🛍️ 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.

Open Food Facts Database

  What is 🍊 Open Food Facts?





  A food products database

Open Food Facts is a database of food products with ingredients, allergens, nutrition facts and all the tidbits of information we can find on product labels.

  Made by everyone

Open Food Facts is a non-profit association of volunteers. 25.000+ contributors like you have added 1.7 million + products from 150 countries using our Android or iPhone app or their camera to scan… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/product-database.

This dataset provides a detailed snapshot of product inventory, perfect for logistics optimization, e-commerce analysis, or supply chain research. It includes key details like product names, categories, prices, stock quantities, and more—sourced from a hypothetical global supplier database. I compiled this while working on a shipment logistics optimization project, and I hope it’s useful for others exploring similar challenges!

Key Features: - 14 columns covering product specs, pricing, stock, and tags. - Sample data includes diverse categories like Home Appliances. - Ideal for data cleaning practice, visualizations, or predictive modeling (e.g., stock depletion).

Potential Use Cases: - Optimize shipment logistics based on stock and expiration dates. - Analyze pricing trends across product categories. - Build recommendation systems using tags and ratings.

Notes: - Dates range from manufacturing to expiration (e.g., 2023-2026). - Some fields (e.g., Product Description) may need refinement—feel free to enhance it! - Suggestions for additional data or improvements are welcome.

Let me know how you use it.....I’d love to hear your feedback!

Dataset Card for "amazon-product-data-filter"

  Dataset Summary

The Amazon Product Dataset contains product listing data from the Amazon US website. It can be used for various NLP and classification tasks, such as text generation, product type classification, attribute extraction, image recognition and more. NOTICE: This is a sample of the full Amazon Product Dataset, which contains 1K examples. Follow the link to gain access to the full dataset.

  Languages… See the full description on the dataset page: https://huggingface.co/datasets/iarbel/amazon-product-data-sample.

Search - Conduct a search of the repository of raw public bulk data. It contains research datasets from the Office of the Chief Economist. The files are updated on a regular or ongoing basis. Use this endpoint if you are interested in searching across multiple patents or applications. For example, you want to return all Patent or Trademark products use productTitle and specify the products you are looking for Patent File Wrapper, for example. Product Data - Contains published, publicly available patent and trademark data in bulk form. Use this endpoint when you want data from a specific Bulk Dataset. You can test APIs right away in SwaggerUI. Download - Contains large bulk files of the Bulk Data Directory (BDD) available for download. Use this endpoint when you want to download bulk data sets.

The Product Catalog Data provides a comprehensive overview of products across various categories. This dataset includes detailed product titles, descriptions, barcodes, category-specific attributes, weight, measurements, and imagery. It's tailored for marketplaces, eCommerce sites, and data analysts who require in-depth product information to enhance user experience, SEO, and product categorization.

Popular Attributes:

✔ Detailed product information

✔ High-quality imagery

✔ Extensive attribute coverage

✔ Ideal for UX and SEO optimization

✔ Comprehensive product categorization

Key Information:

Rich dataset with 30+ attributes per product

Pricing: Flexible subscription models

Update Frequency: Daily updates

Coverage: Global and specific markets

Historical Data: 12 Months +

1.Introduction

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.

This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.

2. Citation

Please cite the following papers when using this dataset:

1. I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted

3. Dataset Modalities

The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.

3.1 Data Collection

The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.

The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.

Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.

It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.

The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).

File	Period	Number of Samples (days)
product 1 2020.xlsx	01/01/2020–31/12/2020	363
product 1 2021.xlsx	01/01/2021–31/12/2021	364
product 1 2022.xlsx	01/01/2022–31/12/2022	365
product 2 2020.xlsx	01/01/2020–31/12/2020	363
product 2 2021.xlsx	01/01/2021–31/12/2021	364
product 2 2022.xlsx	01/01/2022–31/12/2022	365
product 3 2020.xlsx	01/01/2020–31/12/2020	363
product 3 2021.xlsx	01/01/2021–31/12/2021	364
product 3 2022.xlsx	01/01/2022–31/12/2022	365
product 4 2020.xlsx	01/01/2020–31/12/2020	363
product 4 2021.xlsx	01/01/2021–31/12/2021	364
product 4 2022.xlsx	01/01/2022–31/12/2022	364
product 5 2020.xlsx	01/01/2020–31/12/2020	363
product 5 2021.xlsx	01/01/2021–31/12/2021	364
product 5 2022.xlsx	01/01/2022–31/12/2022	365
product 6 2020.xlsx	01/01/2020–31/12/2020	362
product 6 2021.xlsx	01/01/2021–31/12/2021	364
product 6 2022.xlsx	01/01/2022–31/12/2022	365
product 7 2020.xlsx	01/01/2020–31/12/2020	362
product 7 2021.xlsx	01/01/2021–31/12/2021	364
product 7 2022.xlsx	01/01/2022–31/12/2022	365

3.2 Dataset Overview

The following table enumerates and explains the features included across all of the included files.

Feature	Description	Unit
Day	day of the month	-
Month	Month	-
Year	Year	-
daily_unit_sales	Daily sales - the amount of products, measured in units, that during that specific day were sold	units
previous_year_daily_unit_sales	Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year	units
percentage_difference_daily_unit_sales	The percentage difference between the two above values	%
daily_unit_sales_kg	The amount of products, measured in kilograms, that during that specific day were sold	kg
previous_year_daily_unit_sales_kg	Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year	kg
percentage_difference_daily_unit_sales_kg	The percentage difference between the two above values	kg
daily_unit_returns_kg	The percentage of the products that were shipped to selling points and were returned	%
previous_year_daily_unit_returns_kg	The percentage of the products that were shipped to

Company Datasets for valuable business insights!

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We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

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These datasets contain attributes about products sold on ModCloth and Amazon which may be sources of bias in recommendations (in particular, attributes about how the products are marketed). Data also includes user/item interactions for recommendation.

Metadata includes

• ratings

• product images

• user identities

• item sizes, user genders

In this project, we work on repairing three datasets:

• Trials design: This dataset was obtained from the European Union Drug Regulating Authorities Clinical Trials Database (EudraCT) register and the ground truth was created from external registries. In the dataset, multiple countries, identified by the attribute country_protocol_code, conduct the same clinical trials which is identified by eudract_number. Each clinical trial has a title that can help find informative details about the design of the trial.
• Trials population: This dataset delineates the demographic origins of participants in clinical trials primarily conducted across European countries. This dataset include structured attributes indicating whether the trial pertains to a specific gender, age group or healthy volunteers. Each of these categories is labeled as (`1') or (`0') respectively denoting whether it is included in the trials or not. It is important to note that the population category should remain consistent across all countries conducting the same clinical trial identified by an eudract_number. The ground truth samples in the dataset were established by aligning information about the trial populations provided by external registries, specifically the CT.gov database and the German Trials database. Additionally, the dataset comprises other unstructured attributes that categorize the inclusion criteria for trial participants such as inclusion.
• Allergens: This dataset contains information about products and their allergens. The data was collected from the German version of the `Alnatura' (Access date: 24 November, 2020), a free database of food products from around the world `Open Food Facts', and the websites: `Migipedia', 'Piccantino', and `Das Ist Drin'. There may be overlapping products across these websites. Each product in the dataset is identified by a unique code. Samples with the same code represent the same product but are extracted from a differentb source. The allergens are indicated by (‘2’) if present, or (‘1’) if there are traces of it, and (‘0’) if it is absent in a product. The dataset also includes information on ingredients in the products. Overall, the dataset comprises categorical structured data describing the presence, trace, or absence of specific allergens, and unstructured text describing ingredients.

N.B: Each '.zip' file contains a set of 5 '.csv' files which are part of the afro-mentioned datasets:

• "{dataset_name}_train.csv": samples used for the ML-model training. (e.g "allergens_train.csv")
• "{dataset_name}_test.csv": samples used to test the the ML-model performance. (e.g "allergens_test.csv")
• "{dataset_name}_golden_standard.csv": samples represent the ground truth of the test samples. (e.g "allergens_golden_standard.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used for the ML-model training. (e.g "allergens_parker_train.csv")
• "{dataset_name}_parker_train.csv": samples repaired using Parker Engine used to test the the ML-model performance. (e.g "allergens_parker_test.csv")

This dataset contains images of Television, Sofas, Jeans and T-shirt. It Actual raw and unstructured image data extracted from online sites.

All images are of different sites. You may also find some junk images in data for example in television dataset you will find the television remote images.

This dataset is not refined intentionally to make sure practitioners should get taste of What kind of data ML/Data Science Engineer get when they start working on any project in industry.

Sample purchasing data containing information on suppliers, the products they provide, and the projects those products are used for. Data created or adapted from publicly available sources.

These datasets contain reviews from the Steam video game platform, and information about which games were bundled together.

Metadata includes

• reviews

• purchases, plays, recommends (likes)

• product bundles

• pricing information

Basic Statistics:

• Reviews: 7,793,069

• Users: 2,567,538

• Items: 15,474

• Bundles: 615

This data release provides two example groundwater-level datasets used to benchmark the Automated Regional Correlation Analysis for Hydrologic Record Imputation (ARCHI) software package (Levy and others, 2024). The first dataset contains groundwater-level records and site metadata for wells located on Long Island, New York (NY) and some surrounding mainland sites in New York and Connecticut. The second dataset contains groundwater-level records and site metadata for wells located in the southeastern San Joaquin Valley of the Central Valley, California (CA). For ease of exposition these are referred to as NY and CA datasets, respectively. Both datasets are formatted with column headers that can be read by the ARCHI software package within the R computing environment. These datasets were used to benchmark the imputation accuracy of three ARCHI model settings (OLS, ridge, and MOVE.1) against the widely used imputation program missForest (Stekhoven and Bühlmann, 2012). The ARCHI program was used to process the NY and CA datasets on monthly and annual timesteps, respectively, filter out sites with insufficient data for imputation, and create 200 test datasets from each of the example datasets with 5 percent of observations removed at random (herein, referred to as "holdouts"). Imputation accuracy for test datasets was assessed using normalized root mean square error (NRMSE), which is the root mean square error divided by the standard deviation of the observed holdout values. ARCHI produces prediction intervals (PIs) using a non-parametric bootstrapping routine, which were assessed by computing a coverage rate (CR) defined as the proportion of holdout observations falling within the estimated PI. The multiple regression models included with the ARCHI package (OLS and ridge) were further tested on all test datasets at eleven different levels of the p_per_n input parameter, which limits the maximum ratio of regression model predictors (p) per observations (n) as a decimal fraction greater than zero and less than or equal to one. This data release contains ten tables formatted as tab-delimited text files. The “CA_data.txt” and “NY_data.txt” tables contain 243,094 and 89,997 depth-to-groundwater measurement values (value, in feet below land surface) indexed by site identifier (site_no) and measurement date (date) for CA and NY datasets, respectively. The “CA_sites.txt” and “NY_sites.txt” tables contain site metadata for the 4,380 and 476 unique sites included in the CA and NY datasets, respectively. The “CA_NRMSE.txt” and “NY_NRMSE.txt” tables contain NRMSE values computed by imputing 200 test datasets with 5 percent random holdouts to assess imputation accuracy for three different ARCHI model settings and missForest using CA and NY datasets, respectively. The “CA_CR.txt” and “NY_CR.txt” tables contain CR values used to evaluate non-parametric PIs generated by bootstrapping regressions with three different ARCHI model settings using the CA and NY test datasets, respectively. The “CA_p_per_n.txt” and “NY_p_per_n.txt” tables contain mean NRMSE values computed for 200 test datasets with 5 percent random holdouts at 11 different levels of p_per_n for OLS and ridge models compared to training error for the same models on the entire CA and NY datasets, respectively. References Cited Levy, Z.F., Stagnitta, T.J., and Glas, R.L., 2024, ARCHI: Automated Regional Correlation Analysis for Hydrologic Record Imputation, v1.0.0: U.S. Geological Survey software release, https://doi.org/10.5066/P1VVHWKE. Stekhoven, D.J., and Bühlmann, P., 2012, MissForest—non-parametric missing value imputation for mixed-type data: Bioinformatics 28(1), 112-118. https://doi.org/10.1093/bioinformatics/btr597.

The Shopee Products Dataset is a comprehensive resource that empowers businesses, researchers, and analysts to gain a holistic view of the Shopee e-commerce ecosystem. Whether your goal is to conduct market analysis, optimize pricing strategies, understand customer behavior, or evaluate competitors, this dataset offers the essential information you need to make informed decisions and succeed in the dynamic world of Shopee. At its core, this dataset provides key attributes such as product ID, title, ratings, reviews, pricing details, and seller information, among others. These fundamental data elements offer insights into product performance, customer sentiment, and seller credibility.

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

Below are the datasets specified, along with the details of their references, authors, and download sources.

----------- STS-Gold Dataset ----------------

The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

File name: sts_gold_tweet.csv

----------- Amazon Sales Dataset ----------------

This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

Features:

• product_id - Product ID
• product_name - Name of the Product
• category - Category of the Product
• discounted_price - Discounted Price of the Product
• actual_price - Actual Price of the Product
• discount_percentage - Percentage of Discount for the Product
• rating - Rating of the Product
• rating_count - Number of people who voted for the Amazon rating
• about_product - Description about the Product
• user_id - ID of the user who wrote review for the Product
• user_name - Name of the user who wrote review for the Product
• review_id - ID of the user review
• review_title - Short review
• review_content - Long review
• img_link - Image Link of the Product
• product_link - Official Website Link of the Product

License: CC BY-NC-SA 4.0

File name: amazon.csv

----------- Rotten Tomatoes Reviews Dataset ----------------

This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

File name: data_rt.csv

----------- Preprocessed Dataset Sentiment Analysis ----------------

Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
Stemmed and lemmatized using nltk.
Sentiment labels are generated using TextBlob polarity scores.

The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

DOI: 10.34740/kaggle/dsv/3877817

Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

This dataset was used in the experimental phase of my research.

File name: EcoPreprocessed.csv

----------- Amazon Earphones Reviews ----------------

This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

License: U.S. Government Works

Source: www.amazon.in

File name (original): AllProductReviews.csv (contains 14337 reviews)

File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

----------- Amazon Musical Instruments Reviews ----------------

This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

Source: http://jmcauley.ucsd.edu/data/amazon/

File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

1) Data Introduction • The Sample Sales Data is a retail sales dataset of 2,823 orders and 25 columns that includes a variety of sales-related data, including order numbers, product information, quantity, unit price, sales, order date, order status, customer and delivery information.

2) Data Utilization (1) Sample Sales Data has characteristics that: • This dataset consists of numerical (sales, quantity, unit price, etc.), categorical (product, country, city, customer name, transaction size, etc.), and date (order date) variables, with missing values in some columns (STATE, ADDRESSLINE2, POSTALCODE, etc.). (2) Sample Sales Data can be used to: • Analysis of sales trends and performance by product: Key variables such as order date, product line, and country can be used to visualize and analyze monthly and yearly sales trends, the proportion of sales by product line, and top sales by country and region. • Segmentation and marketing strategies: Segmentation of customer groups based on customer information, transaction size, and regional data, and use them to design targeted marketing and customized promotion strategies.

Access our extensive Facebook datasets that provide detailed information on public posts, pages, and user engagement. Gain insights into post performance, audience interactions, page details, and content trends with our ethically sourced data. Free samples are available for evaluation. Over 940M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Post ID Post Content & URL Date Posted Hashtags Number of Comments Number of Shares Likes & Reaction Counts (by type) Video View Count Page Name & Category Page Followers & Likes Page Verification Status Page Website & Contact Info Is Sponsored Post Attachments (Images/Videos) External Link Data And much more

Products Demand Forecasting Dataset

Overview

This dataset provides synthetic data related to demand forecasting to help predict product demand based on various factors including historical sales data, marketing campaigns, seasonal trends, pricing strategies, competitor pricing, stock availability, and public holidays.

Features

1. Date

   • Description: Date of the sales data
   • Data Type: Date
   • Format: YYYY-MM-DD
   • Example: "2021-05-25"
   • Source: Synthetic generated data

2. Product_ID

   • Description: Unique identifier for each product
   • Data Type: String
   • Example: "P001"
   • Source: Synthetic generated data

3. Base_Sales

   • Description: Base sales data without any marketing or seasonal effects
   • Data Type: Numeric (Integer)
   • Format: Integer
   • Example: 120
   • Source: Synthetic generated data

4. Marketing_Campaign

   • Description: Type of marketing campaign
   • Data Type: Categorical
   • Categories: 'None', 'Email', 'Social Media', 'TV', 'Radio'
   • Example: "Social Media"
   • Source: Synthetic generated data

5. Marketing_Effect

   • Description: Impact of the marketing campaign on sales
   • Data Type: Numeric (Float)
   • Format: Float
   • Example: 1.5
   • Source: Calculated based on the chosen marketing campaign

6. Seasonal_Trend

   • Description: Seasonal trend affecting the sales
   • Data Type: Categorical
   • Categories: 'Winter', 'Spring', 'Summer', 'Fall'
   • Example: "Winter"
   • Source: Synthetic generated data

7. Seasonal_Effect

   • Description: Seasonal effect on sales
   • Data Type: Numeric (Float)
   • Format: Float
   • Example: 0.8
   • Source: Calculated based on the chosen seasonal trend

8. Price

   • Description: Price of the product
   • Data Type: Numeric (Float)
   • Format: Float
   • Example: 50.0
   • Source: Synthetic generated data

9. Discount

   • Description: Discount offered on the product
   • Data Type: Numeric (Float)
   • Format: Float
   • Example: 0.1 (10% discount)
   • Source: Synthetic generated data

10. Competitor_Price

    • Description: Price of the same product offered by competitors
    • Data Type: Numeric (Float)
    • Format: Float
    • Example: 48.0
    • Source: Synthetic generated data

11. Stock_Availability

    • Description: Number of units available in stock
    • Data Type: Numeric (Integer)
    • Format: Integer
    • Example: 100
    • Source: Synthetic generated data

12. Public_Holiday

    • Description: Indicates whether the date is a public holiday or not
    • Data Type: Boolean
    • Format: True/False
    • Example: True
    • Source: Synthetic generated data

13. Demand

    • Description: Final demand calculated considering marketing, seasonal effects, and other factors
    • Data Type: Numeric (Integer)
    • Format: Integer
    • Example: 180
    • Source: Calculated based on Base_Sales, Marketing_Effect, Seasonal_Effect, Price, Discount, Competitor_Price, Stock_Availability, and Public_Holiday

Target Variable

• Demand: Indicates the final demand after considering marketing campaigns, seasonal trends, pricing strategies, competitor pricing, stock availability, and public holidays.

Data Range

• Total number of records: 35000
• Date range: January 2019 to December 2021

Source

This dataset is synthetic and was generated using Python. It is intended for educational and research purposes.

Acknowledgements

• The dataset was generated using Python and the data is synthetic.

Fashion Products Dataset from GAP.com offers a curated collection of over 4,500 fashion items, meticulously extracted by Crawl Feeds' in-house web scraping team for research and analysis purposes. This dataset, last updated on October 11, 2021, encompasses a diverse range of products, including clothing, accessories, and more, providing a comprehensive view of GAP's offerings.

Key Features:

• Comprehensive Data Points: Each entry in the dataset includes 16 essential attributes such as product URL, name, product ID (PID), brand, price, currency, condition, availability, color, SKU, product details, average rating, review count, images, breadcrumbs, and the date of data extraction.

• Sample Dataset Access: Prospective users can view a sample of the dataset by signing in, allowing them to assess its structure and relevance to their specific needs.

• Immediate Availability: The dataset is readily available for purchase at $14.00 and is delivered in JSON format, ensuring seamless integration into various applications and systems.

For businesses and researchers seeking more extensive data, the Powerful Fashion Dataset offers a broader spectrum of fashion-related information. This comprehensive dataset is designed to transform your fashion business by providing insights into trend forecasting, customer behavior analysis, and market dynamics. Leveraging such data can enhance decision-making processes, optimize supply chains, and identify emerging markets, ensuring your brand stays ahead in the competitive fashion industry.