Dataset

This dataset was created by Mostafa Ashraf

Contents

Dataset

This dataset was created by Tushar Y

Contents

Amazon Sales Report

Overview:

This dataset provides detailed sales data from Amazon, offering a comprehensive look at various product categories and their performance over time. It includes information on sales figures, order details, product categories, and customer demographics.

Features:

1. Order ID

Description: A unique identifier for each order placed on Amazon. This field helps to track individual orders and link related records.

2. Dates

Description: The date when the order was placed. This field is crucial for analyzing sales trends over time and identifying seasonal patterns.

3. Status

Description: The current status of the order (e.g., Shipped, Delivered, Pending). This field provides insight into the order fulfillment process and helps monitor order processing efficiency.

4. Fulfillment

Description: Indicates the method used to fulfill the order (e.g., Fulfilled by Amazon, Fulfilled by Seller). This feature helps in analyzing the performance of different fulfillment methods and their impact on customer satisfaction.

5. Sales Channel

Description: The channel through which the sale was made (e.g., Amazon Website, Mobile App). This field is useful for evaluating the effectiveness of different sales channels and understanding customer preferences.

6. Category

Description: The product category to which the purchased item belongs (e.g., Electronics, Clothing, Home Goods). This feature aids in analyzing sales performance across various product categories.

7. Ship Service Level

Description: The shipping service level selected for the order (e.g., Standard Shipping, Two-Day Shipping). This field helps to assess the impact of shipping options on delivery times and customer satisfaction.

8. Size

Description: The size of the product ordered (e.g., Small, Medium, Large). This feature is relevant for analyzing sales performance based on product size and understanding inventory requirements.

9. Carrier Status

Description: The status of the shipment with the carrier (e.g., In Transit, Delivered, Returned). This field provides insights into the shipping process and helps in monitoring delivery performance and handling returns.

Use Cases:

Sales Analysis:

Examine trends in sales over time, identify peak periods, and analyze performance by product category.

Customer Insights:

Explore customer demographics to understand purchasing behavior and preferences.

Inventory Management:

Assess which products are performing well and which are not, aiding in inventory and supply chain management.

Marketing Strategies:

Develop targeted marketing campaigns based on sales trends and customer profiles.

Data Source:

This dataset is a simulated collection of Amazon sales data and is intended for educational and analytical purposes.

Acknowledgments:

This dataset was created to facilitate data analysis and machine learning projects. It is ideal for practicing data manipulation, statistical analysis, and predictive modeling.

Analysis of ‘Superstore Sales Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rohitsahoo/sales-forecasting on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Retail dataset of a global superstore for 4 years. Perform EDA and Predict the sales of the next 7 days from the last date of the Training dataset!

Content

Time series analysis deals with time series based data to extract patterns for predictions and other characteristics of the data. It uses a model for forecasting future values in a small time frame based on previous observations. It is widely used for non-stationary data, such as economic data, weather data, stock prices, and retail sales forecasting.

Dataset

The dataset is easy to understand and is self-explanatory

Inspiration

Perform EDA and Predict the sales of the next 7 days from the last date of the Training dataset!

--- Original source retains full ownership of the source dataset ---

Discover the latest insights from Market Research Intellect's report_name, valued at current_value in 2024, with significant growth projected to forecast_value by 2033 at a CAGR of cagr_value (2026-2033).

Dataset

This dataset was created by shubham kumar

Contents

Context and Methodology

Research Domain:
The dataset is part of a project focused on retail sales forecasting. Specifically, it is designed to predict daily sales for Rossmann, a chain of over 3,000 drug stores operating across seven European countries. The project falls under the broader domain of time series analysis and machine learning applications for business optimization. The goal is to apply machine learning techniques to forecast future sales based on historical data, which includes factors like promotions, competition, holidays, and seasonal trends.

Purpose:
The primary purpose of this dataset is to help Rossmann store managers predict daily sales for up to six weeks in advance. By making accurate sales predictions, Rossmann can improve inventory management, staffing decisions, and promotional strategies. This dataset serves as a training set for machine learning models aimed at reducing forecasting errors and supporting decision-making processes across the company’s large network of stores.

How the Dataset Was Created:
The dataset was compiled from several sources, including historical sales data from Rossmann stores, promotional calendars, holiday schedules, and external factors such as competition. The data is split into multiple features, such as the store's location, promotion details, whether the store was open or closed, and weather information. The dataset is publicly available on platforms like Kaggle and was initially created for the Kaggle Rossmann Store Sales competition. The data is made accessible via an API for further analysis and modeling, and it is structured to help machine learning models predict future sales based on various input variables.

Technical Details

Dataset Structure:

The dataset consists of three main files, each with its specific role:

1. Train:
   This file contains the historical sales data, which is used to train machine learning models. It includes daily sales information for each store, as well as various features that could influence the sales (e.g., promotions, holidays, store type, etc.).

   https://handle.test.datacite.org/10.82556/yb6j-jw41
   PID: b1c59499-9c6e-42c2-af8f-840181e809db

2. Test2:
   The test dataset mirrors the structure of train.csv but does not include the actual sales values (i.e., the target variable). This file is used for making predictions using the trained machine learning models. It is used to evaluate the accuracy of predictions when the true sales data is unknown.

   https://handle.test.datacite.org/10.82556/jerg-4b84
   PID: 7cbb845c-21dd-4b60-b990-afa8754a0dd9

3. Store:
   This file provides metadata about each store, including information such as the store’s location, type, and assortment level. This data is essential for understanding the context in which the sales data is gathered.

   https://handle.test.datacite.org/10.82556/nqeg-gy34
   PID: 9627ec46-4ee6-4969-b14a-bda555fe34db

Data Fields Description:

• Id: A unique identifier for each (Store, Date) combination within the test set.

• Store: A unique identifier for each store.

• Sales: The daily turnover (target variable) for each store on a specific day (this is what you are predicting).

• Customers: The number of customers visiting the store on a given day.

• Open: An indicator of whether the store was open (1 = open, 0 = closed).

• StateHoliday: Indicates if the day is a state holiday, with values like:

  • 'a' = public holiday,

  • 'b' = Easter holiday,

  • 'c' = Christmas,

  • '0' = no holiday.

• SchoolHoliday: Indicates whether the store is affected by school closures (1 = yes, 0 = no).

• StoreType: Differentiates between four types of stores: 'a', 'b', 'c', 'd'.

• Assortment: Describes the level of product assortment in the store:

  • 'a' = basic,

  • 'b' = extra,

  • 'c' = extended.

• CompetitionDistance: Distance (in meters) to the nearest competitor store.

• CompetitionOpenSince[Month/Year]: The month and year when the nearest competitor store opened.

• Promo: Indicates whether the store is running a promotion on a particular day (1 = yes, 0 = no).

• Promo2: Indicates whether the store is participating in Promo2, a continuing promotion for some stores (1 = participating, 0 = not participating).

• Promo2Since[Year/Week]: The year and calendar week when the store started participating in Promo2.

• PromoInterval: Describes the months when Promo2 is active, e.g., "Feb,May,Aug,Nov" means the promotion starts in February, May, August, and November.

Software Requirements

To work with this dataset, you will need to have specific software installed, including:

• DBRepo Authorization: This is required to access the datasets via the DBRepo API. You may need to authenticate with an API key or login credentials to retrieve the datasets.

• Python Libraries: Key libraries for working with the dataset include:

  • pandas for data manipulation,

  • numpy for numerical operations,

  • matplotlib and seaborn for data visualization,

  • scikit-learn for machine learning algorithms.

Additional Resources

Several additional resources are available for working with the dataset:

1. Presentation:
   A presentation summarizing the exploratory data analysis (EDA), feature engineering process, and key insights from the analysis is provided. This presentation also includes visualizations that help in understanding the dataset’s trends and relationships.

2. Jupyter Notebook:
   A Jupyter notebook, titled Retail_Sales_Prediction_Capstone_Project.ipynb, is provided, which details the entire machine learning pipeline, from data loading and cleaning to model training and evaluation.

3. Model Evaluation Results:
   The project includes a detailed evaluation of various machine learning models, including their performance metrics like training and testing scores, Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). This allows for a comparison of model effectiveness in forecasting sales.

4. Trained Models (.pkl files):
   The models trained during the project are saved as .pkl files. These files contain the trained machine learning models (e.g., Random Forest, Linear Regression, etc.) that can be loaded and used to make predictions without retraining the models from scratch.

5. sample_submission.csv:
   This file is a sample submission file that demonstrates the format of predictions expected when using the trained model. The sample_submission.csv contains predictions made on the test dataset using the trained Random Forest model. It provides an example of how the output should be structured for submission.

These resources provide a comprehensive guide to implementing and analyzing the sales forecasting model, helping you understand the data, methods, and results in greater detail.

According to Cognitive Market Research, The Global EDA Market size will be USD 14.9 billion in 2023 and will grow at a compound annual growth rate (CAGR) of 10.50% from 2023 to 2030.

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The cat segment held the highest EDA Market revenue share in 2023.
North American EDA will continue to lead, whereas the European EDA Market will experience the most substantial growth until 2030.

Supply Chain and Risk Analysis to Provide Viable Market Output

The industry is facing supply chain and logistics disruptions. EDA tools have been instrumental in analyzing supply chain data, identifying vulnerabilities, predicting risks, and developing disruption mitigation strategies. Consumer behavior has undergone drastic changes due to blockages and restrictions. EDA helps companies analyze changing trends in buying behavior, online shopping preferences, and demand patterns, enabling organizations to adjust their marketing and sales strategies accordingly.

Health and Pharmaceutical Research to Propel Market Growth.

EDA tools have played a key role in analyzing large amounts of data related to vaccine development, drug trials, patient records and epidemiological studies. These tools have helped researchers process and interpret complex medical data, leading to advances in the development of treatments and vaccines. The pandemic has created challenges in data collection, especially in sectors affected by lockdowns or blackouts. Rapidly changing conditions and incomplete data sets make effective EDA difficult due to data quality issues. The economic uncertainty caused by the pandemic has led to budget cuts in some sectors, impacting investment in new technologies. Some organizations have limited budgets that limit their ability to adopt or update EDA tools.

Market Dynamics of the EDA

Privacy and Data Security Issues to Restrict Market Growth.

With the focus on data privacy regulations such as GDPR, CCPA, etc., organizations need to ensure compliance when handling sensitive data. These compliance requirements may limit the scope of the EDA by limiting the availability and use of certain data sets for information analysis. EDA often requires data analysts or data scientists who are skilled in statistical analysis and data visualization tools. A lack of professionals with these specialized skills can hinder an organization's ability to use EDA tools effectively, limiting adoption. Advanced EDA techniques can involve complex algorithms and statistical techniques that are difficult for non-technical users to understand. Interpreting results and deriving actionable insights from EDA results pose challenges that affect applicability to a wider audience.

Key Opportunity of market.

Growing miniaturization in various industries can be an opportunity.

With the age of highly advanced electronics, miniaturization has become a trend that enabled organizations across diverse sectors such as healthcare, consumer electronics, aerospace and defense, automotive and others to design miniature electronic devices. The devices incorporate miniaturized semiconductor components, e.g., surgical instruments and blood glucose meters in healthcare, fitness bands in wearable devices, automotive modules in the automotive sector, and intelligent baggage labels. Miniaturization has a number of advantages such as freeing space for other features and better batteries. The increased consciousness among consumers towards fitness is fueling the demand for smaller fitness devices such as smartwatches and fitness trackers. This is motivating companies to come up with innovative products with improved features, while researchers are concentrating on cost-effective and efficient product development through electronic design tools. Besides, use of portable equipment has gained immense popularity among media professionals because of the increasing demand for live reporting of different events like riots, accidents, sports, and political rallies. As a result of the inconvenience in the use of cumbersome TV production vans to access such events, demand for portable handheld equipment has risen. Such devices are simply portable and can be quickly moved to the event venue if carried in backpacks. Therefore, the need for compact devices across various indust...

Dataset

This dataset was created by Bishoy Nagy 2020

Contents

The Global Cloud Electronic Design Automation (EDA)market is projected to grow significantly, from USD 3,321.9 million in 2025 to USD 6,943.2 million by 2035 an it is reflecting a strong CAGR of 9.6%.

Attributes	Description
Industry Size (2025E)	USD 3,321.9 million
Industry Size (2035F)	USD 6,943.2 million
CAGR (2025 to 2035)	9.6% CAGR

Contracts & Deals Analysis

Company	Synopsys, Inc.
Contract/Development Details	Secured a contract with a leading semiconductor company to provide cloud-based EDA tools, aiming to enhance chip design efficiency and reduce time-to-market.
Date	February 2024
Contract Value (USD Million)	Approximately USD 50
Renewal Period	3 years

Company	Cadence Design Systems
Contract/Development Details	Partnered with a consumer electronics giant to deploy cloud EDA solutions for next-generation product development, focusing on scalability and collaboration.
Date	August 2024
Contract Value (USD Million)	Approximately USD 45
Renewal Period	4 years

Country-wise Insights

Countries	CAGR from 2025 to 2035
India	12.9%
China	11.7%
Germany	7.3%
Japan	9.2%
United States	8.5%

Category-wise Insights

Segment	Semiconductor Intellectual Property (Product Type)
CAGR (2025 to 2035)	11.5%

Segment	Telecommunication (Vertical)
Value Share (2025)	24.8%

Competition Outlook: Cloud Electronic Design Automation (EDA) Market

Company Name	Estimated Market Share (%)
Synopsys, Inc.	30-35%
Cadence Design Systems, Inc.	25-30%
Siemens EDA (Mentor Graphics)	15-20%
ANSYS, Inc.	7-10%
Keysight Technologies	5-8%
Other Companies (combined)	12-18%

Report of Electronic Design Automation (EDA) Software is currently supplying a comprehensive analysis of many things which are liable for economy growth and factors which could play an important part in the increase of the marketplace in the prediction period. The record of Electronic Design Automation (EDA) Software Industry is providing the thorough study on the grounds of market revenue discuss production and price happened. The report also provides the overview of the segmentation on the basis of area, contemplating the particulars of earnings and sales pertaining to marketplace.

The global electronic design automation market is expected to reach a value of US$ 18.45 billion in 2024, as revealed in an updated Fact.MR research report. Worldwide demand for electronic design automation solutions is analyzed to increase at 7.4% CAGR and reach a market value of US$ 37.68 billion by 2034.

Report Attribute	Detail
Electronic Design Automation Market Size (2024E)	US$ 18.45 Billion
Forecasted Market Value (2034F)	US$ 37.68 Billion
Global Market Growth Rate (2024 to 2034)	7.4% CAGR
South Korea Market Growth Rate (2024 to 2034)	9.2% CAGR
Europe Market Share (2034F)	25%
Market Share of Microprocessors & Microcontrollers (2034F)	53%
Key Companies Profiled	Cadence Design Systems; Keysight Technologies, Inc.; Siemens; Ansys Inc.; Synopsys Inc.; Zuken Inc.; Altium Limited; Elnfochips; Advance Micro Devices; Silvaco, Inc.

Country-wise Insights

Attribute	United States
Market Value (2024E)	US$ 5.05 Billion
Growth Rate (2024 to 2034)	6.2% CAGR
Projected Value (2034F)	US$ 9.23 Billion

Attribute	China
Market Value (2024E)	US$ 1.98 Billion
Growth Rate (2024 to 2034)	8.5% CAGR
Projected Value (2034F)	US$ 4.47 Billion

Attribute	Japan
Market Value (2024E)	US$ 1.17 Billion
Growth Rate (2024 to 2034)	9% CAGR
Projected Value (2034F)	US$ 2.77 Billion

Category-wise Insights

Attribute	IC Physical Design & Verification
Segment Value (2024E)	US$ 5.5 Billion
Growth Rate (2024 to 2034)	6.3% CAGR
Projected Value (2034F)	US$ 10.17 Billion

Attribute	Microprocessors & Microcontrollers
Segment Value (2024E)	US$ 9.97 Billion
Growth Rate (2024 to 2034)	7.2% CAGR
Projected Value (2034F)	US$ 19.97 Billion

Report of EDA Tools Market for IC is currently supplying a comprehensive analysis of many things which are liable for economy growth and factors which could play an important part in the increase of the marketplace in the prediction period. The record of EDA Tools Market for IC Industry is providing the thorough study on the grounds of market revenue discuss production and price happened. The report also provides the overview of the segmentation on the basis of area, contemplating the particulars of earnings and sales pertaining to marketplace.

Report of EDA for Electroinc Market is currently supplying a comprehensive analysis of many things which are liable for economy growth and factors which could play an important part in the increase of the marketplace in the prediction period. The record of EDA for Electroinc Industry is providing the thorough study on the grounds of market revenue discuss production and price happened. The report also provides the overview of the segmentation on the basis of area, contemplating the particulars of earnings and sales pertaining to marketplace.

Numavičiaus įmonė "EDA" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Dataset

This dataset was created by Alex Sueppel

Contents

The global Ethylenediamine (EDA) market stood at approximately 698 thousand tonnes in 2024 and is anticipated to grow at a CAGR of 6.22% during the forecast period until 2035.

UAB "EDA Investments" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Gain in-depth insights into Tissue Engineered Collagen Biomaterials Sales Market Report from Market Research Intellect, valued at USD 1.5 billion in 2024, and projected to grow to USD 3.8 billion by 2033 with a CAGR of 10.5% from 2026 to 2033.

Restaurant Sales Dataset with Dirt Documentation

Overview

The Restaurant Sales Dataset with Dirt contains data for 17,534 transactions. The data introduces realistic inconsistencies ("dirt") to simulate real-world scenarios where data may have missing or incomplete information. The dataset includes sales details across multiple categories, such as starters, main dishes, desserts, drinks, and side dishes.

Dataset Use Cases

This dataset is suitable for: - Practicing data cleaning tasks, such as handling missing values and deducing missing information. - Conducting exploratory data analysis (EDA) to study restaurant sales patterns. - Feature engineering to create new variables for machine learning tasks.

Columns Description

Column Name	Description	Example Values
Order ID	A unique identifier for each order.	ORD_123456
Customer ID	A unique identifier for each customer.	CUST_001
Category	The category of the purchased item.	Main Dishes, Drinks
Item	The name of the purchased item. May contain missing values due to data dirt.	Grilled Chicken, None
Price	The static price of the item. May contain missing values.	15.0, None
Quantity	The quantity of the purchased item. May contain missing values.	1, None
Order Total	The total price for the order (Price * Quantity). May contain missing values.	45.0, None
Order Date	The date when the order was placed. Always present.	2022-01-15
Payment Method	The payment method used for the transaction. May contain missing values due to data dirt.	Cash, None

Key Characteristics

1. Data Dirtiness:

   • Missing values in key columns (Item, Price, Quantity, Order Total, Payment Method) simulate real-world challenges.
   • At least one of the following conditions is ensured for each record to identify an item:
     • Item is present.
     • Price is present.
     • Both Quantity and Order Total are present.
   • If Price or Quantity is missing, the other is used to deduce the missing value (e.g., Order Total / Quantity).

2. Menu Categories and Items:

   • Items are divided into five categories:
     • Starters: E.g., Chicken Melt, French Fries.
     • Main Dishes: E.g., Grilled Chicken, Steak.
     • Desserts: E.g., Chocolate Cake, Ice Cream.
     • Drinks: E.g., Coca Cola, Water.
     • Side Dishes: E.g., Mashed Potatoes, Garlic Bread.

3 Time Range: - Orders span from January 1, 2022, to December 31, 2023.

Cleaning Suggestions

1. Handle Missing Values:

   • Fill missing Order Total or Quantity using the formula: Order Total = Price * Quantity.
   • Deduce missing Price from Order Total / Quantity if both are available.

2. Validate Data Consistency:

   • Ensure that calculated values (Order Total = Price * Quantity) match.

3. Analyze Missing Patterns:

   • Study the distribution of missing values across categories and payment methods.

Menu Map with Prices and Categories

Category	Item	Price
Starters	Chicken Melt	8.0
Starters	French Fries	4.0
Starters	Cheese Fries	5.0
Starters	Sweet Potato Fries	5.0
Starters	Beef Chili	7.0
Starters	Nachos Grande	10.0
Main Dishes	Grilled Chicken	15.0
Main Dishes	Steak	20.0
Main Dishes	Pasta Alfredo	12.0
Main Dishes	Salmon	18.0
Main Dishes	Vegetarian Platter	14.0
Desserts	Chocolate Cake	6.0
Desserts	Ice Cream	5.0
Desserts	Fruit Salad	4.0
Desserts	Cheesecake	7.0
Desserts	Brownie	6.0
Drinks	Coca Cola	2.5
Drinks	Orange Juice	3.0
Drinks ...