Prada Fashion Product Dataset – Analyze Products, Pricing

Our Prada Fashion Product Dataset provides detailed insights into luxury fashion items, including pricing, materials, colors, categories, and seasonal trends. Brands, analysts, and researchers can use the Prada Product Dataset to study market positioning, compare premium collections, and understand evolving consumer preferences within the high-end fashion industry.

Our eCommerce Product Dataset helps businesses analyze product performance, pricing variations, catalog structures, and competitive trends across online marketplaces. By leveraging an eCommerce Dataset, companies can improve assortment planning, enhance product recommendations, optimize pricing strategies, and make data-driven decisions to strengthen their digital retail operations and customer experiences.

Prada Fashion Product Dataset Banner

Top Countries:

USA UK Germany Japan India France Canada Australia UK Germany Japan India France Canada Australia UK Germany Japan India France Canada Australia USA UK Germany Japan India France Canada Australia UK Germany Japan India France Canada Australia UK Germany Japan India France Canada Australia

Create your own

B2B-B2C-Marketplace-amazon
B2B-B2C-Marketplace-IndiaMART
B2C-Marketplace-Amazon
B2C-Marketplace-Flipkart
D2C-Marketplace-Nykaa
D2C-Marketplace-Walmar
Electronic-D2C-Apple
Electronic-D2C-boAt
Fashion-Marketplace-Farfetch
Fashion-Marketplace-Myntra
FMCG-Marketplace-Boxed
FMCG-Marketplace-Udaan
Food-Delivery-Swiggy
Food-Delivery-Uber-Eats
Quick Commerce-Blinkit
Quick Commerce-GoPuff
Social-Commerce-Meesho
Social-Commerce-Poshmark
Taxi-Aggregator
Taxi-Aggregator-Uber

Why Choose Prada Fashion Datasets from Actowiz Solutions?

Comprehensive Product Listings

Comprehensive Product Listings

Get complete Prada Fashion product coverage, including product name, category, SKU, material details, size options, sports segment, and gender classification.

Pricing Discounts & Stock Insights

Pricing, Discounts & Stock Insights

Access real-time Prada Fashion data for MRP, selling price, seasonal offers, limited-time discounts, and stock availability across regions.

Product Variants & Attributes

Product Variants & Attributes

Capture all variants — color options, sizes, fit types, high-resolution images, and product specifications.

Customer Ratings & Reviews

Customer Ratings & Reviews

Analyze real customer feedback, product ratings, purchase drivers, quality issues, and trend adoption.

Real-Time & Historical Data Feeds

Real-Time & Historical Data Feeds

Receive hourly, daily, weekly, or monthly Prada Fashion datasets to track product changes, price fluctuations, and demand patterns.

Multiple Delivery Formats

Multiple Delivery Formats

Get your datasets in CSV, Excel, JSON, or integrate directly using the Prada Fashion Scraping API for automated workflows.

Prada Fashion Product Availability and Pricing Dataset

Gain instant visibility into Prada Fashion latest product and pricing trends.

The Prada Clothing & Accessories Dataset provides comprehensive insights into Prada's latest collections, covering clothing, handbags, shoes, and accessories. Fashion analysts, eCommerce platforms, and luxury retail brands can leverage this dataset to understand pricing, styles, seasonal trends, and consumer preferences. By combining historical and current product data, the Prada Fashion Product Dataset allows businesses to benchmark collections, analyze market positioning, and identify emerging trends in high-end fashion. This dataset is essential for brands aiming to make data-driven decisions, optimize product launches, and enhance competitive intelligence in the luxury segment.

Data Insights Include:

  • Detailed product information: materials, colors, sizes, and categories
  • Pricing history and trend analysis
  • Seasonal and collection-based insights
  • Competitive benchmarking capabilities
  • Supports eCommerce and retail analytics

Unlock the full potential of Prada collections with the Prada Clothing & Accessories Dataset for smarter fashion insights today!

Sample Prada Dataset Preview

S.No Product Name Category Sub-Category Brand Model/Style Color Size Material Gender Price (USD) Discount (%) Final Price (USD) Stock Status SKU Rating Review Count Release Year Season Country of Origin Supplier Weight (kg) Dimensions (cm) Product Description Features Care Instructions Warranty
1 Prada Leather Tote Handbags Tote Prada Cervo Leather Tote Black L Leather Women 2500 10 2250 In Stock SKU001 4.8 120 2023 Summer Italy Prada Supplier 1.2 35x25x15 Elegant leather tote with Prada logo Durable, Stylish Clean with soft cloth 2 years
2 Prada Cloudbust Sneakers Footwear Sneakers Prada Cloudbust White 9 Fabric/Leather Men 650 15 552.5 In Stock SKU002 4.6 85 2024 Spring Italy Prada Supplier 0.8 30x20x10 Modern sneakers with comfort sole Lightweight, Breathable Hand wash only 1 year
3 Prada PR 17WS Sunglasses Accessories Sunglasses Prada PR 17WS Black N/A Plastic/Metal Unisex 420 5 399 In Stock SKU003 4.9 45 2023 Summer Italy Prada Supplier 0.3 15x5x5 Stylish sunglasses with UV protection Scratch-resistant, Lightweight Wipe with microfiber 6 months
4 Prada Saffiano Wallet Accessories Wallets Prada Saffiano Compact Red N/A Leather Women 480 8 441.6 In Stock SKU004 4.7 60 2022 Winter Italy Prada Supplier 0.25 12x9x2 Compact wallet with multiple card slots Durable, Elegant Clean with soft cloth 1 year
5 Prada Cahier Shoulder Bag Handbags Shoulder Bags Prada Cahier Beige M Leather/Metal Women 3200 12 2816 Out of Stock SKU005 4.9 95 2023 Fall Italy Prada Supplier 1.1 32x22x14 Elegant shoulder bag with metal details Stylish, Durable Wipe with microfiber 2 years
6 Prada Re-Nylon Backpack Bags Backpacks Prada Re-Nylon Blue L Nylon Unisex 1800 10 1620 In Stock SKU006 4.8 110 2024 Summer Italy Prada Supplier 0.9 40x30x18 Eco-friendly nylon backpack Lightweight, Durable Spot clean 1 year
7 Prada Linea Rossa Cap Accessories Caps Prada Linea Rossa Red M Cotton Men 210 5 199.5 In Stock SKU007 4.5 35 2023 Summer Italy Prada Supplier 0.15 20x15x10 Stylish cotton cap with logo Breathable, Comfortable Hand wash only 6 months
8 Prada Cloudbust Thunder Sneakers Footwear Sneakers Prada Cloudbust Thunder Black/White 10 Fabric/Leather Men 700 10 630 In Stock SKU008 4.7 50 2024 Spring Italy Prada Supplier 0.85 31x21x11 High-performance sneakers with cushioned sole Lightweight, Breathable Hand wash only 1 year
9 Prada Diagramme Crossbody Bag Handbags Crossbody Prada Diagramme Pink S Leather Women 2100 15 1785 In Stock SKU009 4.8 70 2023 Summer Italy Prada Supplier 0.95 28x18x10 Compact crossbody bag with quilted design Stylish, Elegant Wipe with microfiber 2 years
10 Prada Tessuto Sneakers Footwear Sneakers Prada Tessuto Grey 8 Fabric Unisex 620 10 558 In Stock SKU010 4.6 40 2022 Fall Italy Prada Supplier 0.8 30x20x10 Lightweight sneakers with modern design Comfortable, Durable Hand wash only 1 year
Buy Now

Prada Fashion Product Dataset Categories

Core Fields

Prada Luxury Fashion Dataset

Comprehensive collection of Prada's luxury items, styles, trends, and categories.

Prada Apparel & Footwear Dataset

Detailed product info, sizes, colors, materials, and seasonal fashion collections.

Prada Bags & Leather Goods Dataset

Catalog of handbags, wallets, belts, and leather accessories with details.

Prada Pricing & Discount Dataset

Historical and current pricing, discounts, offers, and seasonal price variations.

Prada Retail Store Location Dataset

Global Prada store addresses, geolocation, city, country, and contact details.

Prada Real-Time Product Dataset

Live updates of Prada products, availability, stock, pricing, and trends.

Quality and normalization

Geo & Marketplace Coverage

Actowiz offers region-specific Prada Fashion Product datasets, ensuring you get accurate and localized data

North America:

Europe:

Asia:

Middle East & Africa:

Each regional dataset can be purchased separately or bundled.

Geo Coverage

North America:

  • United States
  • Canada
  • Mexico
Buy

Use Cases of Prada Fashion Product Datasets

For Retailers & Brands For Retailers & Brands

Product assortment planning

Competitive price benchmarking

Seasonal trend analysis

For Market Analysts For Market Analysts

Consumer preference insights

Market share tracking

Sales trend evaluation

For Investors & Consultants For Investors & Consultants

Investment opportunity analysis

Brand performance review

Market growth forecasting

For AI & ML Teams For AI & ML Teams

Predictive demand modeling

Recommendation system training

Pricing optimization algorithms

Benefits of Prada Fashion Datasets

Faster Market Insights

Gain instant visibility into Prada Fashion product listings, pricing updates, new arrivals, and stock changes with real-time & historical datasets.

Accurate Trend Forecasting

Use structured Prada Fashion data to predict demand, analyze customer buying patterns, and understand fast-changing fashion and sports trends.

Competitive Edge

Benchmark Prada Fashion prices, discounts, promotions, and inventory availability to stay ahead in the sportswear and retail market.

Custom Data Feeds

Access only the Prada Fashion data your business needs - fully filtered by product category, color, size, region, or sales performance.

Pricing & Subscription Models

We offer flexible plans to fit every business need:

One-Time Dataset Purchase

Best for research projects.

File formats

CSV, JSON, Parquet.

Monthly Subscription

Regularly updated datasets delivered automatically.

Enterprise License

Access all Prada regions with unlimited API calls.

Frequently Asked Questions

The Prada Fashion Product Dataset is a structured collection of Prada's luxury items, including clothing, footwear, accessories, bags, and pricing information. It helps businesses, analysts, and brands gain insights into product trends, market positioning, and consumer preferences.
Retailers, eCommerce platforms, fashion analysts, investors, and AI/ML teams can leverage the Prada Clothing & Accessories Dataset to track product trends, analyze pricing, optimize inventory, and enhance competitive intelligence in the luxury fashion industry.
The dataset includes detailed product attributes like category, material, color, size, pricing, seasonal collection, availability, and discounts. It provides a holistic view of Prada's offerings for analysis, benchmarking, and decision-making.
Yes, the Prada Pricing & Discount Dataset provides historical and real-time price data, helping businesses monitor competitor pricing, optimize discounts, and design strategic pricing models for products across locations and collections.
Yes, the Prada Retail Store Location Dataset contains geolocation, addresses, city, country, and contact details for Prada stores worldwide, allowing businesses to analyze retail footprint and market coverage.
The Prada Real-Time Product Data provides live updates on product availability, new arrivals, pricing, and inventory changes, ensuring businesses and analysts always have access to accurate and current information.
Investors and consultants can leverage the datasets to assess brand performance, track product trends, forecast market growth, and identify profitable opportunities within the luxury fashion sector.
Yes, AI and ML teams can utilize product attributes, pricing, and trend data for predictive modeling, recommendation engines, demand forecasting, and pricing optimization algorithms.
Actowiz Solutions provides region-specific coverage across North America (USA, Canada, Mexico), Europe (UK, France, Italy), Asia (China, Japan, India), and the Middle East & Africa (UAE, Saudi Arabia, South Africa).
You can access the datasets through Actowiz Solutions by contacting their team. They provide structured, reliable, and localized datasets suitable for analytics, market research, and AI applications.
Social Proof That Converts

Trusted by Global Leaders Across Q-Commerce, Travel, Retail, and FoodTech

Our web scraping expertise is relied on by 4,000+ global enterprises including Zomato, Tata Consumer, Subway, and Expedia — helping them turn web data into growth.

4,000+ Enterprises Worldwide
50+ Countries Served
20+ Industries
Join 4,000+ companies growing with Actowiz →
Real Results from Real Clients

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

1 min
★★★★★
"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"
TG
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
2 min
★★★★★
"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."
II
Iulen Ibanez
CEO / Datacy.es
1:30
★★★★★
"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."
FC
Febbin Chacko
-Fin, Small Business Owner
4.8/5 Average Rating
📹 50+ Video Testimonials
🔄 92% Client Retention
🌍 50+ Countries Served

Join 4,000+ Companies Growing with Actowiz

From Zomato to Expedia — see why global leaders trust us with their data.

Why Global Leaders Trust Actowiz

Backed by automation, data volume, and enterprise-grade scale — we help businesses from startups to Fortune 500s extract competitive insights across the USA, UK, UAE, and beyond.

icons
7+
Years of Experience
Proven track record delivering enterprise-grade web scraping and data intelligence solutions.
icons
4,000+
Projects Delivered
Serving startups to Fortune 500 companies across 50+ countries worldwide.
icons
200+
In-House Experts
Dedicated engineers across scrapers, AI/ML models, APIs, and data quality assurance.
icons
9.2M
Automated Workflows
Running weekly across eCommerce, Quick Commerce, Travel, Real Estate, and Food industries.
icons
270+ TB
Data Transferred
Real-time and batch data scraping at massive scale, across industries globally.
icons
380M+
Pages Crawled Weekly
Scaled infrastructure for comprehensive global data coverage with 99% accuracy.

AI Solutions Engineered
for Your Needs

LLM-Powered Attribute Extraction: High-precision product matching using large language models for accurate data classification.
Advanced Computer Vision: Fine-grained object detection for precise product classification using text and image embeddings.
GPT-Based Analytics Layer: Natural language query-based reporting and visualization for business intelligence.
Human-in-the-Loop AI: Continuous feedback loop to improve AI model accuracy over time.
🎯 Product Matching 🏷️ Attribute Tagging 📝 Content Optimization 💬 Sentiment Analysis 📊 Prompt-Based Reporting

Connect the Dots Across
Your Retail Ecosystem

We partner with agencies, system integrators, and technology platforms to deliver end-to-end solutions across the retail and digital shelf ecosystem.

icons
Analytics Services
icons
Ad Tech
icons
Price Optimization
icons
Business Consulting
icons
System Integration
icons
Market Research
Become a Partner →

Popular Datasets — Ready to Download

Browse All Datasets →
icons
Amazon
eCommerce
Free 100 rows
icons
Zillow
Real Estate
Free 100 rows
icons
DoorDash
Food Delivery
Free 100 rows
icons
Walmart
Retail
Free 100 rows
icons
Booking.com
Travel
Free 100 rows
icons
Indeed
Jobs
Free 100 rows

Latest Insights & Resources

View All Resources →
thumb
Blog

Food Inflation Tracking: Using Web Scraping for Real-Time Price Index Data

How retailers, governments, and analysts use web scraping to build real-time food inflation indexes. Track grocery price trends across 200+ supermarkets.

thumb
Case Study

UK DTC Brand Detects 800+ MAP Violations in First Month

How a $50M+ consumer electronics brand used Actowiz MAP monitoring to detect 800+ violations in 30 days, achieving 92% resolution rate and improving retailer satisfaction by 40%.

thumb
Report

Track UK Grocery Products Daily Using Automated Data Scraping to Monitor 50,000+ UK Grocery Products from Morrisons, Asda, Tesco, Sainsbury’s, Iceland, Co-op, Waitrose, Ocado

Track UK Grocery Products Daily Using Automated Data Scraping across Morrisons, Asda, Tesco, Sainsbury’s, Iceland, Co-op, Waitrose, and Ocado for insights.

Start Where It Makes Sense for You

Whether you're a startup or a Fortune 500 — we have the right plan for your data needs.

icons
Enterprise
Book a Strategy Call
Custom solutions, dedicated support, volume pricing for large-scale needs.
icons
Growing Brand
Get Free Sample Data
Try before you buy — 500 rows of real data, delivered in 2 hours. No strings.
icons
Just Exploring
View Plans & Pricing
Transparent plans from $500/mo. Find the right fit for your budget and scale.
GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [continent] => Array
                (
                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

            [country] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [location] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [postal] => Array
                (
                    [code] => 43215
                )

            [registered_country] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [subdivisions] => Array
                (
                    [0] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                )

            [traits] => Array
                (
                    [ip_address] => 216.73.216.153
                    [prefix_len] => 22
                )

        )

    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => geonameId
                    [2] => names
                )

        )

    [country:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

    [locales:protected] => Array
        (
            [0] => en
        )

    [maxmind:protected] => GeoIp2\Record\MaxMind Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [validAttributes:protected] => Array
                (
                    [0] => queriesRemaining
                )

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
                )

        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.153
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
                (
                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
                    [5] => isAnonymous
                    [6] => isAnonymousProxy
                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
                    [21] => userType
                )

        )

    [city:protected] => GeoIp2\Record\City Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
                )

        )

    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
                )

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                        (
                            [0] => en
                        )

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
                        )

                )

        )

)
 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

Start Your Project

+1

Additional Trust Elements

✨ "1000+ Projects Delivered Globally"

⭐ "Rated 4.9/5 on Google & G2"

🔒 "Your data is secure with us. NDA available."

💬 "Average Response Time: Under 12 hours"