentage affect the profit in the "Tables" sub-category?

11.Identify all transactions where the profit is greater than $50 but the discount is 0.

12.List the transactions in the "Chairs" sub-category sorted by profit in descending order.

13.Find the transaction with the highest discount and state its impact on profit.

14.What is the most profitable sub-category in the East region?

15.Which region has the highest profit-to-sales ratio?

# data[].attributes.body
1. Which sub-category generates the most profit relative to its sales?

2. What are the total sales in each region (West, South, Central)?

3. How does profit vary across the Consumer, Corporate, and Home Office segments?

4. Which cities generate the highest profit?

5. Which products or sub-categories result in negative profit, and in which regions or cities?

6. How do states compare in terms of total sales and profit?

7. Which sub-category has the highest quantity sold?

# data[].attributes.body
1. Which customer ordered the most items/generated the most revenue?

2. Which sub category of items had the highest number of sales?

3. Which ship mode was the most common?

4. Which state had the lowest sales?

5. Which segment of customers placed the most orders?

# data[].attributes.body
1. How many orders were made in total?

2. Which order date had the most orders?

3. What is the earliest shipping date in the data?

4. Do most orders come from one city or many different cities?

5. Which ship mode is used the least?

# data[].attributes.body
Kevin

1. Which product category generated the highest total sales overall?

2. What is the average profit per order for customers in the East region?

3. Which sub-category has the highest number of orders?

4. Which shipping mode is used most frequently across all orders?

5. Is there a difference in average sales between Consumer and Corporate customer segments?

# data[].attributes.body
1. Which ship mode has the most orders

2. Which region has the most sales

3. Which region has the lowest sales

4. Which category gets the highest sales

5. Which product contributes most to the sales

# data[].attributes.body
1 : which ship mode has the most orders

2 : which region has the most sales

3: which region has the lowest sales

4: which category gets the highest sales

5: which product contributes most to the sales

# data[].attributes.body
Which month in which year had the most customer sales and why?

What are the most profitable years out of the chosen ones?

What trends, profits, or benefits were given over time?

# data[].attributes.headline
5 questions on sample data from Saeed Saeed 

# data[].attributes.subject
5 questions on sample data from Saeed Saeed 

# data[].attributes.body
1) Which category has the highest total value in the dataset?

2) How does the data change over time?

3) What is the percentage distribution of the data across different categories?

4) Is there a noticeable difference between two groups in the data?

5) Which value or category appears most frequently?