****Attribute information:****

Row ID: A unique identifier for each row in the table Order ID: The identifier for each sales order Order Date: The date the order was placed Ship Date: The date the order was shipped Delivery Duration: The amount of time it took to deliver the order Ship Mode: The shipping method used for the order Customer ID: The identifier for the customer who placed the order Customer Name: The name of the customer who placed the order Country: The customer's country City: The customer's city State: The customer's state Postal Code: The customer's postal code Region: The customer's region Product ID: The identifier for the product that was ordered Category: The category of the product that was ordered (e.g., furniture, office supplies, technology) Sub-Category - This attribute likely refers to a subcategory within a larger product category (e.g., Tables within Furniture). (Bookcases - Chairs - Labels - Tables - Storage - Furnishings - Art - Phones - Binders - Appliances - Paper - Others). Product Name - This attribute specifies the name of the product sold. (Bush Somerset Collection Bookcase - Hon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back - Self-Adhesive Address Labels for Typewriters by Universal - Bretford CP4500 Series Slim Rectangular Table - Others).

Sales - This attribute shows the total sales amount for each product. Values are listed in currency format Quantity - This attribute specifies the number of units sold for each product. Integer values. Discount - This attribute indicates the discount offered on the product. Discount Value - This attribute shows the total discount amount applied to the product. Profit - This attribute shows the profit earned on the sale of each product. COGS - This attribute likely refers to each product's Cost of Goods Sold. COGS = Sales - Profit

The turnover of the wholesale of office furniture in the United Kingdom increased by 81.7 million euros (+7.44 percent) since the previous year. Therefore, the turnover in the United Kingdom reached a peak in 2018 with 1.2 billion euros. For the purpose of Eurstat Dataset NACE Rev.2 Section K turnover comprises the totals invoiced by the observation unit during the reference period, which corresponds to market sales of goods or services supplied to third parties.

this graph was created in google sheets:

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Row ID: A unique identifier for each row in the table, ensuring that every entry is distinct.

Order ID: The identifier for each sales order, used to track individual transactions.

Order Date: The date on which the order was placed, providing a timeline for sales activities.

Ship Date: The date on which the order was shipped to the customer, crucial for calculating delivery times.

Delivery Duration: The amount of time it took to deliver the order, measured from the order date to the ship date.

Ship Mode: The shipping method used for the order, such as standard, express, or overnight shipping.

Customer ID: The identifier for the customer who placed the order, useful for tracking customer purchasing behavior.

Customer Name: The name of the customer who placed the order, aiding in personalized customer service.

Country: The customer's country, providing geographical insights into sales distribution.

City: The customer's city, helping to pinpoint regional sales trends.

State: The customer's state, further refining geographical sales analysis.

Postal Code: The customer's postal code, offering a detailed location for demographic studies.

Region: The customer's region, summarizing broader geographical areas for sales reporting.

Product ID: The identifier for the product that was ordered, essential for inventory management and sales tracking.

Category: The category of the product that was ordered, such as furniture, office supplies, or technology, useful for understanding sales by product type.

Sub-Category: This attribute likely refers to a subcategory within a larger product category, such as Tables within Furniture. Examples include:

Bookcases Chairs Labels Tables Storage Furnishings Art Phones Binders Appliances Paper Others Product Name: This attribute specifies the name of the product sold, such as:

Bush Somerset Collection Bookcase Hon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back Self-Adhesive Address Labels for Typewriters by Universal Bretford CP4500 Series Slim Rectangular Table Others Sales: This attribute shows the total sales amount for each product, with values listed in currency format.

Quantity: This attribute specifies the number of units sold for each product, represented by integer values.

Discount: This attribute indicates the discount percentage offered on the product.

Discount Value: This attribute shows the total discount amount applied to the product, expressed in currency format.

Profit: This attribute shows the profit earned on the sale of each product, highlighting the financial performance.

COGS (Cost of Goods Sold): This attribute likely refers to each product's cost of goods sold, calculated as Sales minus Profit, providing insight into the direct costs associated with the products sold.

The data is taken from Saudi SOUQ.com shopping website, specifically the office furniture category. Description of data features:

1) Item_title : the name of the item. 2) Item_price : the price after discount. 3) price_before_discount : the price after discount; any item with '0' fiqure mens that there is no discount on that particular item. 4) rate : is the rate that is done by the customers who used the same product.

Questions to be raised while investigating the data:

1) the probabilty of a customer buying a product that has good review with discounted price ? 2) In case of having products that don't reach customers' satisfaction; how can a business owner come up with a product that would increase the probability of a customer being sataisfaied throuh using the data privided.

The GlobalMart Supermarket Sales Dataset contains 5,000 transaction records representing sales operations across multiple international regions. It reflects the activities of a supermarket chain that operates in North America, South America, Europe, and Asia, offering a broad mix of products in categories such as electronics, furniture, and home appliances. Each record captures details of the sales process, including order transactions, customer segments, sales representatives, teams, product categories, and financial performance metrics. With fields covering revenue, sales targets, deal sizes, and sales pipeline stages, the dataset supports analysis of critical key performance indicators (KPIs) such as revenue growth, regional comparisons, target achievement, and conversion rates. The inclusion of region and country fields enables geographic mapping of sales trends at both macro (regional) and micro (country) levels. Similarly, sales team and representative fields allow for performance evaluation at an individual and team scale.

• Order_ID – Unique identifier for each order/transaction
• Order_Date – Date of the order (YYYY-MM-DD format)
• Region – Sales region (North, South, East, West)
• Country – Country within the region (e.g., USA, Canada, India, Brazil, UK, Germany)
• Sales_Rep – Name of the sales representative handling the transaction
• Team – Assigned sales team (Team A, Team B, Team C)
• Customer_ID – Unique customer identifier
• Customer_Segment – Customer type/segment (Retail, Corporate, SME)
• Product_Category – Broad category of the product sold (Electronics, Furniture, Appliances)
• Product_Name – Specific product name (e.g., Laptop Pro X, Office Chair, Refrigerator)
• Stage – Sales pipeline stage (Won, Lost, Opportunity)
• Units_Sold – Number of units sold (if won or opportunity)
• Revenue – Total revenue generated from the transaction
• Target – Sales target linked to the order or reporting period
• Deal_Size – Value of the sales deal (useful for pipeline analysis and averages)

Register of payments made as provided for in the Local Government Act 2001 to Councillors and the Register of Individual attendance records at Council Meetings for all Councillors. These registers include details of the following;Annual Representational Payment (Salary) which is subject to tax and statutory deductions.The Annual Allowance which is primarily in respect of expenses incurred through attendance at Council meetings. It is made up of three elements: travel to and from meetings, subsistence, postage and miscellaneous expenses incurred in their representational role. The amending regulations (S.I. No. 494 of 2017) provided for the introduction of a new vouched expenses allowance that elected members may choose to opt for in place of the existing fixed annual rate for miscellaneous expenses. There are attendance thresholds in place which determine the amount due to each Councillor. Payments in respect of travel and subsistence expenses incurred through attendance at conferences/seminars etc.Attendance Registers: This outlines details of Individual attendance records at Council Meetings for all Councillors.When examining these details the following should be taken into consideration;The Mayor /Deputy Mayor’s Allowance: The Local Government Act provides that a Local Authority can pay an allowance for reasonable expenses to its Mayor and Deputy Mayor for their term of office; these payments are subject to statutory deductions. Please note the term of office of Mayor and Deputy Mayor straddles two calendar years as they are elected at the annual meeting held at the end of June each year.The Strategic Policy Committee Chair Allowance is to cover all expenses relating to the position, including meetings of the Corporate Policy Group (High Level Strategic Group of the Council which meets a minimum of 12 times per year, compromising of the Mayor, Chief Executive and SPC Chairs), meetings with Local Authority Officials and /or meetings with external groups.

Dirty Retail Store Sales Dataset

Overview

The Dirty Retail Store Sales dataset contains 12,575 rows of synthetic data representing sales transactions from a retail store. The dataset includes eight product categories with 25 items per category, each having static prices. It is designed to simulate real-world sales data, including intentional "dirtiness" such as missing or inconsistent values. This dataset is suitable for practicing data cleaning, exploratory data analysis (EDA), and feature engineering.

File Information

• File Name: retail_store_sales.csv
• Number of Rows: 12,575
• Number of Columns: 11

Columns Description

Column Name	Description	Example Values
Transaction ID	A unique identifier for each transaction. Always present and unique.	TXN_1234567
Customer ID	A unique identifier for each customer. 25 unique customers.	CUST_01
Category	The category of the purchased item.	Food, Furniture
Item	The name of the purchased item. May contain missing values or None.	Item_1_FOOD, None
Price Per Unit	The static price of a single unit of the item. May contain missing or None values.	4.00, None
Quantity	The quantity of the item purchased. May contain missing or None values.	1, None
Total Spent	The total amount spent on the transaction. Calculated as Quantity * Price Per Unit.	8.00, None
Payment Method	The method of payment used. May contain missing or invalid values.	Cash, Credit Card
Location	The location where the transaction occurred. May contain missing or invalid values.	In-store, Online
Transaction Date	The date of the transaction. Always present and valid.	2023-01-15
Discount Applied	Indicates if a discount was applied to the transaction. May contain missing values.	True, False, None

Categories and Items

The dataset includes the following categories, each containing 25 items with corresponding codes, names, and static prices:

Electric Household Essentials

Item Code	Item Name	Price
Item_1_EHE	Blender	5.0
Item_2_EHE	Microwave	6.5
Item_3_EHE	Toaster	8.0
Item_4_EHE	Vacuum Cleaner	9.5
Item_5_EHE	Air Purifier	11.0
Item_6_EHE	Electric Kettle	12.5
Item_7_EHE	Rice Cooker	14.0
Item_8_EHE	Iron	15.5
Item_9_EHE	Ceiling Fan	17.0
Item_10_EHE	Table Fan	18.5
Item_11_EHE	Hair Dryer	20.0
Item_12_EHE	Heater	21.5
Item_13_EHE	Humidifier	23.0
Item_14_EHE	Dehumidifier	24.5
Item_15_EHE	Coffee Maker	26.0
Item_16_EHE	Portable AC	27.5
Item_17_EHE	Electric Stove	29.0
Item_18_EHE	Pressure Cooker	30.5
Item_19_EHE	Induction Cooktop	32.0
Item_20_EHE	Water Dispenser	33.5
Item_21_EHE	Hand Blender	35.0
Item_22_EHE	Mixer Grinder	36.5
Item_23_EHE	Sandwich Maker	38.0
Item_24_EHE	Air Fryer	39.5
Item_25_EHE	Juicer	41.0

Furniture

Item Code	Item Name	Price
Item_1_FUR	Office Chair	5.0
Item_2_FUR	Sofa	6.5
Item_3_FUR	Coffee Table	8.0
Item_4_FUR	Dining Table	9.5
Item_5_FUR	Bookshelf	11.0
Item_6_FUR	Bed F...

The given dataset appears to be a sales dataset containing information about different orders. Here is a description of the data:

1. Row ID: An identifier for each row in the dataset.
2. Order ID: Unique identifier for each order.
3. Order Date: The date when the order was placed.
4. Ship Date: The date when the order was shipped.
5. Ship Mode: The mode of shipping chosen for the order.
6. Customer ID: Unique identifier for each customer.
7. Customer Name: Name of the customer who placed the order.
8. Segment: The segment to which the customer belongs (e.g., consumer, corporate).
9. Country: The country where the order was placed (in this case, United States).
10. City: The city where the order was placed.
11. State: The state where the order was placed.
12. Postal Code: The postal code associated with the order's location.
13. Region: The region of the country where the order was placed.
14. Product ID: Unique identifier for each product.
15. Category: The category to which the product belongs (e.g., furniture, office supplies).
16. Sub-Category: The sub-category to which the product belongs (e.g., bookcases, chairs).
17. Product Name: The name of the product.
18. Cost: The cost of the product.
19. Price: The price at which the product was sold.
20. Profit: The profit made from the sale of the product.
21. Quantity: The quantity of the product ordered.
22. Sales: The total sales generated from the order (quantity multiplied by price).

The dataset provides detailed information about each order, including customer details, product details, sales information, and shipping information. It can be used to analyze various aspects of the sales data, such as profitability, customer segments, product categories, and regional sales performance.