Dataset Overview: This dataset comprises 2,000 entries scraped from AliExpress, detailing a variety of furniture products. It captures key sales metrics and product details, offering a snapshot of consumer purchasing patterns and market trends in the online furniture retail space.

Data Science Applications: The dataset is ripe for exploratory data analysis, market trend analysis, and price optimization studies. It can also be used for predictive modeling to forecast sales, understand the impact of discounts on sales volume, and analyze the relationship between product features and their popularity.

Column Descriptors: - productTitle: The name of the furniture item. - originalPrice: The original price of the item before any discounts. - price: The current selling price of the item. - sold: The number of units sold. - tagText: Additional tags associated with the item (e.g., "Free shipping").

Ethically Collected Data: The data was collected in compliance with ethical standards, ensuring respect for user privacy and platform terms of service.

Acknowledgements: This dataset was created with data sourced from AliExpress, using Apify for scraping. The thumbnail image was generously provided by Spacejoy on Unsplash. We extend our gratitude to these parties for their contributions to this dataset.

Photo by Spacejoy on Unsplash.

Furniture Ecommerce Product Data

Price, Image, and Features

By [source]

About this dataset

Welcome to our dataset of furniture products from a leading e-commerce website! Explore the ever-evolving online shopping trends. This data set has all you need to know about how consumers shop in the online furniture market, encompassing comprehensive information of each product such as titles, prices, categories and ratings!

Gain insight into consumer preferences about different types of products by analyzing the data on product features in JSON format or the quantity of ratings each product has received. Analyze how prices affect consumer decisions with details on item URL and image URL for better understanding. Reach out to discover emerging products and industry trends that will help you make better informed business decisions.

The columns included in this dataset are: Title, Price, Category_path, Rating, Qty_califications, Features_JSON_format, Image_Url and Item_Url. Dive into this comprehensive collection of must-have information for any furniture enthusiast or business to succeed

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How to use the dataset

This dataset is a great resource for exploring the shopping trends in furniture e-commerce. As such, it can be used to gain insights into emerging products, industry trends and popular products. Here are some ideas on how you can use this dataset:

• Analyze the rating of each product and draw insights from users’ satisfaction with different features or categories. You can also compare ratings across brands for a better understanding of their relative popularity in the market.
• Review the pricing data to assess how customers respond to different pricing strategies and what price points tend to appeal more strongly to them. This will help you decide which prices will maximize your profits while offering competitive rates in comparison with other businesses in your industry sector.
• Create data visualizations of categories, ratings and prices together to identify categories that have higher customer interest at different price points so that you can adjust your marketing strategy accordingly, or focus new product development efforts therefrom ahead of time;
• Study the features offered by competitors' products along with their price tags so that new customers are attracted more towards similar good quality offerings at a lower cost compared to other retailers;
  5 Identify seasonal variations in customer purchasing behavior such as peak buying periods based on location or product category;
  6 Conduct demand forecasting analysis by measuring purchase frequency over long period of time for trending items vs untrending ones.;
  7 Use item URL’s/ Image URL's & Title columns along with commerce website crawling technique for framing targeted advertisements reaching out potential customers according specific& targeted conditions like purchase frequency or certain category related keywords searched etc;

Research Ideas

• Analyzing customer rating trends: This dataset can be used to analyze customer comments and ratings in order to determine which products have the highest/lowest rating and why, providing valuable insights into what kind of characteristics customers look for when making purchases.
• Developing targeted promotions: Utilizing this dataset, businesses can compare their product prices against competitors’, allowing them to identify the most competitively priced products and develop targeted promotions accordingly in order to maximize sales performance.
• Determining product feature relevance: By analyzing the relationship between customer ratings and features such as size, color, design elements etc., businesses can determine which product features are most relevant for customers hence strategize their product development better

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: competitors_raw_data.csv | Column name | Description | |:-------------------------|:--------------...

E-Commerce Data - Chairs on eBay This dataset has been compiled to explore and analyze trends in chair sales on eBay. It serves as a practical resource for understanding e-commerce dynamics, particularly in the furniture sector.

This dataset contains detailed information about chairs listed for sale on eBay, including pricing, item condition, seller ratings, and other relevant attributes. It provides valuable insights for anyone interested in analyzing e-commerce trends, predicting prices, or studying market segmentation in the furniture category.

The data was ethically mined using the Apify API from Apify.com, ensuring that all personally identifiable information (PII) has been removed to maintain privacy and confidentiality. This dataset is a great resource for practicing data cleaning, price prediction models, and market trend analysis.

This dataset is perfect for those working on e-commerce analytics, machine learning projects, or anyone exploring real-world retail data. Feel free to reach out for any questions or collaboration on related projects!

We have a comprehensive database of 100 most-selling companies for each country (in Europe and USA) for each market segment. We can integrate companies based on your specific needs and provide additional insights blending them with other market data.

The market segments we cover are: Animals & Pets Animal Health Animal Parks & Zoo Cats & Dogs Horses & Riding Pet Services Pet Stores Beauty & Well-being Cosmetics & Makeup Hair Care & Styling Personal Care Salons & Clinics Tattoos & Piercings Wellness & Spa Yoga & Meditation Business Services Administration & Services Associations & Centers HR & Recruiting Import & Export IT & Communication Office Space & Supplies Print & Graphic Design Research & Development Sales & Marketing Shipping & Logistics Wholesale Construction & Manufacturing Architects & Engineers Building Materials Chemicals & Plastic Construction Services Contractors & Consultants Factory Equipment Garden & Landscaping Industrial Supplies Manufacturing Production Services Tools & Equipment Education & Training Colleges & Universities Courses & Classes Education Services Language Learning Music & Theater Classes School & High School Specials Schools Vocational Training Electronics & Technology Appliances & Electronics Audio & Visual Computers & Phones Internet & Software Repair & Services Events & Entertainment Adult Entertainment Children's Entertainment Clubbing & Nightlife Events & Venues Gambling Gaming Museums & Exhibits Music & Movies Theater & Opera Wedding & Party Food, Beverages & Tobacco Agriculture & Produce Asian Grocery Stores Bakery & Pastry Beer & Wine Beverages & Liquor Candy & Chocolate Coffee & Tea Food Production Fruits & Vegetables Grocery Stores & Markets Lunch & Catering Meat, Seafood & Eggs Smoking & Tobacco Health & Medical Clinics Dental Services Diagnostics & Testing Doctors & Surgeons Health Equipment Hospital & Emergency Medical Specialists Mental Health Pharmacy & Medicine Physical Aids Pregnancy & Children Therapy & Senior Health Vision & Hearing Hobbies & Crafts Art & Handicraft Astrology & Numerology Fishing & Hunting Hobbies Metal, Stone & Glass Work Music & Instruments Needlework & Knitting Outdoor Activities Painting & Paper Home & Garden Bathroom & Kitchen Cultural Goods Decoration & Interior Energy & Heating Fabric & Stationery Furniture Stores Garden & Pond Home & Garden Services Home Goods Stores Home Improvements Home Services Cleaning Service Providers Craftsman House Services House Sitting & Security Moving & Storage Plumbing & Sanitation Repair Service Providers Legal Services & Government Customs & Toll Government Department Law Enforcement Lawyers & Attorneys Legal Service Providers Libraries & Archives Municipal Department Registration Services Media & Publishing Books & Magazines Media & Information Photography Video & Sound Money & Insurance Accounting & Tax Banking & Money Credit & Debt Services Insurance Investments & Wealth Real Estate Public & Local Services Employment & Career Funeral & Memorial Housing Associations Kids & Family Military & Veteran Nature & Environment Professional Organizations Public Services & Welfare Religious Institutions Shelters & Homes Waste Management Restaurants & Bars African & Pacific Cuisine Bars & Cafes Chinese & Korean Cuisine European Cuisine General Restaurants Japanese Cuisine Mediterranean Cuisine Middle Eastern Cuisine North & South American Cuisine Southeast Asian Cuisine Takeaway Vegetarian & Diet Shopping & Fashion Accessories Clothing & Underwear Clothing Rental & Repair Costume & Wedding Jewelry & Watches Malls & Marketplaces Sports Ball Games Bat-and-ball Games Bowls & Lawn Sports Dancing & Gymnastics Equipment & Associations Extreme Sports Fitness & Weight Lifting Golf & Ultimate Hockey & Ice Skating Martial arts & Wrestling Outdoor & Winter Sports Shooting & Target Sports Swimming & Water Sports Tennis & Racquet Sports Travel & Vacation Accommodation & Lodging Activities & Tours Airlines & Air Travel Hotels Travel Agencies Utilities Energy & Power Oil & Fuel Water Utilities Vehicles & Transportation Air & Water Transport Airports & Parking Auto Parts & Wheels Bicycles Cars & Trucks Motorcycle & Powersports Other Vehicles & Trailers Taxis & Public Transport Vehicle Rental Vehicle Repair & Fuel

Fields available for each shop since the date of the first review on Trustpilot:

• businessUrl
• companyName
• reviewDate
• verified
• reviewTitle
• reviewDescription
• reviewRating
• reviewCountry
• reviewLanguage
• reviewCompanyResponse
• consumerAvatar
• consumerName
• reviewUrl

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...

Housing Prices By Location

Why Location DOES Matter in Housing Prices

By [source]

About this dataset

The Hyderabad House Data dataset contains information on various aspects of housing in Hyderabad, India, including price, locality, furnishings, and number of bedrooms and bathrooms. This data was collected from one of the housing rental portals in Hyderabad using web scraping methods, and provides valuable insights into the city's real estate market.

The dataset includes a variety of features that will be helpful for those looking to rent or buy a property in Hyderabad. In addition to general information about the property such as price, locality, and number of bedrooms and bathrooms, the dataset also includes detailed information about furniture, tennants, and area. This data can be used to help understand the city's real estate market and make informed decisions about renting or buying a property in Hyderabad

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How to use the dataset

Guide to Using the Hyderabad House Dataset

This dataset can be used to study various aspects of housing in Hyderabad, India, including price, locality, furnishings, and number of bedrooms and bathrooms. The data was collected from one of the housing rental portals in Hyderabad using web scraping methods.

To get started, you may want to take a look at the columns in the dataset:

• Bedrooms: This column indicates the number of bedrooms in a given house.
• Bathrooms: This column indicates the number of bathrooms in a given house.
• Furnishing: This column indicates whether or not the house is furnished.
• Tennants: This column indicates whether or not tenants are allowed in the house.
• Area: This column indicates the size of the house in square feet.
• Price: This column indicates the price of rent for a given house per month.

• Locality: This column indicates the locality/location of a given house.

  With this information in mind, you can now begin to explore some questions that you may be interested in answering with this dataset!

Research Ideas

• This dataset can be used to study the relationship between housing prices and various other factors such as locality, furnishings, number of bedrooms and bathrooms, etc.

• This dataset can be used to predict future housing prices in Hyderabad based on historical data.

• This dataset can be used to study the trends in the Hyderabad real estate market

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Hyderabad_House_Data.csv | Column name | Description | |:---------------|:--------------| | **** | | | Bedrooms | | | Bathrooms | | | Furnishing | | | Tennants | | | Area | | | Price | | | Locality | |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Introduction

This is a web scraping on Maroof webpage.

Content Description

Maroof is a trusted and useful service to all e-commerce dealers in Saudi Arabia. -There are 15 main panels that categorize the stores: E-market Photograph Cook Electronic solutions Academic Feminine services Design and print Party and party planning Beauty and makeup Electronic Other Cars Properties Decore and Furniture Craft -The data contains 9 columns and 27366 rows (stores).

Acknowledgements

Thanks to Misk and General Assembly for giving us the opportunity to provide this data

Inspiration

Maroof's data is very helpful for exploring the available e-stores in Saudi Arabi and predicting the possible e-stores and services in the future.

About Flipkart:

Flipkart is an Indian e-commerce company, headquartered in Bangalore, Karnataka, India. It is the largest e-commerce company in India and was founded by Sachin and Binny Bansal. The company has wide variety of products electronics like laptops, tablets, smartphones, and mobile accessories to in-vogue fashion staples like shoes, clothing and lifestyle accessories; from modern furniture like sofa sets, dining tables, and wardrobes to appliances that make your life easy like washing machines, TVs, ACs, mixer grinder juicers and other time-saving kitchen and small appliances; from home furnishings like cushion covers, mattresses and bedsheets to toys and musical instruments.

Mobile Phones

Mobile phones are one of the most rapidly rising industries, as well as one of the most prominent industries in the technology sector. The rate of increase has been exponential, with the number of mobile phone customers increasing fivefold in the last decade. Globally, the number of smartphones sold to end users climbed from 300 million in 2010 to 1.5 billion by 2020.

Flipkart and Mobile Phones

As previously stated, mobile phones are in high demand and are one of the ideal products for a novice to sell. Flipkart will be the ideal spot for a vendor to market their stuff because its reach.

Content

The dataset contains description of top 5 most popular mobile brand in India. Columns : There are 16 columns each having a title which is self explanatory. Rows : There are 430 rows each having a mobile with at least a distinct feature.

Acknowledgements

The data was retrieved directly from Flipkart website using some web crawling techniques

Assumption

We don’t have direct sales report of how many units of a mobile model was sold. In general, number of people rating a product is directly proportional to number of units sold. So, for the purpose of the solution, we are using number of people rating the product as the equivalent units sold.

Inspiration

The objective is to address a hypothetical business problem for a Flipkart Authorized Seller. According to the hypothesis the individual is looking to sell mobile phones on Flipkart. For this, the individual is looking for the best product, brand, specification and deals that can generate the most revenue with the least amount of investment and budget constraints.

Questions to be answered: 1. Whether he should sell product for a particular brand only or try to focus on model from different brands? 2. Using EDA and Data Visualization find out insights and relation between different features 3. Perform detailed analysis of each brand. 4. Assuming a budget for the problem come to a solution with maximum return.