Delve into Comprehensive WebAutomation Dataset: Amazon Best Seller Products with Global Coverage

Explore the depths of eCommerce with our expansive WebAutomation dataset, meticulously curated to provide a comprehensive overview of Amazon's best seller products. With global coverage and an extensive array of data points, including pricing data, eCommerce product details, and seller ratings, our dataset empowers businesses and researchers to extract actionable insights and drive informed decision-making.

What Sets Us Apart:

• Global Coverage: Our dataset spans across various regions and countries, offering insights into Amazon's best seller products on a global scale. Whether you're interested in market trends in North America, Europe, Asia, or beyond, our dataset has you covered.

• Rich Pricing Data: Dive into detailed pricing information for a wide range of products, enabling precise analysis of pricing strategies, competitive landscapes, and market trends. With historical pricing data, track changes over time and identify patterns to inform pricing decisions.

• Comprehensive Product Details: Gain access to a wealth of eCommerce product details, including product descriptions, specifications, images, and customer reviews. Whether you're conducting market research, competitor analysis, or product development, our dataset provides the depth of information needed to make informed decisions.

• Seller Ratings Data: Understand the reputation and performance of sellers on Amazon with our seller ratings data. Evaluate seller reliability, customer satisfaction levels, and overall trustworthiness to guide partnership decisions and enhance the customer experience.

  Use Cases:

• Market Analysis: Analyze market trends, consumer preferences, and competitive landscapes to identify growth opportunities and strategic advantages.

• Price Optimization: Utilize pricing data and historical trends to optimize pricing strategies, maximize profitability, and stay competitive in the market.

• Product Development: Inform product development efforts by leveraging comprehensive product details and customer feedback to identify gaps in the market and tailor offerings to meet customer needs.

• Partnership Evaluation: Evaluate seller ratings and performance metrics to make informed decisions when selecting partners and suppliers, ensuring a seamless and trustworthy customer experience.

  Unlock the Power of Data:

Empower your business with actionable insights derived from our WebAutomation dataset. Whether you're a market researcher, business analyst, or eCommerce professional, our dataset provides the tools and resources needed to stay ahead in today's dynamic marketplace.

Access comprehensive Naver Shopping dataset containing millions of Korean product listings, pricing history, and seller analytics. Our dataset covers South Korea's entire e-commerce landscape from K-beauty to electronics, updated daily. Leverage historical pricing data, seasonal trends, and consumer behavior patterns unique to Korean online shopping. Includes Smart Store rankings, Naver Pay adoption rates, and cross-category purchase correlations. Pre-processed data with Korean-English translations, normalized categories, and enriched seller metrics. Perfect for market research, competitive analysis, and understanding Korean consumer preferences.

Comprehensive Amazon (AMZN) Stock Price Dataset: 1997-2025**

This meticulously curated dataset contains 29 years of historical Amazon (AMZN) stock market data, spanning from May 15, 1997, to December 31, 2025. With over 7,200 trading days of OHLCV (Open, High, Low, Close, Volume) data and engineered technical indicators, this dataset is perfect for:

• 📊 Time Series Analysis & Forecasting
• 🤖 Machine Learning Price Prediction Models
• 📈 Technical Analysis & Trading Strategy Development
• 🎓 Financial Data Science Education
• 💹 Stock Market Trend Analysis

Dataset Features

Core Price Data:

• Daily Open, High, Low, Close prices
• Trading Volume
• Price changes and daily returns
• Price range (High-Low spread)

Technical Indicators:

• Moving Averages (7-day, 30-day, 90-day)
• 7-day Volatility metrics
• Cumulative returns from inception

Temporal Features:

• Year, Quarter, Month breakdown
• Day of week analysis
• Date-based aggregations

Data Source & Provenance

Primary Source: Yahoo Finance
Collection Tool: yfinance Python library (official Yahoo Finance API wrapper)
Ticker Symbol: AMZN (Amazon.com Inc.)
Exchange: NASDAQ

Collection Methodology: 1. Historical stock data retrieved via yfinance API 2. Data covers all trading days from 1997-05-15 to 2025-12-31 3. Includes adjusted closing prices and split-adjusted values 4. Raw OHLCV data enhanced with calculated technical indicators 5. Data cleaned and validated for missing values 6. Beginner-friendly column naming and structure

Transformations Applied: - Removed duplicate ticker rows from raw yfinance output - Converted date strings to datetime format - Calculated daily returns: (Close - Open) / Open * 100 - Added moving averages using rolling window calculations - Computed volatility using 7-day rolling standard deviation - Generated cumulative returns relative to first trading day - Added temporal features (year, month, quarter, day of week)

Dataset Statistics

Use Cases

1. Stock Price Prediction: Build LSTM, Prophet, ARIMA, or XGBoost models
2. Technical Analysis: Backtest trading strategies with moving averages
3. Volatility Analysis: Study market behavior during economic events
4. Educational Projects: Perfect for learning data science & finance
5. Portfolio Optimization: Analyze Amazon's long-term performance
6. Market Research: Study e-commerce giant's stock evolution

File Format

• Format: CSV (Comma-Separated Values)
• Encoding: UTF-8
• Size: ~700 KB
• Delimiter: Comma (,)
• Missing Values: Minimal (NaN in early MA/volatility due to window calculations)

Citation

If you use this dataset in your research or projects, please cite:

Amazon Stock Price Dataset (1997-2025)
Author: Muhammad Ibrahim Shahrukh
Source: Yahoo Finance via yfinance
Date Collected: 2025
URL: [Your Kaggle Dataset URL]

License Recommendation

CC BY-SA 4.0 (Creative Commons Attribution-ShareAlike 4.0)
Allows for commercial and non-commercial use with attribution

snapshot-tab-pane AI In E-Commerce Market Size 2025-2029The AI in e-commerce market size is valued to increase by USD 26.79 billion, at a CAGR of 34.7% from 2024 to 2029. Imperative for hyper-personalization and enhanced customer experience will drive the ai in e-commerce market.Major Market Trends & InsightsNorth America dominated the market and accounted for a 33% growth during the forecast period.By Component - Software segment was valued at USD 2 billion in 2023By Technology - Machine learning segment accounted for the largest market revenue share in 2023Market Size & ForecastMarket Opportunities: USD 1.00 millionMarket Future Opportunities: USD 26793.90 millionCAGR from 2024 to 2029 : 34.7%Market SummaryIn the dynamic realm of e-commerce, Artificial Intelligence (AI) has emerged as a game-changer, revolutionizing business operations and customer experiences. According to recent market intelligence, the global AI in retail market is projected to reach a value of USD7.36 billion by 2027, underscoring its significant impact. AI's integration into e-commerce is driven by several factors. First, it enables hyper-personalization, catering to consumers' unique preferences and needs. Second, generative AI streamlines content and commerce management, ensuring seamless and efficient processes. However, challenges persist, including data privacy concerns and evolving regulatory compliance. AI's influence extends beyond customer-facing applications.It powers supply chain optimization, predictive analytics, and inventory management, enhancing overall business agility. Despite these advancements, e-commerce businesses must navigate the complexities of AI implementation, ensuring they maintain a human-like customer experience and ethical data handling practices. As AI continues to evolve, it will further transform e-commerce, offering new opportunities for innovation and growth. From intelligent product recommendations to virtual shopping assistants, the possibilities are endless. However, businesses must remain vigilant, adapting to the ever-changing AI landscape and its implications for their operations and customer interactions.What will be the Size of the AI In E-Commerce Market during the forecast period?Get Key Insights on Market Forecast (PDF) Request Free SampleHow is the AI In E-Commerce Market Segmented ?The AI in e-commerce industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.Component SoftwareServicesTechnology Machine learningNLPComputer visionSpeech recognitionOthersApplication Product recommendationsCustomer supportInventory managementDynamic pricingOthersGeography North America USCanadaEurope FranceGermanyItalyUKAPAC ChinaIndiaJapanSouth America BrazilRest of World (ROW) By Component InsightsThe software segment is estimated to witness significant growth during the forecast period.The market continues to evolve, with software solutions increasingly shaping the digital retail landscape. These solutions, encompassing customer segmentation, marketing automation, and supply chain optimization, leverage advanced technologies like deep learning algorithms, machine learning models, and natural language processing. They offer capabilities such as conversational commerce, predictive analytics, and targeted advertising, enhancing customer experiences and streamlining operations. Notably, 75% of online retailers currently use AI for personalized shopping recommendations. Cloud-based platforms and API integrations facilitate scalability and real-time analytics, while computer vision systems and sentiment analysis tools enable image recognition and emotion detection.Ethical guidelines and data security protocols ensure trust and privacy. Overall, AI-powered e-commerce solutions optimize pricing, sales forecasting, website performance, and inventory management, offering a competitive edge in the ever-evolving digital marketplace.Request Free SampleThe Software segment was valued at USD 2 billion in 2019 and showed a gradual increase during the forecast period.Request Free SampleRegional AnalysisNorth America is estimated to contribute 33% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.See How AI In E-Commerce Market Demand is Rising in North America Request Free SampleThe market is witnessing significant advancements, particularly in North America, where the United States and Canada lead in technological adoption and consumer digital literacy. With broadband access ubiquitous and online shopping deeply ingrained in the cultur

snapshot-tab-pane Subscription E-commerce Platform Market Size 2026-2030The subscription e-commerce platform market size is valued to increase by USD 196.23 billion, at a CAGR of 19.5% from 2025 to 2030. Convenience and personalization provided by subscription services will drive the subscription e-commerce platform market.Major Market Trends & InsightsNorth America dominated the market and accounted for a 46.3% growth during the forecast period.By Type - Offline segment was valued at USD 51.66 billion in 2024By Application - Beauty and personal care segment accounted for the largest market revenue share in 2024Market Size & ForecastMarket Opportunities: USD 318.68 billionMarket Future Opportunities: USD 196.23 billionCAGR from 2025 to 2030 : 19.5%Market SummaryThe subscription e-commerce platform market is characterized by rapid technological evolution, driven by consumer demand for convenience and personalization. This digital commerce model, built on long-term customer relationships and predictable revenue streams, moves beyond simple transactions. Success hinges on robust platform capabilities, including automated billing, flexible delivery scheduling, and insightful data analytics.For instance, a direct-to-consumer company offering curated food boxes leverages its platform’s inventory management system to synchronize with its supply chain in real time, minimizing waste and ensuring product freshness. This level of operational efficiency is critical. However, the landscape is not without challenges. High customer acquisition costs and the constant threat of subscriber churn force businesses to innovate continuously.They must offer unique value, from exclusive products to seamless user experiences, to foster loyalty. As the market matures, the focus shifts from pure growth to sustainable retention, demanding more sophisticated tools for churn reduction analytics and customer lifecycle management. Platforms that master this balance are best positioned for long-term success.What will be the Size of the Subscription E-commerce Platform Market during the forecast period? Get Key Insights on Market Forecast (PDF) Request Free SampleHow is the Subscription E-commerce Platform Market Segmented?The subscription e-commerce platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2026-2030, as well as historical data from 2020-2024 for the following segments.TypeOfflineOnlineApplicationBeauty and personal careFood and beveragesClothing and fashionEntertainmentHealth and fitnessPlatformVertical-specific DTCMulti-category marketplacesGeographyNorth AmericaUSCanadaMexicoEuropeUKGermanyFranceAPACChinaJapanIndiaSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaSaudi ArabiaUAESouth AfricaRest of World (ROW)By Type InsightsThe offline segment is estimated to witness significant growth during the forecast period.The segment for physical goods subscription models is defined by the complex interplay between digital management and logistics.This recurring revenue business model hinges on sophisticated e-commerce platform features, including advanced fulfillment integration and real-time inventory synchronization with a core order management system.Success in this area is increasingly dependent on seamless third-party logistics API connections, which have been shown to improve fulfillment accuracy by over 15%. As subscriber acquisition cost rises, retaining customers through reliable delivery is paramount.Platforms providing a real-time analytics dashboard for MRR tracking and supporting digital wallet integration offer a competitive edge.Ultimately, the choice of subscription billing software and the adoption of sustainable subscription practices are critical for long-term viability in this competitive space. Request Free SampleThe Offline segment was valued at USD 51.66 billion in 2024 and showed a gradual increase during the forecast period. Request Free SampleRegional AnalysisNorth America is estimated to contribute 46.3% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. See How Subscription E-commerce Platform Market Demand is Rising in North America Request Free SampleThe geographic landscape of the subscription e-commerce platform market is defined by varying regional maturity and adoption rates.North America accounts for over 46% of the industry's incremental growth, driven by high consumer demand and advanced digital infrastructure supporting complex B2B subscription models.In this region, platforms offering sophisticated tax compliance automation and customer retent

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Multi‑Carrier Rate Shopping APIs Market Outlook

According to our latest research, the global Multi-Carrier Rate Shopping APIs market size in 2024 stands at USD 1.21 billion, reflecting robust demand for real-time shipping and logistics optimization solutions. With a CAGR of 14.7% anticipated from 2025 to 2033, the market is projected to reach USD 3.78 billion by 2033. This impressive growth is primarily driven by the surge in e-commerce activities, the increasing complexity of global supply chains, and the need for seamless integration of shipping rate comparison tools across diverse platforms. As per our latest research, the growing adoption of automation and digitalization in logistics is a significant catalyst propelling the Multi-Carrier Rate Shopping APIs market forward.

The accelerating growth of the Multi-Carrier Rate Shopping APIs market is deeply rooted in the exponential rise of e-commerce worldwide. Online retailers and marketplaces are under constant pressure to provide competitive shipping rates, transparent delivery timelines, and seamless customer experiences. Multi-Carrier Rate Shopping APIs empower businesses to compare real-time shipping rates from multiple carriers, enabling them to select the most cost-effective and reliable shipping options for every transaction. This capability not only enhances customer satisfaction but also allows retailers to optimize their shipping expenses and streamline their operations. The proliferation of omnichannel retailing, where customers expect consistent shipping experiences across physical and digital touchpoints, further amplifies the demand for these APIs. As businesses scale globally, the complexity of managing multiple carriers and delivery partners grows, making rate shopping APIs an indispensable part of the modern logistics toolkit.

Another major growth driver for the Multi-Carrier Rate Shopping APIs market is the increasing adoption of advanced technologies within the logistics and transportation sectors. The integration of artificial intelligence, machine learning, and big data analytics into API solutions is enabling more sophisticated rate comparison, predictive analytics, and dynamic routing. These technological advancements allow businesses to anticipate shipping delays, optimize carrier selection based on historical performance, and reduce operational inefficiencies. Furthermore, the growing trend of cloud-based deployment is making these APIs more accessible to small and medium enterprises (SMEs), which historically faced barriers to entry due to high upfront costs and complex IT requirements. By leveraging scalable and cost-effective cloud solutions, businesses of all sizes can now access real-time carrier rates, automate shipping processes, and improve their overall logistics performance.

Additionally, regulatory changes and the evolving landscape of international trade are shaping the Multi-Carrier Rate Shopping APIs market. With the rise of cross-border e-commerce, businesses are increasingly required to navigate complex customs regulations, varying tax structures, and diverse shipping standards. Multi-Carrier Rate Shopping APIs help companies remain compliant by providing updated information on shipping restrictions, customs documentation, and duties for different regions. This not only reduces the risk of shipment delays and penalties but also enhances transparency for end consumers. The ability to seamlessly integrate these APIs with enterprise resource planning (ERP) and order management systems is further accelerating adoption across industries such as retail, manufacturing, and third-party logistics (3PL).

From a regional perspective, North America continues to dominate the Multi-Carrier Rate Shopping APIs market, accounting for the largest share in 2024. The region’s advanced e-commerce infrastructure, high penetration of digital logistics solutions, and presence of leading technology providers create a fertile environment for market growth. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, expanding e-commerce markets in China and India, and increasing investments in logistics modernization. Europe also demonstrates significant market potential, particularly in countries with robust cross-border trade and logistics networks, such as Germany, the UK, and France. Latin America and the Middle East & Africa are witnessing steady growth, supported by rising e-commerce adoption and efforts to modernize supply chain operations.
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The Lazada dataset contains comprehensive product listing records, customer reviews, and seller metrics from one of Southeast Asia's top online marketplaces. This dataset is ideal for historical analysis, building ML models, and enriching your own e-commerce databases with structured insights from Lazada.

About this Dataset

This dataset offers a comprehensive, up-to-date look at the historical stock performance of Amazon.com Inc. (AMZN), one of the world's most influential technology and e-commerce companies.

About the Company

Amazon.com Inc. is an American multinational technology company founded in 1994 by Jeff Bezos. It is best known for its e-commerce, cloud computing (Amazon Web Services), digital streaming, and artificial intelligence ventures. Headquartered in Seattle, Washington, Amazon has grown to be a global leader in online retail and is a key component of the S&P 500, making its stock performance a significant indicator of consumer spending and technology sector trends.

Key Features

Daily OHLCV Data: The dataset contains essential Open, High, Low, Close, and Volume metrics for each trading day.

Comprehensive History: Includes data from Amazon's early trading history to the present, offering a long-term perspective.

High-Quality Data: The data is clean and sourced from a reliable financial API, ideal for direct use in analysis and modeling.

Regular Updates: The dataset is designed for regular, automated updates to ensure data freshness for time-sensitive projects.

Data Dictionary

Date: The date of the trading session in YYYY-MM-DD format.

ticker: The standard ticker symbol for Amazon.com Inc. on the NASDAQ exchange: 'AMZN'.

name: The full name of the company: 'Amazon.com Inc.'.

Open: The stock price in USD at the start of the trading session.

High: The highest price reached during the trading day in USD.

Low: The lowest price recorded during the trading day in USD.

Close: The final stock price at market close in USD.

Volume: The total number of shares traded on that day.

Data Collection

The data for this dataset is collected using the yfinance Python library, which pulls information directly from the Yahoo Finance API.

Potential Use Cases

Financial Analysis: Analyze historical price trends, volatility, and trading volume of Amazon stock.

Machine Learning: Develop and test models for stock price prediction and time series forecasting.

Educational Projects: A perfect real-world dataset for students and data enthusiasts to practice data cleaning, visualization, and modeling.

global B2B cross border payments market size was $31.85 trillion in 2024 and is grow to $55.45 trillion by 2034, a CAGR of 5.70% between 2025 and 2034.

This dataset offers a comprehensive, up-to-date look at the historical stock performance of PayPal Holdings, Inc. (PYPL), an American multinational financial technology company operating an online payments system.

About the Company

PayPal Holdings, Inc. operates a worldwide online payments system that supports online money transfers and serves as an electronic alternative to traditional paper methods like checks and money orders. The company originated as a key part of eBay, from which it spun off in 2015. Headquartered in San Jose, California, PayPal provides a platform for both consumers and merchants, facilitating e-commerce by allowing payments to be made and received globally. Its services also include brands like Venmo, Xoom, and Braintree.

Key Features

Daily OHLCV Data: The dataset contains essential Open, High, Low, Close, and Volume metrics for each trading day.

Comprehensive History: Includes data from PayPal's early trading history to the present, offering a long-term perspective.

Regular Updates: The dataset is designed for regular, automated updates to ensure data freshness for time-sensitive projects.

Data Dictionary

Date: The date of the trading session in YYYY-MM-DD format.

ticker: The standard ticker symbol for PayPal Holdings, Inc. on the NASDAQ: 'PYPL'.

name: The full name of the company: 'PayPal Holdings, Inc.'.

Open: The stock price in USD at the start of the trading session.

High: The highest price reached during the trading day in USD.

Low: The lowest price recorded during the trading day in USD.

Close: The final stock price at market close in USD.

Volume: The total number of shares traded on that day.

Data Collection

The data for this dataset is collected using the yfinance Python library, which pulls information directly from the Yahoo Finance API.

Potential Use Cases

Financial Analysis: Analyze historical price trends, volatility, and trading volume of PayPal stock.

Machine Learning: Develop and test models for stock price prediction and time series forecasting.

Educational Projects: A perfect real-world dataset for students and data enthusiasts to practice data cleaning, visualization, and modeling.

[218+ Pages Report] The global bot security market size is expected to grow from USD 611.2 million in 2023 to USD 2,991.80 million by 2032, at a CAGR of 19.30% from 2024-2032

Global Bot Security Market valued at USD 589.87 Million in 2023 and is predicted to reach USD 3098.84 Million by the end of 2032, with a CAGR of 20.24%.

Der globale Markt für grenzüberschreitende B2B-Zahlungen hatte im Jahr 2024 ein Volumen von 31.85 Billionen US-Dollar und wird bis 2034 auf 55.45 Billionen US-Dollar anwachsen, was einer durchschnittlichen jährlichen Wachstumsrate (CAGR) von 5.70 % zwischen 2025 und 2034 entspricht.

[222+ Pages Report] The India payment gateway market size is expected to grow from USD 1,632.7 million in 2023 to USD 4,012.3 million by 2032, at a CAGR of 10.5% from 2024-2032