Description: This dataset contains comprehensive information on inventory, sales, and customer interactions for a clothing company. The dataset is designed to provide insights into various aspects of the business, including product performance, sales trends, and customer preferences.

Content Overview:

Product Information:

Product ID: Unique identifier for each clothing item. Product Name: Descriptive name of the clothing item. Category: Type of clothing (e.g., shirts, pants, dresses, accessories). Subcategory: More specific classification (e.g., formal shirts, casual pants). Brand: Brand name associated with the product. Size: Available sizes (e.g., S, M, L, XL). Color: Available colors for the item. Material: Fabric or material used in the product. Price: Retail price of the clothing item. Stock Quantity: Current inventory count. Sales Transactions:

Transaction ID: Unique identifier for each sale. Date: Date of the transaction. Time: Time of the transaction. Product ID: Identifier for the purchased product. Quantity Sold: Number of units sold in the transaction. Total Sale Amount: Total revenue from the transaction. Discount Applied: Discount percentage or amount applied during the sale. Payment Method: Payment method used (e.g., credit card, cash, online). Customer Information:

Customer ID: Unique identifier for each customer. Name: Full name of the customer. Email: Email address of the customer. Phone Number: Contact phone number. Address: Shipping address for deliveries. Loyalty Status: Status of customer loyalty program (e.g., regular, VIP). Store Information:

Store ID: Unique identifier for each store location. Store Name: Name of the store. Location: Geographic location or address of the store. Manager Name: Name of the store manager. Seasonal and Promotional Data:

Season: Season during which the product is marketed (e.g., Spring, Summer). Promotion ID: Identifier for promotional events or discounts. Promotion Details: Description of the promotion or sale event. Purpose: The dataset is intended for analyzing sales performance, inventory management, customer behavior, and overall business operations. It supports various analytical tasks such as trend analysis, forecasting, and performance evaluation.

Usage: Researchers, analysts, and business professionals can use this dataset to gain insights into

Context

A 11.11 sale was going on Daraz within all categories. This dataset contains some product data from all categories such as health & beauty, men's & boys' fashion, groceries and others. Each product data carries information like product title, original price, discount, seller name and some more .

• The scraper program for data collection can be found at Daraz 11.11 Top Selling Product Data Analysis git repo.
• The Tableau visualization is available at 11.11 sale data analysis.

Dataset Containings

File Name: [All the .csv files within categorical_data folder]

• Category: Category of the product.
• SubCategory: Link to the relatable sub-category page.
• Title: The given name of the product given by seller.
• Original Price: Price before 11.11 sale.
• Discount Price: Price running on 11.11 sale.
• Discount: The discount(%) offered by the seller.
• Seller Name: The shop name selling the product.
• Number of Ratings: The total number of ratings given for the product.
• Positive Seller Ratings: Seller’s positive ratings percentage.
• Ship On Time: Seller’s on time shipping percentage.
• Chat Response Rate: Seller’s message reply percentage.
• Delivery Type: Whether the product is a “Free Delivery” product or “Standard Delivery” one?
• Flagship Store: Whether the shop is a flagship store or not? Yes/ No
• Cash On Delivery: Whether cash on delivery option is available or not?

File Name: subategories.csv

• Category: Category Name
• SubCategory Name: The subcategory name.
• SubCategory Link: Url of the subccategory page

File Name: Top_Selling_Product_Data.csv

• Category: Category of the product.
• SubCategory: Subcategory of the product within the category.
• Title: The given name of the product given by seller.
• Original Price: Price before 11.11 sale.
• Discount Price: Price running on 11.11 sale.
• Discount: The discount(%) offered by the seller.
• Seller Name: The shop name selling the product.
• Number of Ratings: The total number of ratings given for the product.
• Positive Seller Ratings: Seller’s positive ratings percentage.
• Ship On Time: Seller’s on time shipping percentage.
• Chat Response Rate: Seller’s message reply percentage.
• Delivery Type: Whether the product is a “Free Delivery” product or “Standard Delivery” one?
• Flagship Store: Whether the shop is a flagship store or not? Yes/ No
• No. of products to be sold: Total products to be sold in discount price to reach a break-even point equal to the sell of 50 products in original price. (Hypothetical Situation)
• Sell percentage to increase: Sell percentage to increase than the normal sale to reach the break-even point. (Hypothetical Situation)

Acknowledgements

The website daraz was used to scrape the dataset. If you use the data research purpose, don't forget add a citation.

Inspiration

This dataset can be used for traditional machine learning based project and also natural language processing workings.

📝 Dataset Description – Adidas Synthetic Sales Dataset

This dataset represents synthetic sales and customer transaction data for Adidas products across multiple regions and store types. It contains 1,200 rows of transaction-level information designed for analytics, visualization, machine learning, and business intelligence purposes.

📦 Dataset Overview

• Time Period: January 2023 – September 2025
• Number of Records: 1,200
• Granularity: Each row represents a unique order transaction.
• Purpose: To simulate Adidas sales operations for use in Power BI dashboards, SQL queries, Python analysis, and ML models.

📊 Columns and Descriptions

🔍 Possible Use Cases

• Power BI Dashboards – Build interactive reports on sales trends, customer demographics, and regional performance.
• SQL Practice – Write queries for aggregations, filtering, joins, and analytics.
• Machine Learning – Train models for demand forecasting, customer segmentation, or profitability prediction.
• Data Visualization – Create charts on product performance, seasonal demand, and discount impact.

⚠️ Note: This is a synthetic dataset generated for educational and analytical purposes. It does not represent real Adidas company data.

The share of cash at points-of-sale was estimated to be around ** percent, although this is based on a limited amount of information. No data exists on the actual use of cash money, outside of survey diaries. To that end, the numbers shown here are estimations based on the available data regarding cash withdrawals and payment transactions, and assume that consumers who withdraw cash money are likely to spend this in a physical store. As not all payment data is readily available, like the use of OTC or over-the-counter cash withdrawals, note this can still mean that the cash share numbers provided here are not necessarily accurate. In the case of the United States, figures are less publicly available than for, for example, European countries, as especially the ATM cash withdrawals is not measured often. One of the reasons the figures provided here look relatively old is because some of its measurements stem a three-year annual survey. Instead, the country does focus a lot more on data covering "noncash" payments.

Data Set Description This dataset simulates a retail environment with a million rows and 100+ columns, covering customer information, transactional data, product details, promotional information, and customer behavior metrics. It includes data for predicting total sales (regression) and customer churn (classification).

Detailed Column Descriptions Customer Information:

customer_id: Unique identifier for each customer. age: Age of the customer. gender: Gender of the customer (e.g., Male, Female, Other). income_bracket: Income bracket of the customer (e.g., Low, Medium, High). loyalty_program: Whether the customer is part of a loyalty program (Yes/No). membership_years: Number of years the customer has been a member. churned: Whether the customer has churned (Yes/No) - Target for classification. marital_status: Marital status of the customer. number_of_children: Number of children the customer has. education_level: Education level of the customer (e.g., High School, Bachelor's, Master's). occupation: Occupation of the customer. Transactional Data:

transaction_id: Unique identifier for each transaction. transaction_date: Date of the transaction. product_id: Unique identifier for each product. product_category: Category of the product (e.g., Electronics, Clothing, Groceries). quantity: Quantity of the product purchased. unit_price: Price per unit of the product. discount_applied: Discount applied on the transaction. payment_method: Payment method used (e.g., Credit Card, Debit Card, Cash). store_location: Location of the store where the purchase was made. Customer Behavior Metrics:

avg_purchase_value: Average value of purchases made by the customer. purchase_frequency: Frequency of purchases (e.g., Daily, Weekly, Monthly, Yearly). last_purchase_date: Date of the last purchase made by the customer. avg_discount_used: Average discount percentage used by the customer. preferred_store: Store location most frequently visited by the customer. online_purchases: Number of online purchases made by the customer. in_store_purchases: Number of in-store purchases made by the customer. avg_items_per_transaction: Average number of items per transaction. avg_transaction_value: Average value per transaction. total_returned_items: Total number of items returned by the customer. total_returned_value: Total value of returned items. Sales Data:

total_sales: Total sales amount for each customer over the last year - Target for regression. total_transactions: Total number of transactions made by each customer. total_items_purchased: Total number of items purchased by each customer. total_discounts_received: Total discounts received by each customer. avg_spent_per_category: Average amount spent per product category. max_single_purchase_value: Maximum value of a single purchase. min_single_purchase_value: Minimum value of a single purchase. Product Information:

product_name: Name of the product. product_brand: Brand of the product. product_rating: Customer rating of the product. product_review_count: Number of reviews for the product. product_stock: Stock availability of the product. product_return_rate: Rate at which the product is returned. product_size: Size of the product (if applicable). product_weight: Weight of the product (if applicable). product_color: Color of the product (if applicable). product_material: Material of the product (if applicable). product_manufacture_date: Manufacture date of the product. product_expiry_date: Expiry date of the product (if applicable). product_shelf_life: Shelf life of the product (if applicable). Promotional Data:

promotion_id: Unique identifier for each promotion. promotion_type: Type of promotion (e.g., Buy One Get One Free, 20% Off). promotion_start_date: Start date of the promotion. promotion_end_date: End date of the promotion. promotion_effectiveness: Effectiveness of the promotion (e.g., High, Medium, Low). promotion_channel: Channel through which the promotion was advertised (e.g., Online, In-store, Social Media). promotion_target_audience: Target audience for the promotion (e.g., New Customers, Returning Customers). Geographical Data:

customer_zip_code: Zip code of the customer's residence. customer_city: City of the customer's residence. customer_state: State of the customer's residence. store_zip_code: Zip code of the store. store_city: City where the store is located. store_state: State where the store is located. distance_to_store: Distance from the customer's residence to the store. Seasonal and Temporal Data:

holiday_season: Whether the transaction occurred during a holiday season (Yes/No). season: Season of the year (e.g., Winter, Spring, Summer, Fall). weekend: Whether the transaction occurred on a weekend (Yes/No). Customer Interaction Data:

customer_support_calls: Number of calls made to customer support. email_subscription...

According to Cognitive Market Research, the global Cash Register market size is USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of 9.00% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD XX million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.0% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.4% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.7% from 2024 to 2031.
occupy a significant portion of the market. One of the main engines of economies in both developed and developing countries, the ever-expanding hospitality sector has profited from the introduction of information technology (IT).

Market Dynamics of Cash Register Market

Key Drivers of Cash Register Market

Rising use of ECRs by SMEs' to Boost Market Growth:

ECR suppliers will benefit from the growing number of SMEs utilizing ECRs throughout the projection period as it will enable them to grow their customer base. The adoption of ECRs by end-user sectors has increased due to factors including affordability and convenience of usage.

Adoption of Cloud-Based Solutions to Boost Market Growth:

Retailers' need for cloud-based SaaS platforms will grow during the course of the anticipated timeframe. With the help of these technologies, shops will be able to store vast amounts of data that authorised units will be able to access remotely. The anticipated outcome of this is an improvement in connectivity and a decrease in data transfer latency between acquirers and merchants during checkout terminal financial transactions.

Key Restraint Factors Of Cash Register Market

Worldwide Cash Register Market will Face Significant Challenges from the Expanding E-Commerce Sector:

One of the main obstacles to the adoption of ECRs to facilitate remote payments and bills will be the increase in worldwide e-commerce sales. Among the biggest e-commerce markets, APAC is anticipated to have growth in sales throughout the projection period. E-commerce platforms facilitate quick and secure transactions using Internet banking and are user-friendly.

Key Trends of Cash Register Market

Adoption of Cloud-Enabled and Hybrid Cash Register Systems: Cloud-based cash registers offer immediate data access, scalability, and the ability to manage remotely. Retailers are increasingly favoring hybrid systems that merge cash handling with digital payment options.

Integration with Customer Loyalty and CRM Programs: Connecting cash registers to loyalty programs and customer relationship management systems improves personalized marketing efforts. This integration assists businesses in increasing repeat sales and enhancing customer retention.

Impact of Covid-19 on the Cash Register Market

The COVID-19 pandemic outbreak in 2020 led to brief lockdowns and social distancing policies in the area. Because of this, the majority of individuals were forced to purchase online while confined to their houses. Electronic cash registers are replaced by digital transaction systems and digital bills while shopping online. Customers are now more likely to use emerging payment methods like mobile wallets and mobile card readers, which has decreased the need for electronic cash registers (ECRs) in the area. Nonetheless, the pandemic is anticipated to abate by the end of 2021 thanks to the availability of COVID-19 vaccinations, which would be advantageous for the local economy. Introduction of the Cash Register Market

A cash register is a device used to compute and record transactions. A drawer that is installed underneath a cash register is used to store currency. While most cash registers are now computerized, most of them were once mechanical. "Till" is equivalent to "cash register" in the UK, Ireland, and...

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SHOP:TSX Shopify Inc.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Snapshot img

The online retail market share in the US is expected to increase to USD 460.13 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 11.64%.

The report extensively covers online retail market in the US segmentation by the following:

Product - Apparel, footwear, and accessories, consumer electronics and electricals, food and grocery, home furniture and furnishing, and others
Device - Smartphones and tablets and PCs

The US online retail market report offers information on several market vendors, including Amazon.com Inc., Apple Inc., Best Buy Co. Inc., Costco Wholesale Corp., eBay Inc., Kroger Co., Target Corp., The Home Depot Inc., Walmart Inc., and Wayfair Inc. among others.

This online retail market in the US research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches.

What will the Online Retail Market Size in the US be During the Forecast Period?

Download the Free Report Sample to Unlock the Online Retail Market Size in the US for the Forecast Period and Other Important Statistics

Online Retail Market in the US: Key Drivers, Trends, and Challenges

The growing seasonal and holiday sales is notably driving the online retail market growth in the US, although factors such as transportation and logistics may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the online retail industry in the US. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key US Online Retail Market Driver

The growing seasonal and holiday sales is one of the key drivers supporting the US online retail market growth. For instance, from November 1 to December 24, e-commerce sales in the US increased by 11% in 2021, when compared to a massive 47.2% growth in the holiday season of 2020. E-commerce sales made up 20.9 % of total retail sales in the holiday season of 2021, slightly higher than 20.6 percent in 2020. Thanksgiving, Black Friday, and Cyber Monday are the days that see a high amount of online shopping. Apparel, footwear and accessories, consumer electronics, computer hardware, and toys are the largest gaining product categories during the holiday season. Consumers in the US spent $204.5 billion online in November and December 2021, up 8.6% over the same period in 2020. Such exciting sales and offers are driving the market growth.

Key US Online Retail Market Trend

Omni-channel retailing is one of the key US online retail market trends fueling the market growth. It is rapidly becoming the norm for many retailers in the US. It offers consumers the option to shop online and pick up the merchandise from the store nearest to their location on the same day. Retailers are observing a high web influence on their in-store sales. For instance, Best Buy is integrating its offline and online stores to boost revenues. As a part of its omnichannel strategy, the retailer is utilizing physical stores as distribution centers for online purchases. According to Best Buy, 40% of its online shoppers prefer picking up their purchases from physical stores. Best Buy also challenges online and discount retailers with its match-to-price strategy, claiming to offer gadgets at or below the price offered by competitors. Such strategies are expected to boost market growth during the forecast period.

Key US Online Retail Market Challenge

Transportation and logistics are some of the factors hindering the US online retail market growth. Product procurement or sourcing, shipment of ordered items, and delivery to customers are the three major processes where the intervention of transportation and logistics come into the picture. All these processes require a high investment of both time and money, which challenges the efficiency and effectiveness of retailers and their costing strategies. The higher cost incurred from transportation and logistics reduces the margin of retailers, and most of the time, retailers are unable to break even. Between rising fuel prices, driver shortages, as well as a governmental and societal push for increased digitization and sustainability, transport and logistics will continue to be under a lot of pressure. Such factors will negatively impact the market growth during the forecast period.

This online retail market in the US analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2022-2026.

Who are the Major Online Retail Market Vendors in the US?

The report analyzes the market’s competitive landscape and offers information on several market vendors, includi

This dataset is the continuation of the Sample Dataset "Supermarket sales". It is a version of the year 2020. It is an example dataset for the purpose of practicing data analysis.

You can also work independently without having the "Supermarket sales" dataset

Attribute information

Invoice id: Computer generated sales slip invoice identification number Branch: Branch of supercenter (3 branches are available identified by A, B and C). City: Location of supercenters Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card. Gender: Gender type of customer Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel Unit price: Price of each product in $ Quantity: Number of products purchased by customer Tax: 5% tax fee for customer buying Total: Total price including tax Date: Date of purchase (Record available from January 2019 to March 2019) Time: Purchase time (10am to 9pm) Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet) COGS: Cost of goods sold Gross margin percentage: Gross margin percentage Gross income: Gross income Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10)

This dataset provides a comprehensive collection of consumer behavior data that can be used for various market research and statistical analyses. It includes information on purchasing patterns, demographics, product preferences, customer satisfaction, and more, making it ideal for market segmentation, predictive modeling, and understanding customer decision-making processes.

The dataset is designed to help researchers, data scientists, and marketers gain insights into consumer purchasing behavior across a wide range of categories. By analyzing this dataset, users can identify key trends, segment customers, and make data-driven decisions to improve product offerings, marketing strategies, and customer engagement.

Key Features: Customer Demographics: Understand age, income, gender, and education level for better segmentation and targeted marketing. Purchase Behavior: Includes purchase amount, frequency, category, and channel preferences to assess spending patterns. Customer Loyalty: Features like brand loyalty, engagement with ads, and loyalty program membership provide insights into long-term customer retention. Product Feedback: Customer ratings and satisfaction levels allow for analysis of product quality and customer sentiment. Decision-Making: Time spent on product research, time to decision, and purchase intent reflect how customers make purchasing decisions. Influences on Purchase: Factors such as social media influence, discount sensitivity, and return rates are included to analyze how external factors affect purchasing behavior.

Columns Overview: Customer_ID: Unique identifier for each customer. Age: Customer's age (integer). Gender: Customer's gender (categorical: Male, Female, Non-binary, Other). Income_Level: Customer's income level (categorical: Low, Middle, High). Marital_Status: Customer's marital status (categorical: Single, Married, Divorced, Widowed). Education_Level: Highest level of education completed (categorical: High School, Bachelor's, Master's, Doctorate). Occupation: Customer's occupation (categorical: Various job titles). Location: Customer's location (city, region, or country). Purchase_Category: Category of purchased products (e.g., Electronics, Clothing, Groceries). Purchase_Amount: Amount spent during the purchase (decimal). Frequency_of_Purchase: Number of purchases made per month (integer). Purchase_Channel: The purchase method (categorical: Online, In-Store, Mixed). Brand_Loyalty: Loyalty to brands (1-5 scale). Product_Rating: Rating given by the customer to a purchased product (1-5 scale). Time_Spent_on_Product_Research: Time spent researching a product (integer, hours or minutes). Social_Media_Influence: Influence of social media on purchasing decision (categorical: High, Medium, Low, None). Discount_Sensitivity: Sensitivity to discounts (categorical: Very Sensitive, Somewhat Sensitive, Not Sensitive). Return_Rate: Percentage of products returned (decimal). Customer_Satisfaction: Overall satisfaction with the purchase (1-10 scale). Engagement_with_Ads: Engagement level with advertisements (categorical: High, Medium, Low, None). Device_Used_for_Shopping: Device used for shopping (categorical: Smartphone, Desktop, Tablet). Payment_Method: Method of payment used for the purchase (categorical: Credit Card, Debit Card, PayPal, Cash, Other). Time_of_Purchase: Timestamp of when the purchase was made (date/time). Discount_Used: Whether the customer used a discount (Boolean: True/False). Customer_Loyalty_Program_Member: Whether the customer is part of a loyalty program (Boolean: True/False). Purchase_Intent: The intent behind the purchase (categorical: Impulsive, Planned, Need-based, Wants-based). Shipping_Preference: Shipping preference (categorical: Standard, Express, No Preference). Payment_Frequency: Frequency of payment (categorical: One-time, Subscription, Installments). Time_to_Decision: Time taken from consideration to actual purchase (in days).

Use Cases: Market Segmentation: Segment customers based on demographics, preferences, and behavior. Predictive Analytics: Use data to predict customer spending habits, loyalty, and product preferences. Customer Profiling: Build detailed profiles of different consumer segments based on purchase behavior, social media influence, and decision-making patterns. Retail and E-commerce Insights: Analyze purchase channels, payment methods, and shipping preferences to optimize marketing and sales strategies.

Target Audience: Data scientists and analysts looking for consumer behavior data. Marketers interested in improving customer segmentation and targeting. Researchers are exploring factors influencing consumer decisions and preferences. Companies aiming to improve customer experience and increase sales through data-driven decisions.

This dataset is available in CSV format for easy integration into data analysis tools and platforms such as Python, R, and Excel.

Cash was a dominant payment method in Bulgaria, with over ** percent of all transactions being done with cash money in 2020. This according to estimations based on the available data regarding cash withdrawals and payment transactions. No data exists on the actual use of cash money, outside of survey diaries. Such a diary, covering consumers' use of cash money in shops, was held in Europe only once, in 2017. As not all payment data is readily available, like the use of OTC or over-the-counter cash withdrawals, note this can still mean that the cash share numbers provided here are not necessarily accurate.

This interactive sales dashboard is designed in Excel for B2C type of Businesses like Dmart, Walmart, Amazon, Shops & Supermarkets, etc. using Slicers, Pivot Tables & Pivot Chart.

Dashboard Overview

1. Sales dashboard ==> basically, it is designed for the B2C type of business. like Dmart, Walmart, Amazon, Shops & supermarkets, etc.
2. Slices ==> slices are used to drill down the data, on the basis of yearly, monthly, by sales type, and by mode of payment.
3. Total Sales/Total Profits ==> here is, the total sales, total profit, and profit percentage these all are combined into a monthly format and we can hide or unhide it to view it as individually or comparative.
4. Product Visual ==> the visual indicates product-wise sales for the selected period. Only 10 products are visualized at a glance, and you can scroll up & down to view other products in the list.
5. Daily Sales ==> It shows day-wise sales. (Area Chart)
6. Sales Type/Payment Mode ==> It shows sales percentage contribution based on the type of selling and mode of payment.
7. Top Product & Category ==> this is for the top-selling product and product category.
8. Category ==> the final one is the category-wise sales contribution.

Datasheets Overview

1. The dataset has the master data sheet or you can call it a catalog. It is added in the table form.
2. The first column is the product ID the list of items in this column is unique.
3. Then we have the product column instead of these two columns, we can manage with only one also but I kept it separate because sometimes product names can be the same, but some parameters will be different, like price, supplier, etc.
4. The next column is the category column, which is the product category. like cosmetics, foods, drinks, electronics, etc.
5. Then we have 4th column which is the unit of measure (UOM) you can update it also, based on the products you have.
6. And the last two columns are buying price and selling price, which means unit purchasing price and unit selling price.

Input Sheet

The first column is the date of Selling. The second column is the product ID. The third column is quantity. The fourth column is sales types, like direct selling, are purchased by a wholesaler or ordered online. The fifth column is a mode of payment, which is online or in cash. You can update these two as per requirements. The last one is a discount percentage. if you want to offer any discount, you can add it here.

Analysis Sheet: where all backend calculations are performed.

So, basically these are the four sheets mentioned above with different tasks.

However, a sales dashboard enables organizations to visualize their real-time sales data and boost productivity.

A dashboard is a very useful tool that brings together all the data in the forms of charts, graphs, statistics and many more visualizations which lead to data-driven and decision making.

Questions & Answers

1. What percentage of profit ratio of sales are displayed in the year 2021 and year 2022? ==> Total profit ratio of sales in the year 2021 is 19% with large sales of PRODUCT42, whereas profit ratio of sales for 2022 is 22% with large sales of PRODUCT30.
2. Which is the top product that have large number of sales in year 2021-2022? ==> The top product in the year 2021 is PRODUCT42 with the total sales of $12,798 whereas in the year 2022 the top product is PRODUCT30 with the total sales of $13,888.
3. In Area Chart which product is highly sold on 28th April 2022? ==> The large number of sales on 28th April 2022 is for PRODUCT14 with a 24% of profit ratio.
4. What is the sales type and payment mode present? ==> The sale type and payment modes show the sales percentage contribution based on the type of selling and mode of payment. Here, the sale types are Direct Sales with 52%, Online Sales with 33% and Wholesaler with 15%. Also, the payment modes are Online mode and Cash equally distributed with 50%.
5. In which month the direct sales are highest in the year 2022? ==> The highest direct sales can be easily identified which is designed by monthly format and it’s the November month where direct sales are highest with 28% as compared with other months.
6. Which payment mode is highly received in the year 2021 and year 2022? ==> The payments received in the year 2021 are the cash payments with 52% as compared with online transactions which are 48%. Also, the cash payment highly received is in the month of March, July and October with direct sales of 42%, Online with 45% and wholesaler with 13% with large sales of PRODUCT24. ==> The payments received in the year 2022 are the Online payments with 52% as compared with cash payments which are 48%. Also, the online payment highly received is in the month of Jan, Sept and December with direct sales of 45%, Online with 37% and whole...

Context

The growth of supermarkets in most populated cities are increasing and market competitions are also high. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this dataset.

Attribute information Invoice id: Computer generated sales slip invoice identification number Branch: Branch of supercenter (3 branches are available identified by A, B and C). City: Location of supercenters Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card. Gender: Gender type of customer Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel Unit price: Price of each product in $ Quantity: Number of products purchased by customer Tax: 5% tax fee for customer buying Total: Total price including tax Date: Date of purchase (Record available from January 2019 to March 2019) Time: Purchase time (10am to 9pm) Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet) COGS: Cost of goods sold Gross margin percentage: Gross margin percentage Gross income: Gross income Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10)

Acknowledgements Thanks to all who take time and energy to perform Kernels with this dataset and reviewers.

Purpose This dataset can be used for predictive data analytics purpose.

This dataset contains information about supermarket sales. Below are its key details:

General Information: Total Rows: 1,000 Total Columns: 17 Memory Usage: Approximately 132.9 KB

Column Descriptions: - Invoice ID: Unique identifier for each transaction. - Branch: Store branch where the transaction occurred (A, B, or C). - City: City where the store is located (Yangon, Naypyitaw, Mandalay). - Customer type: Indicates whether the customer is a Member or Normal. - Gender: Gender of the customer (Male or Female). - Product line: Category of the purchased product (e.g., Health and Beauty, Fashion Accessories). - Unit price: Price per unit of the product (float). - Quantity: Number of items purchased (integer). - Tax 5%: Tax applied to the purchase (calculated at 5%). - Total: Total cost including tax. - Date: Date of the transaction (format: MM/DD/YYYY). - Time: Time of the transaction (HH:MM). - Payment: Payment method (Ewallet, Credit card, Cash). - cogs: Cost of goods sold (calculated as Total - Tax). - Gross margin percentage: Always 4.76% for all transactions. - Gross income: Gross profit earned from the transaction. - Rating: Customer satisfaction rating (scale: 4.0 to 10.0).

Business Context

Prediction of bankruptcy is a phenomenon of increasing interest to firms who stand to lose money because of unpaid debts. Since computers can store huge data sets pertaining to bankruptcy, making accurate predictions from them beforehand is becoming important. The data were collected from the Taiwan Economic Journal for the years 1999 to 2009. Company bankruptcy was defined based on the business regulations of the Taiwan Stock Exchange. In this project you will use various classification algorithms on bankruptcy dataset to predict bankruptcies with satisfying accuracies long before the actual event.

Dataset Description

Updated column names and description to make the data easier to understand (Y = Output feature, X = Input features)

Bankrupt?: Class label 1 : Yes , 0: No ROA(C) before interest and depreciation before interest: Return On Total Assets(C) ROA(A) before interest and % after tax: Return On Total Assets(A) ROA(B) before interest and depreciation after tax: Return On Total Assets(B) Operating Gross Margin: Gross Profit/Net Sales Realized Sales Gross Margin: Realized Gross Profit/Net Sales Operating Profit Rate: Operating Income/Net Sales Pre-tax net Interest Rate: Pre-Tax Income/Net Sales After-tax net Interest Rate: Net Income/Net Sales Non-industry income and expenditure/revenue: Net Non-operating Income Ratio Continuous interest rate (after tax): Net Income-Exclude Disposal Gain or Loss/Net Sales Operating Expense Rate: Operating Expenses/Net Sales Research and development expense rate: (Research and Development Expenses)/Net Sales Cash flow rate: Cash Flow from Operating/Current Liabilities Interest-bearing debt interest rate: Interest-bearing Debt/Equity Tax rate (A): Effective Tax Rate Net Value Per Share (B): Book Value Per Share(B) Net Value Per Share (A): Book Value Per Share(A) Net Value Per Share (C): Book Value Per Share(C) Persistent EPS in the Last Four Seasons: EPS-Net Income Cash Flow Per Share Revenue Per Share (Yuan ¥): Sales Per Share Operating Profit Per Share (Yuan ¥): Operating Income Per Share Per Share Net profit before tax (Yuan ¥): Pretax Income Per Share Realized Sales Gross Profit Growth Rate Operating Profit Growth Rate: Operating Income Growth After-tax Net Profit Growth Rate: Net Income Growth Regular Net Profit Growth Rate: Continuing Operating Income after Tax Growth Continuous Net Profit Growth Rate: Net Income-Excluding Disposal Gain or Loss Growth Total Asset Growth Rate: Total Asset Growth Net Value Growth Rate: Total Equity Growth Total Asset Return Growth Rate Ratio: Return on Total Asset Growth Cash Reinvestment %: Cash Reinvestment Ratio Current Ratio Quick Ratio: Acid Test Interest Expense Ratio: Interest Expenses/Total Revenue Total debt/Total net worth: Total Liability/Equity Ratio Debt ratio %: Liability/Total Assets Net worth/Assets: Equity/Total Assets Long-term fund suitability ratio (A): (Long-term Liability+Equity)/Fixed Assets Borrowing dependency: Cost of Interest-bearing Debt Contingent liabilities/Net worth: Contingent Liability/Equity Operating profit/Paid-in capital: Operating Income/Capital Net profit before tax/Paid-in capital: Pretax Income/Capital Inventory and accounts receivable/Net value: (Inventory+Accounts Receivables)/Equity Total Asset Turnover Accounts Receivable Turnover Average Collection Days: Days Receivable Outstanding Inventory Turnover Rate (times) Fixed Assets Turnover Frequency Net Worth Turnover Rate (times): Equity Turnover Revenue per person: Sales Per Employee Operating profit per person: Operation Income Per Employee Allocation rate per person: Fixed Assets Per Employee Working Capital to Total Assets Quick Assets/Total Assets Current Assets/Total Assets Cash/Total Assets Quick Assets/Current Liability Cash/Current Liability Current Liability to Assets Operating Funds to Liability Inventory/Working Capital Inventory/Current Liability Current Liabilities/Liability Working Capital/Equity Current Liabilities/Equity Long-term Liability to Current Assets Retained Earnings to Total Assets Total income/Total expense Total expense/Assets Current Asset Turnover Rate: Current Assets to Sales Quick Asset Turnover Rate: Quick Assets to Sales Working capitcal Turnover Rate: Working Capital to Sales Cash Turnover Rate: Cash to Sales Cash Flow to Sales Fixed Assets to Assets Current Liability to Liability Current Liability to Equity Equity to Long-term Liability Cash Flow to Total Assets Cash Flow to Liability CFO to Assets Cash Flow to Equity Current Liability to Current Assets Liability-Assets Flag: 1 if Total Liability exceeds Total Assets, 0 otherwise Net Income to Total Assets Total assets to GNP price No-credit Interval Gross Profit to Sales Net Income to Stockholder's Equity Liability to Equity Degree of Financial Leverage (DFL) Interest Coverage Ratio (Interest expense to EBIT) Net Income Flag: 1 if Net I...