This is a dataset downloaded off excelbianalytics.com created off of random VBA logic. I recently performed an extensive exploratory data analysis on it and I included new columns to it, namely: Unit margin, Order year, Order month, Order weekday and Order_Ship_Days which I think can help with analysis on the data. I shared it because I thought it was a great dataset to practice analytical processes on for newbies like myself.

The link for the Excel project to download can be found on GitHub here. It includes the raw data, Pivot Tables, and an interactive dashboard with Pivot Charts and Slicers. The project also includes business questions and the formulas I used to answer. The image below is included for ease. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12904052%2F61e460b5f6a1fa73cfaaa33aa8107bd5%2FBusinessQuestions.png?generation=1686190703261971&alt=media" alt=""> The link for the Tableau adjusted dashboard can be found here.

A screenshot of the interactive Excel dashboard is also included below for ease. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12904052%2Fe581f1fce8afc732f7823904da9e4cce%2FScooter%20Dashboard%20Image.png?generation=1686190815608343&alt=media" alt="">

This dataset contains video game sales data prepared for an Excel data analysis and dashboard project.

It includes detailed information on:

Game titles

Platforms

Genres

Publishers

Regional and global sales

The dataset was cleaned, structured, and analyzed in Microsoft Excel to explore patterns in the global video game market. It can be used to:

Practice data cleaning and pivot tables

Build interactive dashboards

Perform sales comparisons across regions and genres

Develop business insights from entertainment data

🧩 File Information

Format: .xlsx (Excel Workbook)

Columns: Name, Platform, Year, Genre, Publisher, NA_Sales, EU_Sales, JP_Sales, Other_Sales, Global_Sales

💡 Use Cases

Excel dashboard and chart creation

Data visualization and storytelling

Business and market analysis practice

Portfolio or learning projects

👤 Prepared by

Adewale Lateef W — for data analysis and Excel dashboard learning purposes.

About Datasets:

Domain : Sales Project: Coca Cola Sales Analysis Datasets: Power BI Dataset vF Dataset Type: Excel Data Dataset Size: 52k+ records

KPI's: 1. Analyze Profit Margins per Brand 2. Sales by Region 3. Price per unit 4. Operating Profit 5. Additional Analysis

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains Power Query, Q&A visual, Key influencers visual, map chart, matrix, dynamic timeline, dashboard, formatting, text box.

This is the Car sales data set which include information about different cars . This data set is being taken from the Analytixlabs for the purpose of prediction In this we have to see two things

First we have see which feature has more impact on car sales and carry out result of this

Secondly we have to train the classifier and to predict car sales and check the accuracy of the prediction.

The Superstore Sales Data dataset, available in an Excel format as "Superstore.xlsx," is a comprehensive collection of sales and customer-related information from a retail superstore. This dataset comprises* three distinct tables*, each providing specific insights into the store's operations and customer interactions.

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...

🛍️ Product Sales Dataset (2023–2024)

📌 Overview

This dataset contains 200,000 synthetic sales records simulating real-world product transactions across different U.S. regions. It is designed for data analysis, business intelligence, and machine learning projects, especially in the areas of sales forecasting, customer segmentation, profitability analysis, and regional trend evaluation.

The dataset provides detailed transactional data including customer names, product categories, pricing, and revenue details, making it highly versatile for both beginners and advanced analysts.

📂 Dataset Structure

• Rows: 200,000
• Columns: 14

Features

1. Order_ID – Unique identifier for each order
2. Order_Date – Date of transaction
3. Customer_Name – Name of the customer
4. City – City of the customer
5. State – State of the customer
6. Region – Region (East, West, South, Centre)
7. Country – Country (United States)
8. Category – Broad product category (e.g., Accessories, Clothing & Apparel)
9. Sub_Category – Subdivision of category (e.g., Sportswear, Bags)
10. Product_Name – Product description
11. Quantity – Units purchased
12. Unit_Price – Price per unit (USD)
13. Revenue – Total sales amount (Quantity × Unit Price)
14. Profit – Net profit earned from the transaction

🎯 Potential Use Cases

• Sales Analysis: Track revenue, profit, and performance by product, category, or region.
• Customer Analytics: Identify top customers, purchasing frequency, and loyalty patterns.
• Profitability Insights: Compare profit margins across categories and sub-categories.
• Time-Series Analysis: Study seasonal demand and forecast future sales.
• Visualization Projects: Build dashboards in Power BI, Tableau, or Excel.
• Machine Learning: Train models for demand prediction, price optimization, or segmentation.

📊 Example Insights

• Which region generates the highest revenue?
• What are the top 10 most profitable products?
• Are some product categories more popular in certain regions?
• Which customers contribute the most to total revenue?

🏷️ Tags

business · sales · profitability · forecasting · customer analysis · retail

📜 License

This dataset is synthetic and created for educational and analytical purposes. You are free to use, modify, and share it under the CC BY 4.0 License.

🙌 Acknowledgments

This dataset was generated to provide a realistic foundation for learning and practicing Data Analytics, Power BI, Tableau, Python, and Excel projects.

Tool: Microsoft Excel

Dataset: Coffee Sales

Process: 1. Data Cleaning: • Remove duplicates and blanks. • Standardize date and currency formats.

1. Data Manipulation: • Sorting and filtering function to work
   with interest subsets of data. • Use XLOOKUP, INDEX-MATCH and IF
   formula for efficient data manipulation, such as retrieving, matching and organising information in spreadsheets

2. Data Analysis: • Create Pivot Tables and Pivot Charts with the formatting to visualize trends.

3. Dashboard Development: • Insert Slicers with the formatting for easy filtering and dynamic updates.

Highlights: This project aims to understand coffee sales trends by country, roast type, and year, which could help identify marketing opportunities and customer segments.

About Datasets:

Domain : Sales Project: McDonalds Sales Analysis Project Dataset: START-Dashboard Dataset Type: Excel Data Dataset Size: 100 records

KPI's: 1. Customer Satisfaction 2. Sales by Country 2022 3. 2021-2022 Sales Trend 4. Sales 5. Profit 6. Customers

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains dashboard, hyperlink, shapes, icons, map, radar chart, line chart, doughnut chart, KPIs, formatting.

his dataset contains transactional grocery data from BlinkIT, a grocery delivery platform. It includes product names, categories, prices, units sold, and potentially order or date-based features (depending on the columns in the file

Dataset

This dataset was created by Andrés Armando Sánchez Martín

Released under Community Data License Agreement - Sharing - Version 1.0

Contents

This dataset simulates sales transactions for mobile phones and laptops, including product specifications, customer details, and sales information. It contains 50,000 rows of randomly generated data to help analyze product sales trends, customer purchasing behavior, and regional distribution of sales.

Dataset Overview

• Dataset Type: Structured tabular data
• Number of Rows: 50,000
• Number of Columns: 16

Purpose of the Dataset
This dataset can be used for:
✅ Sales Analysis – Understanding product demand and pricing trends.
✅ Customer Behavior Analysis– Identifying buying patterns across locations.
✅ Inventory Management – Monitoring inward and dispatched product movements.
✅ Machine Learning & AI – Predicting sales trends, customer preferences, and stock management.

Key Features in the Dataset

1. Product Information

   • Product: Type of product (Mobile Phone / Laptop).
   • Brand: Various brands like Apple, Samsung, Dell, Lenovo, OnePlus, etc.
   • Product Code: Unique identifier for each product.
   • Product Specification: Brief description of the product features.

2. Sales & Pricing Details

   • Price: Cost of the product (randomly generated).
   • Inward Date: Date when the product was received in stock.
   • Dispatch Date: Date when the product was sold/dispatched.
   • Quantity Sold: Number of units sold per transaction.

3. Customer & Location Details

   • Customer Name: Randomly generated customer names.
   • Customer Location: City of the customer.
   • Region: Sales region (North, South, East, West, Central).

4. Technical Specifications -Core Specification (For Laptops): Includes processor models like i3, i5, i7, i9, Ryzen 3-9.
   -Processor Specification (For Mobiles): Includes processors like Snapdragon, Exynos, Apple A-Series, and MediaTek Dimensity.
   -RAM: Randomly assigned memory sizes (4GB to 32GB).
   -ROM: Storage capacity (64GB to 1TB).
   -SSD (For Laptops): Additional storage (256GB to 2TB), "N/A" for mobile phones.

Potential Use Cases: Business Intelligence Dashboards Market Trend Analysis Supply Chain Optimization
Customer Segmentation
Machine Learning Model Training (Sales Prediction, Price Optimization, etc.)

This dataset provides detailed insights into retail sales, featuring a range of factors that influence sales performance. It includes records on sales revenue, units sold, discount percentages, marketing spend, and the impact of seasonal trends and holidays.

Key Features:

• Sales Revenue (USD): Total revenue generated from sales.
• Units Sold: Quantity of items sold.
• Discount Percentage: The percentage discount applied to products.
• Marketing Spend (USD): Budget allocated to marketing efforts.
• Store ID: Identifier for the retail store.
• Product Category: The category to which the product belongs (e.g., Electronics, Clothing).
• Date: The date when the sale occurred.
• Store Location: Geographic location of the store.
• Day of the Week: Day when the sale took place.
• Holiday Effect: Indicator of whether the sale happened during a holiday period.

Use Cases:

• Predictive Modeling: Build models to forecast future sales based on historical data.
• Marketing Analysis: Evaluate the effectiveness of marketing spend and discount strategies.
• Seasonal Trend Analysis: Examine how different seasons and holidays impact sales.
• Revenue Optimization: Identify strategies to optimize pricing and marketing for increased revenue.

Notes:

This dataset is synthetic and generated for analysis purposes. It reflects typical retail sales patterns and is designed to support a wide range of data science and business analytics projects.

Advertisement Sales Dataset

The Advertisement Sales dataset is a collection of data points used to analyze the impact of advertising on sales. This dataset consists of 200 entries, each representing a unique observation with data on various types of media advertising and corresponding sales figures.

Key Features: ID: A unique identifier for each observation. TV: The amount of money spent on TV advertising (in thousands of dollars). Radio: The amount of money spent on Radio advertising (in thousands of dollars). Newspaper: The amount of money spent on Newspaper advertising (in thousands of dollars). Sales: The sales figures for the product (in thousands of units).

Summary Statistics: TV advertising: Ranges from $0.7k to $296.4k, with an average spend of $147.03k. Radio advertising: Ranges from $0k to $49.6k, with an average spend of $23.29k. Newspaper advertising: Ranges from $0.3k to $114k, with an average spend of $30.55k. Sales: Ranges from 1.6k to 27k units, with an average of 14.04k units.

Use Cases: Advertising Strategy: Businesses can use this dataset to understand the effectiveness of different advertising channels (TV, Radio, Newspaper) on sales performance. Predictive Modeling: Analysts can build predictive models to forecast sales based on advertising spend across different media.

ROI Analysis: Marketers can calculate the return on investment (ROI) for each advertising channel to optimize their budgets. Correlation Studies: Researchers can study the correlation between advertising spend and sales to derive insights on consumer behavior.

Potential Analyses: Regression Analysis: Determine how changes in advertising budgets influence sales. Comparative Analysis: Compare the effectiveness of different advertising mediums. Trend Analysis: Identify trends in advertising spending and sales performance over time.

This dataset provides a robust foundation for exploring the relationships between advertising expenditures and sales outcomes, enabling data-driven decision-making for marketing strategies. ​

This dataset contains a list of sales and movement data by item and department appended monthly.

It is rich in information that can be leveraged for various data science applications. For instance, analyzing this dataset can offer insights into consumer behavior, such as preferences for specific types of beverages (e.g., wine, beer) during different times of the year. Furthermore, the dataset can be used to identify trends in sales and transfers, highlighting seasonal effects or the impact of certain suppliers on the market.

One could start with exploratory data analysis (EDA) to understand the basic distribution of sales and transfers across different item types and suppliers. Time series analysis can provide insights into seasonal trends and sales forecasts. Cluster analysis might reveal groups of suppliers or items with similar sales patterns, which can be useful for targeted marketing and inventory management.

The sales data for the first two products (P1 and P2) are weekly and data was collected until November 10, 2019. Products P3 and P4 are daily and might be related. For product P4, the company has provided potential explanatory variables X1 (price) and X2 (weather forecast of temperature in °C) that may be helpful for forecasting these two products. The sales data for products P3 and P4 was collected until November 24, 2019. Data for product P5 is weekly and was collected until August 30, 2019.

Visualization - https://public.tableau.com/views/ProductSales_16457072047730/Dashboard1?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link

https://raw.githubusercontent.com/Masterx-AI/Project_Retail_Analysis_with_Walmart/main/Wallmart1.jpg" alt="">

Description:

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Acknowledgements

The dataset is taken from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

This dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of vgchartz.com.

Fields include

• Rank - Ranking of overall sales

• Name - The games name

• Platform - Platform of the games release (i.e. PC,PS4, etc.)

• Year - Year of the game's release

• Genre - Genre of the game

• Publisher - Publisher of the game

• NA_Sales - Sales in North America (in millions)

• EU_Sales - Sales in Europe (in millions)

• JP_Sales - Sales in Japan (in millions)

• Other_Sales - Sales in the rest of the world (in millions)

• Global_Sales - Total worldwide sales.

The script to scrape the data is available at https://github.com/GregorUT/vgchartzScrape. It is based on BeautifulSoup using Python. There are 16,598 records. 2 records were dropped due to incomplete information.

In today's data-driven world, extracting meaningful insights from vast amounts of information is crucial for informed decision-making. This presentation tackles the challenge of creating presentable data visualizations based on employee type and region of sales.

Leveraging the power of PivotTables in Microsoft Excel, we will delve into a comprehensive approach to transforming raw sales data into compelling visual representations. By mastering PivotTable techniques, we will gain insights into employee sales trends, identify top performers, and uncover regional sales patterns.