Unlock valuable insights with our comprehensive Fashion Dataset from Flipkart. This dataset is meticulously curated, offering detailed information on a wide range of fashion products available on Flipkart.

Whether you're conducting data analysis, enhancing your machine learning models, or performing market research, this dataset is an invaluable resource. It includes product names, descriptions, prices, images, and customer reviews.

Optimize your projects with high-quality, structured data and stay ahead in the competitive fashion industry. Explore the vast collection and leverage the power of data for your research and development needs.

Crawl Feeds ready to download fashion datasets

ml-hub/flipkart-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Model:

Description: The name of the smartphone model. Example: "Samsung Galaxy S21", "iPhone 13", "Google Pixel 6". Notes: This is a categorical variable that uniquely identifies each phone. Price:

Description: The cost of the smartphone, typically in the local currency (e.g., USD). Example: 999, 799, 699. Notes: This is a numerical variable, which can be used to analyze the affordability and market positioning of different models. RAM:

Description: The amount of random-access memory (RAM) in the smartphone, typically measured in gigabytes (GB). Example: 4 GB, 8 GB, 12 GB. Notes: This numerical variable impacts the phone's ability to handle multiple tasks simultaneously and affects overall performance. Display:

Description: The specifications of the smartphone's display, often given in terms of size (in inches) and resolution. Example: "6.1 inches, 1080x2400 pixels". Notes: This variable is usually a mix of numerical and categorical data, reflecting the screen size and resolution. Rear Camera:

Description: The specifications of the main (rear) camera(s), often including the number of cameras, megapixels (MP), and other features (e.g., wide-angle, telephoto). Example: "12 MP + 12 MP dual", "108 MP". Notes: This is often a categorical variable with numerical components, indicating the camera's capabilities. Front Camera:

Description: The specifications of the front (selfie) camera, typically measured in megapixels. Example: "10 MP", "32 MP". Notes: Similar to the rear camera, this is a categorical variable with numerical components, indicating the quality of the front camera. Battery:

Description: The battery capacity of the smartphone, typically measured in milliampere-hours (mAh). Example: 4000 mAh, 5000 mAh. Notes: This numerical variable impacts the phone's battery life and usage duration. Processor:

Description: The type and model of the smartphone's processor (CPU). Example: "Snapdragon 888", "Apple A14 Bionic". Notes: This categorical variable indicates the processing power and efficiency of the phone. Star Ratings:

Description: The average user rating of the smartphone, typically on a scale from 1 to 5 stars. Example: 4.5, 3.8. Notes: This numerical variable reflects user satisfaction and can be used to gauge the overall reception of the phone. Ratings:

Description: The total number of user ratings received for the smartphone. Example: 1500, 5000. Notes: This numerical variable indicates the popularity and extent of user feedback.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The B2C E-commerce Market size was valued at USD 6.23 trillion in 2023 and is projected to reach USD 21.18 trillion by 2032, exhibiting a CAGR of 19.1 % during the forecasts period. The B2C e-commerce can be defined as the sale of commercial products or services through the internet between buyers and sellers. This market pertains to several industries that fall under its fold that includes the area of retail, travelling, electronics and digital products. Some of the most common implementations are in the ecommerce sites, mobile applications, and membership services. Some aspects of the B2C e-commerce market include increased popularity of omnichannel retailing that combines online and offline environments and the shift to the concept of individualization due to the digitalization and data processing using artificial intelligence and machine learning. Also, growth is noted in mobile commerce (m-commerce) as a result of the increase in the number of mobile devices and more effective mobile payments. To this list one should also include the concepts of social commerce and sustainability which also became significant in today’s society due to increasing importance of ethical and convenient shopping. Recent developments include: In March 2024, Blink, an Amazon company, launched the Blink Mini 2 camera. The new compact plug-in camera offers enhanced features such as person detection, a broader field of view, a built-in LED spotlight for night view in color, and improved image quality. The Blink Mini 2 is designed to work indoors and outdoors, with the option to purchase the Blink Weather Resistant Power Adapter for outdoor use. , In October 2023, Flipkart.com introduced the 'Flipkart Commerce Cloud,' a customized suite of AI-driven retail technology solutions for global retailers and e-commerce businesses. This extensive offering includes marketplace technology, retail media solutions, pricing, and inventory management features rigorously assessed by Flipkart.com. The company aims to equip international sellers with reliable and secure tools to enhance business expansion and efficiency within the competitive global market. , In August 2023, Shopify and Amazon.com, Inc. announced a strategic partnership that will allow Shopify merchants to seamlessly implement Amazon's "Buy with Prime" option on their sites. As a result of the agreement, Amazon.com, Inc. Prime customers will enjoy a more efficient checkout process on various platforms. This collaboration allows Amazon Prime members to utilize their existing Amazon payment options, while Shopify will handle the transaction processing through its system, showcasing a partnership between the two leading companies. , In February 2023, eBay acquired 3PM Shield, a developer of AI-powered online retail solutions. 3PM Shield uses machine learning and artificial intelligence to analyze extensive data sets, enhancing marketplace compliance and user experience. This acquisition aligns with eBay's goal to offer a "safe and reliable" platform by boosting its ability to block the sale of counterfeit and prohibited items. By incorporating 3PM Shield's sophisticated monitoring technologies, eBay seeks to enhance its capability to address problematic seller behavior and spot problematic listings, fostering a safer e-commerce space for its worldwide community of sellers and buyers. .

Downloadable Beauty & Grooming Dataset: Enhance product development & customer targeting. Dataset included data points like brand, price, description & more.

Unleash the Power of Beauty & Grooming Data to Explore trends and preferences.

Context

This dataset contains 6 months of Customer online orders. The data is simple but messy and unorganized. This for beginner and Intermediate level who want to improve there skills in Pandas, matplotlib, and seaborn.

Content

Dataset context columns like: crawl_timestamp, product_name, product_category_tree, retail_price, discounted_price, brand.

The main focus is to clean the dataset and make it organized using pandas.

Acknowledgements

I wouldn't be here without the help of data.world. Thank You.

Inspiration

I have some questions for this Dataset: 1. What was the best month for sales? How much was earned that month? 2. What time should we display advertisements to maximize the likelihood of purchases? 3. Which category sold most in that six month period? 4. Top 10 products sold most in that six month period?

Share of online sales during India's festive shopping season in 2020

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Flipkart Online Services Pvt Ltd Company Profile, Opportunities, Challenges and Risk (SWOT, PESTLE and Value Chain); Corporate and ESG Strategies; Competitive Intelligence; Financial KPI’s; Operational KPI’s; Recent Trends: “ Read More

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

Flipkart: The King of Indian E-Commerce

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

Flipkart Jewellery Products Dataset offers a comprehensive collection of detailed information on various jewellery items available on Flipkart, encompassing categories such as necklaces, earrings, rings, and bracelets. This dataset is meticulously designed to assist researchers, analysts, and developers in gaining valuable insights into pricing trends, product popularity, and consumer preferences within the online jewellery market.

For those seeking a broader dataset, the Flipkart E-commerce Dataset provides extensive information on over 5.7 million products, including product names, descriptions, prices, customer reviews, ratings, and images. This dataset serves as an invaluable resource for data analysis, machine learning projects, and in-depth market research

Key Attributes:

• Product ID: A unique identifier for each jewellery product.
• Product Name: The name of the jewellery item.
• Category: The category under which the jewellery product falls (e.g., necklaces, earrings).
• Subcategory: Additional granularity within the main category (e.g., diamond earrings, gold necklaces).
• Price: The listed price of the product.
• Discount: The discount offered on the product (if any).
• Final Price: The price after applying the discount.
• Customer Ratings: Average customer ratings of the product (on a scale of 1 to 5).
• Number of Reviews: The total number of customer reviews.
• Seller Name: The name of the seller offering the product.
• Stock Status: Availability status of the product (in stock, out of stock).
• Product URL: A link to the product page on Flipkart.

Potential Use Cases:

1. Market Analysis: Understand the pricing strategies and discount patterns for jewellery items on Flipkart.
2. Recommendation Systems: Develop personalized product recommendation systems based on customer ratings and reviews.
3. Price Comparison Tools: Build tools to compare jewellery prices across different sellers and platforms.
4. Consumer Behavior Studies: Analyze customer preferences and trends in jewellery purchases.
5. Sales Forecasting: Use historical data to predict future sales trends in the jewellery category.

Dataset Format:

The dataset is available in JSON, with each row representing a unique jewellery product and columns capturing various attributes mentioned above.

This dataset is an invaluable resource for anyone interested in exploring the dynamics of the online jewellery market in India, particularly on the Flipkart platform.

Flipkart Private Limited reported a revenue amounting to over *** billion Indian rupees in the financial year 2024. Additionally, the e-commerce player had an increase of ** percent in its revenue, compared to the previous year. Online marketplaces in India Along with a rising internet penetration rate in India, the number of digital buyers rose linearly. The rise of e-commerce the country goes hand in hand with the expansion of online marketplaces. Although marketplace shipping continues to be the most popular shipping method, direct-to-consumer shipments continued to grow in terms of share of e-commerce shipments. Flipkart as Amazon’s competitor As the global e-commerce player, Amazon ranks first in terms of net e-commerce sales, with Flipkart close behind. In 2018, American retail giant Walmart acquired the home-grown retailer for ** billion U.S. dollars and became a ** percent stakeholder. Flipkart’s product portfolio is widespread, standing out in the online sales of apparel with more than ** percent of buyers purchasing apparel on the Bengaluru-based player. Its continued growth led to plans of an initial public offering (IPO) in the United States by the end of 2021.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This was my second dataset ever published. It was basically to work on web scraping skills using scrapy. This was done on the online shopping website. The main motive of doing this is to help people work on their EDA skills and also the knack of solving basic regression problems. This is the dataset that gives you space to hone your data visualizations skills well.

Generated prompt data, Built with Llama 3.1 Data Preparation Raw Data

Flipkart's biggest sales event, the Big Billion Days sale, reported *** million visitors in 2024, a decrease in comparison to previous year. The festive sale offers major discounts on a wide variety of products, from electronics to fashion products. Furthermore, customers also receive cashback and bank offers to maximize their savings.

Dataset

This dataset was created by CandySniper

Contents

It contains the following files:

Certainly! Here's a summary for the dataset extracted from Flipkart's laptop section:

Dataset Summary:

• Source: Flipkart's laptop section
• Number of Rows: 456
• Number of Columns: 4
• Columns:
  1. Name: The name or model of the laptop.
  2. Before Price: The original price of the laptop before any discount, in ₹ (Indian Rupees).
  3. After Price: The discounted price of the laptop, in ₹.
  4. Other Details: Additional details about the laptop such as RAM, storage, processor, etc.

Data Integrity:

• The dataset has been extracted from Flipkart's website and may be subject to changes in the website's structure or data format.
• Care has been taken to ensure data accuracy, but discrepancies may occur due to factors such as website updates or data extraction methods.

Note:

• The dataset provides valuable information for analyzing laptop prices and specifications available on Flipkart's platform.
• It can be used for various purposes, including price comparison, trend analysis, and market research in the laptop industry.

Data Preprocessing Raw Data