I collected the data largely using Open AI.

Celebrity - Their stage name.

Name - Their birth name.

Nationality - Where they were born, using the 2 letter country code standards.

Gender - Their gender.

Estimated Net Worth - This was not gathered using AI. I used Google and if it returned an estimated range like 80 million to 100 million, I chose the lowest amount given, or 80 million in the example.

Age at End of 2023 - Their age on 12/31/23.

Birth Date - Their birthday in mm/dd/yyyy format.

Birth Month - The month they were born in.

Birth Day - The day of the month they were born on.

Birth Year - The year they were Born.

Industry - What Industries they operate in.

What you can analyze:

• What the average male and female celebrity net worth and age then compare the two.
• What the average net worth of a celebrity under 30 is compared to ones over 30.
• The proportions of men and women that have a net worth over a certain amount, are over a certain age, or are of each nationality.
• What the average celebrity's net worth within each industry is and then compare between industries.
• If you like astrology, you can check to see what the average astrological sign's net worth is and then compare amongst the other astrological signs.
• Also, if you like astrology, see which astrological sign is most common amongst celebrities.
• Comparing the average age, net worth, or most common industry occurred in any nation then compare amongst other nations.
• Comparing any combination of these demographics to other combinations of these demographics. (For the purpose of example: Comparing what the average net worth of a female celebrity born before 1980 in Great Britain makes compared to the average net worth of a male celebrity born after 1980 in the US. Though you may not have any logical reason to compare these 2 things, it shows how you can add more and more conditions then compare in order to get more accurate conclusions.)

Context

Covid-19 Data collected from various sources on the internet. This dataset has daily level information on the number of affected cases, deaths, and recovery from the 2019 novel coronavirus. Please note that this is time-series data and so the number of cases on any given day is the cumulative number.

Content

The dataset includes 28 files scrapped from various data sources mainly the John Hopkins GitHub repository, the ministry of health affairs India, worldometer, and Our World in Data website. The details of the files are as follows

• countries-aggregated.csv A simple and cleaned data with 5 columns with self-explanatory names. -covid-19-daily-tests-vs-daily-new-confirmed-cases-per-million.csv A time-series data of daily test conducted v/s daily new confirmed case per million. Entity column represents Country name while code represents ISO code of the country. -covid-contact-tracing.csv Data depicting government policies adopted in case of contact tracing. 0 -> No tracing, 1-> limited tracing, 2-> Comprehensive tracing. -covid-stringency-index.csv The nine metrics used to calculate the Stringency Index are school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stay-at-home requirements; public information campaigns; restrictions on internal movements; and international travel controls. The index on any given day is calculated as the mean score of the nine metrics, each taking a value between 0 and 100. A higher score indicates a stricter response (i.e. 100 = strictest response). -covid-vaccination-doses-per-capita.csv A total number of vaccination doses administered per 100 people in the total population. This is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g. people receive multiple doses). -covid-vaccine-willingness-and-people-vaccinated-by-country.csv Survey who have not received a COVID vaccine and who are willing vs. unwilling vs. uncertain if they would get a vaccine this week if it was available to them. -covid_india.csv India specific data containing the total number of active cases, recovered and deaths statewide. -cumulative-deaths-and-cases-covid-19.csv A cumulative data containing death and daily confirmed cases in the world. -current-covid-patients-hospital.csv Time series data containing a count of covid patients hospitalized in a country -daily-tests-per-thousand-people-smoothed-7-day.csv Daily test conducted per 1000 people in a running week average. -face-covering-policies-covid.csv Countries are grouped into five categories: 1->No policy 2->Recommended 3->Required in some specified shared/public spaces outside the home with other people present, or some situations when social distancing not possible 4->Required in all shared/public spaces outside the home with other people present or all situations when social distancing not possible 5->Required outside the home at all times regardless of location or presence of other people -full-list-cumulative-total-tests-per-thousand-map.csv Full list of total tests conducted per 1000 people. -income-support-covid.csv Income support captures if the government is covering the salaries or providing direct cash payments, universal basic income, or similar, of people who lose their jobs or cannot work. 0->No income support, 1->covers less than 50% of lost salary, 2-> covers more than 50% of the lost salary. -internal-movement-covid.csv Showing government policies in restricting internal movements. Ranges from 0 to 2 where 2 represents the strictest. -international-travel-covid.csv Showing government policies in restricting international movements. Ranges from 0 to 2 where 2 represents the strictest. -people-fully-vaccinated-covid.csv Contains the count of fully vaccinated people in different countries. -people-vaccinated-covid.csv Contains the total count of vaccinated people in different countries. -positive-rate-daily-smoothed.csv Contains the positivity rate of various countries in a week running average. -public-gathering-rules-covid.csv Restrictions are given based on the size of public gatherings as follows: 0->No restrictions 1 ->Restrictions on very large gatherings (the limit is above 1000 people) 2 -> gatherings between 100-1000 people 3 -> gatherings between 10-100 people 4 -> gatherings of less than 10 people -school-closures-covid.csv School closure during Covid. -share-people-fully-vaccinated-covid.csv Share of people that are fully vaccinated. -stay-at-home-covid.csv Countries are grouped into four categories: 0->No measures 1->Recommended not to leave the house 2->Required to not leave the house with exceptions for daily exercise, grocery shopping, and ‘essent...

Context The world's population has undergone remarkable growth, exceeding 7.5 billion by mid-2019 and continuing to surge beyond previous estimates. Notably, China and India stand as the two most populous countries, with China's population potentially facing a decline while India's trajectory hints at surpassing it by 2030. This significant demographic shift is just one facet of a global landscape where countries like the United States, Indonesia, Brazil, Nigeria, and others, each with populations surpassing 100 million, play pivotal roles.

The steady decrease in growth rates, though, is reshaping projections. While the world's population is expected to exceed 8 billion by 2030, growth will notably decelerate compared to previous decades. Specific countries like India, Nigeria, and several African nations will notably contribute to this growth, potentially doubling their populations before rates plateau.

Content This dataset provides comprehensive historical population data for countries and territories globally, offering insights into various parameters such as area size, continent, population growth rates, rankings, and world population percentages. Spanning from 1970 to 2023, it includes population figures for different years, enabling a detailed examination of demographic trends and changes over time.

Dataset Structured with meticulous detail, this dataset offers a wide array of information in a format conducive to analysis and exploration. Featuring parameters like population by year, country rankings, geographical details, and growth rates, it serves as a valuable resource for researchers, policymakers, and analysts. Additionally, the inclusion of growth rates and world population percentages provides a nuanced understanding of how countries contribute to global demographic shifts.

This dataset is invaluable for those interested in understanding historical population trends, predicting future demographic patterns, and conducting in-depth analyses to inform policies across various sectors such as economics, urban planning, public health, and more.

Visual question answering

VQA understands a provided image and if a person asks question about this, it provides an answer after analyzing (or reasoning) the image via natural language.

KVQA dataset

As part of T-Brain’s projects on social value, KVQA dataset, a Korean version of VQA dataset was created. KVQA dataset consists of photos taken by Korean visually impaired people, questions about the photos, and 10 answers from 10 distinct annotators for each question. Currently, it consists of 30,000 sets of images and questions, and 300,000 answers, but by the end of this year, we will increase the dataset size to 100,000 sets of images and questions, and 1 million answers. This dataset can be used only for educational and research purposes. Please refer to the attached license for more details. We hope that the KVQA dataset can simultaneously provide opportunities for the development of Korean VQA technology as well as creation of meaningful social value in Korean society.

You can download KVQA dataset via this link.

Evaluation

We measure the model's accuracy by using answers collected from 10 different people for each question. If the answer provided by a VQA model is equal to 3 or more answers from 10 annotators, it gets 100%; if less than 3, it gets a partial score proportionately. To be consistent with ‘human accuracies’, measured accuracies are averaged over all 10 choose 9 sets of human annotators. Please refer to VQA Evaluation which we follow.

Usage

from datasets import load_dataset

raw_datasets = load_dataset(
        "kvqa.py", 
        "default",
        cache_dir="huggingface_datasets", 
        data_dir="data",
        ignore_verifications=True,
      )

dataset_train = raw_datasets["train"]

for item in dataset_train:
  print(item)
  exit()

Data statistics

v1.0 (Jan. 2020)

Data

Data field description

Data example

[{
    "image": "KVQA_190712_00143.jpg",
    "source": "kvqa",
    "answers": [{
      "answer": "피아노",
      "answer_confidence": "yes"
    }, {
      "answer": "피아노",
      "answer_confidence": "yes"
    }, {
      "answer": "피아노 치고있다",
      "answer_confidence": "maybe"
    }, {
      "answer": "unanswerable",
      "answer_confidence": "maybe"
    }, {
      "answer": "게임",
      "answer_confidence": "maybe"
    }, {
      "answer": "피아노 앞에서 무언가를 보고 있음",
      "answer_confidence": "maybe"
    }, {
      "answer": "피아노치고있어",
      "answer_confidence": "maybe"
    }, {
      "answer": "피아노치고있어요",
      "answer_confidence": "maybe"
    }, {
      "answer": "피아노 연주",
      "answer_confidence": "maybe"
    }, {
      "answer": "피아노 치기",
      "answer_confidence": "yes"
    }],
    "question": "방에 있는 사람은 지금 뭘하고 있지?",
    "answerable": 1,
    "answer_type": "other"
  },
  {
    "image": "VizWiz_train_000000008148.jpg",
    "source": "vizwiz",
    "answers": [{
      "answer": "리모컨",
      "answer_confidence": "yes"
    }, {
      "answer": "리모컨",
      "answer_confidence": "yes"
    }, {
      "answer": "리모컨",
      "answer_confidence": "yes"
    }, {
      "answer": "티비 리모컨",
      "answer_confidence": "yes"
    }, {
      "answer": "리모컨",
      "answer_confidence": "yes"
    }, {
      "answer": "리모컨",
      "answer_confidence": "yes"
    }, {
      "answer": "리모컨",
      "answer_confidence": "yes"
    }, {
      "answer": "리모컨",
      "answer_confidence": "maybe"
    }, {
      "answer": "리모컨",
      "answer_confidence": "yes"
    }, {
      "answer": "리모컨",
      "answer_confidence": "yes"
    }],
    "question": "이것은 무엇인가요?",
    "answerable": 1,
    "answer_type": "other"
  }
]

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

https://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

https://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

https://user-images.githubusercontent.com/91852182/145270229-514f0983-3bbb-4cd3-be64-980e92656a02.png"> https://user-images.githubusercontent.com/91852182/145270251-6f6f6472-8817-435c-a995-9bc4bfef10d1.png">

After we will clear our data frame, will remove missing values.

https://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

This collection contains individual-level and 1-percent national sample data from the 1960 Census of Population and Housing conducted by the Census Bureau. It consists of a representative sample of the records from the 1960 sample questionnaires. The data are stored in 30 separate files, containing in total over two million records, organized by state. Some files contain the sampled records of several states while other files contain all or part of the sample for a single state. There are two types of records stored in the data files: one for households and one for persons. Each household record is followed by a variable number of person records, one for each of the household members. Data items in this collection include the individual responses to the basic social, demographic, and economic questions asked of the population in the 1960 Census of Population and Housing. Data are provided on household characteristics and features such as the number of persons in household, number of rooms and bedrooms, and the availability of hot and cold piped water, flush toilet, bathtub or shower, sewage disposal, and plumbing facilities. Additional information is provided on tenure, gross rent, year the housing structure was built, and value and location of the structure, as well as the presence of air conditioners, radio, telephone, and television in the house, and ownership of an automobile. Other demographic variables provide information on age, sex, marital status, race, place of birth, nationality, education, occupation, employment status, income, and veteran status. The data files were obtained by ICPSR from the Center for Social Analysis, Columbia University. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR07756.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

The Brain Health Data Pilot (BHDP) project aims to be a shared database (like a library) of information for scientists studying brain health, especially for diseases like dementia, which affects about 900,000 people in the UK. Its main feature is a huge collection of brain images linked to routinely collected health records, both from NHS Scotland, which will help scientists learn more about dementia and other brain diseases. What is special about this database is that it will get better over time – as scientists use it and add their discoveries, it becomes more valuable.

This dataset is a subset of the Prescribing Information System (PIS) data for use with the BHDP project.

The information is supplied by Practitioner & Counter Fraud Services Division (P&CFS) who is responsible for the processing and pricing of all prescriptions dispensed in Scotland. These data are augmented with information on prescriptions written in Scotland that were dispensed elsewhere in the United Kingdom. GP’s write the vast majority of these prescriptions, with the remainder written by other authorised prescribers such as nurses and dentists. Also included in the dataset are prescriptions written in hospitals that are dispensed in the community. Note that prescriptions dispensed within hospitals are not included. Data includes CHI number, prescriber and dispenser details for community prescribing, costs and drug information. Data on practices (e.g. list size), organisational structures (e.g. practices within Community Health Partnerships (CHPs) and NHS Boards), prescribable items (e.g. manufacturer, formulation code, strength) are also included. Around 100 million data items are loaded per annum.

Success.ai’s Prospect Data for Biotechnology & Pharmaceutical Innovators Globally provides a powerful dataset designed to connect businesses with key players driving innovation in the biotech and pharmaceutical industries worldwide. Covering companies engaged in drug development, biotechnology research, and life sciences innovation, this dataset offers verified profiles, professional histories, work emails, and phone numbers of decision-makers and industry leaders.

With access to over 700 million verified global profiles and 30 million company profiles, Success.ai ensures your outreach, market research, and partnership efforts are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is indispensable for navigating the fast-evolving biotech and pharmaceutical landscape.

Why Choose Success.ai’s Prospect Data for Biotech and Pharmaceutical Innovators?

1. Verified Contact Data for Industry Professionals

   • Access verified work emails, phone numbers, and LinkedIn profiles of executives, R&D leads, compliance officers, and procurement managers in the biotech and pharmaceutical sectors.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and maximizing communication efficiency.

2. Comprehensive Coverage Across Global Markets

   • Includes profiles of professionals and companies from North America, Europe, Asia-Pacific, and other emerging biotech and pharmaceutical markets.
   • Gain insights into global industry trends, drug development pipelines, and regional innovations.

3. Continuously Updated Datasets

   • Real-time updates capture leadership changes, research breakthroughs, funding activities, and regulatory compliance updates.
   • Stay ahead of market developments and align your strategies with industry dynamics.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible use of data and compliance with legal standards.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with decision-makers, researchers, and executives in biotech and pharmaceutical industries worldwide.
• 30M Company Profiles: Access detailed firmographic data, including revenue ranges, research capacities, and operational footprints.
• Professional Histories: Gain insights into the expertise, career progressions, and roles of professionals driving innovation.
• Leadership Contact Information: Connect directly with CEOs, R&D heads, regulatory managers, and other key stakeholders shaping the future of life sciences.

Key Features of the Dataset:

1. Decision-Maker Profiles in Biotech and Pharmaceuticals

   • Identify and engage with professionals leading research, clinical trials, supply chains, and compliance efforts.
   • Target individuals responsible for strategic decisions in drug development, technology integration, and regulatory adherence.

2. Advanced Filters for Precision Targeting

   • Filter professionals and companies by industry focus (biotech, generics, vaccines), geographic region, revenue size, or workforce composition.
   • Tailor campaigns to address specific needs, such as drug discovery, manufacturing scalability, or market entry.

3. Research and Innovation Insights

   • Access data on research priorities, product pipelines, and innovation trends across global biotech and pharmaceutical sectors.
   • Leverage these insights to position your offerings effectively and uncover new opportunities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes with industry professionals.

Strategic Use Cases:

1. Sales and Lead Generation

   • Present products, services, or technologies that accelerate R&D, streamline production, or ensure compliance to biotech and pharmaceutical companies.
   • Build relationships with procurement teams, regulatory managers, and R&D heads managing budgets and resource allocation.

2. Market Research and Competitive Analysis

   • Analyze global trends in biotechnology and pharmaceuticals to guide product innovation and strategic planning.
   • Benchmark against competitors to identify market gaps, emerging niches, and high-growth opportunities.

3. Partnership Development and Licensing

   • Engage with organizations seeking strategic partnerships, co-development opportunities, or licensing agreements for drug development.
   • Foster alliances that drive mutual growth and innovation in life sciences.

4. Regulatory Compliance and Risk Mitigation

   • Connect with compliance officers and legal professionals overseeing regulatory adherence, clinical trials, and product approvals.
   • Offer solutions that simplify compliance reporting, risk management, and quality assurance processes.

Why Choose Success.ai?

1. Best Price...

Money Supply M0 in the United States increased to 53615000 USD Million in October from 5478000 USD Million in September of 2025. This dataset provides - United States Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Any form of currency that only exists digitally relying on cryptography to prevent counterfeiting and fraudulent transactions is defined as cryptocurrency. Bitcoin was the very first Cryptocurrency. It was invented in 2009 by an anonymous person, or group of people, who referred to themselves as Satoshi Nakamoto. When someone sends a bitcoin (or a fraction of a bitcoin) to someone else, “miners” record that transaction in a block and add the transaction to a digital ledger. These blocks are collectively known as the blockchain – an openly accessible ledger of every transaction ever made in bitcoin. Blockchains are distributed across many computers so that the record of transactions cannot be altered. Only 21 million bitcoins can ever be mined and about 17 million have been mined so far. Bitcoin is mined, or created, by people (miners) getting their computers to solve mathematical problems, in order to update and verify the ledger.

The value of bitcoin is determined by what people are willing to pay for it, and is very volatile, fluctuating wildly from day to day. In April 2013, the value of 1 bitcoin (BTC) was around $100 USD. At the beginning of 2017 its value was $1,022 USD and by the 15th of December it was worth $19,497. As of the 3rd of March 2018, 1 BTC sells for $11,513 USD. So, the time series analysis of bitcoin series is very challenging.

The following dataset is the daily closing price of bitcoin from the 27th of April 2013 to the 3rd of March 2018. Source: coinmarketcap.com

The dataset is focused on has gathered from coinmarketcap.com (https://coinmarketcap.com/). includes he daily closing price of bitcoin from the 27th of April 2013 to the 3rd of March 2018 and is available in the csv file Bitcoin_Historical_Price.csv

This Model includes a mean absolute scaled error (MASE), for each of model fits and forecasts. Using the real values of daily bitcoin for 10 days of forecast period (4th - 13th of March 2018).

This model is used to analyze the data, accurately predict the value of bitcoin for the next 10 days. The model includes descriptive analysis, proper visualization, model specification, model fitting and selection, and diagnostic checking.

Success.ai’s Import Export Data for Import, Export & Trade Professionals in Asia delivers a comprehensive dataset tailored for businesses aiming to connect with key players in Asia’s dynamic trade industry. Covering professionals involved in import/export operations, international logistics, and supply chain management, this dataset provides verified contact details, firmographic insights, and actionable professional data.

With access to over 700 million verified global profiles and 70 million business datasets, Success.ai ensures your outreach, market research, and trade strategies are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is essential for navigating the complexities of global trade in Asia.

Why Choose Success.ai’s Import Export Data?

1. Verified Contact Data for Effective Engagement

   • Access verified work emails, phone numbers, and LinkedIn profiles of trade professionals, logistics experts, and supply chain managers.
   • AI-driven validation ensures 99% accuracy, reducing data gaps and improving communication efficiency.

2. Comprehensive Coverage of Asian Trade Markets

   • Includes profiles of professionals from key Asian markets such as China, India, Japan, South Korea, and Southeast Asia.
   • Gain insights into regional trade trends, import/export regulations, and supply chain dynamics.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership roles, trade activities, and market expansions.
   • Stay aligned with evolving market conditions and emerging trade opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful data usage for all business initiatives.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with import/export professionals, logistics managers, and trade consultants across Asia.
• 70M Business Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic footprints.
• Contact Details: Gain verified work emails, phone numbers, and business locations for precise targeting.
• Industry Trends: Understand key import/export opportunities, supply chain challenges, and market dynamics in Asia.

Key Features of the Dataset:

1. Professional Profiles in Import/Export and Logistics

   • Identify and engage with trade professionals managing cross-border operations, customs compliance, and supply chain efficiency.
   • Target individuals responsible for vendor selection, international partnerships, and trade negotiations.

2. Firmographic and Geographic Insights

   • Access data on company structures, trade volumes, and operational hubs in key Asian markets.
   • Pinpoint high-value prospects in established and emerging trade routes for strategic engagement.

3. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (manufacturing, wholesale, retail), geographic location, or revenue size.
   • Tailor campaigns to address specific trade needs such as market entry, cost optimization, or regulatory compliance.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with trade professionals.

Strategic Use Cases:

1. Sales and Business Development

   • Present trade services, logistics solutions, or supply chain optimization tools to import/export managers and trade consultants.
   • Build relationships with procurement teams and logistics managers seeking reliable partners and innovative solutions.

2. Market Research and Competitive Analysis

   • Analyze trends in Asia’s import/export landscape, including key trade routes, regulatory changes, and logistics challenges.
   • Benchmark against competitors to identify growth opportunities, underserved markets, and emerging needs.

3. Partnership Development and Trade Collaboration

   • Engage with businesses seeking partnerships for supply chain management, customs compliance, or international expansion.
   • Foster alliances that enhance efficiency, reduce costs, and drive growth in the import/export sector.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers recruiting for roles in international trade, logistics, or operations.
   • Provide workforce optimization platforms or training solutions tailored to the trade and logistics industry.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality import/export data at competitive prices, ensuring maximum ROI for your marketing, sales, and trade initiatives.

2. Seamless Integration

   • Integrate verified trade data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, simplifying workflows and ...

Success.ai’s Fashion & Apparel Data for Apparel, Fashion & Luxury Goods Professionals in Asia provides a robust dataset tailored for businesses seeking to connect with key players in Asia’s thriving fashion and luxury goods industries. Covering roles such as brand managers, designers, retail executives, and supply chain leaders, this dataset includes verified contact details, professional insights, and actionable business data.

With access to over 700 million verified global profiles and 130 million profiles focused on Asia, Success.ai ensures your outreach, marketing, and business development strategies are supported by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution positions you to succeed in Asia’s competitive and ever-growing fashion markets.

Why Choose Success.ai’s Fashion & Apparel Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of professionals in apparel, fashion, and luxury goods industries across Asia.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and enhancing communication efficiency.

2. Comprehensive Coverage of Asian Fashion Professionals

   • Includes profiles from major fashion hubs such as China, India, Japan, South Korea, and Southeast Asia.
   • Gain insights into regional consumer trends, emerging fashion markets, and luxury goods opportunities.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership, market expansions, and product launches.
   • Stay aligned with evolving industry trends and capitalize on new opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with professionals across the global fashion and apparel industries, with a focus on Asia.
• 130M+ Profiles in Asia: Gain detailed insights into professionals shaping the region’s fashion and luxury goods markets.
• Verified Contact Details: Access work emails, phone numbers, and business locations for precise targeting.
• Leadership Insights: Engage with designers, brand managers, and retail leaders driving Asia’s fashion trends.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with decision-makers in apparel design, luxury goods branding, retail operations, and supply chain management.
   • Target individuals leading innovation in sustainable fashion, fast fashion, and digital transformation.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (luxury goods, ready-to-wear, footwear), geographic location, or job function.
   • Tailor campaigns to align with specific market needs, such as emerging e-commerce platforms or regional fashion preferences.

3. Industry and Regional Insights

   • Leverage data on consumer behaviors, market growth, and regional trends in Asia’s fashion and luxury goods sectors.
   • Refine marketing strategies, product development, and partnership outreach based on actionable insights.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Brand Expansion

   • Design targeted campaigns to promote apparel, luxury goods, or retail solutions to fashion professionals in Asia.
   • Leverage multi-channel outreach, including email, phone, and social media, to maximize engagement.

2. Product Development and Consumer Insights

   • Utilize data on regional trends and consumer preferences to guide product development and marketing strategies.
   • Collaborate with brand managers and designers to tailor collections or launch new offerings aligned with market demands.

3. Partnership Development and Retail Collaboration

   • Build relationships with retail chains, luxury brands, and supply chain leaders seeking strategic alliances.
   • Foster partnerships that expand distribution channels, enhance brand visibility, or improve operational efficiencies.

4. Market Research and Competitive Analysis

   • Analyze trends in Asia’s fashion industry to refine business strategies, identify market gaps, and anticipate consumer demands.
   • Benchmark against competitors to stay ahead in the fast-paced fashion landscape.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality fashion and apparel data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, streamlining workfl...

If you are fascinated in knowing what are the top 100 global brands of 2022 and other details about them, then this dataset is for you. This dataset has been sourced from a couple of websites like https://brandirectory.com/rankings/global/table , https://en.wikipedia.org/wiki/Main_Page. The ranking of the brands is from an independent firm called 'Brandirectory' from 'BrandFinance'. The dataset contains the following columns: 1. Brand(String)-Name of the brand. 2. Founded By/How it was founded(String)-Founder of the brand/How the brand was founded. 3. Founded In(Integer) - The year in which the brand was found. 4. Country(String) - The country in which the brand was founded. 5. Key People(String)- CEO/Chairman/President/Head of the brand. 6. Genre/Industry(String)- The genre or the industry that the brand belongs to. 7. Website(String)-The Website of the brand. 8. Rank in 2022(Integer) - Rank of the brand in 2022. 9. Rank in 2021(Integer)- Rank of the brand in 2021. 10. Brand Value($M) in 2022(Integer) - Brand value in millions in 2022. 11. Brand Value($M) in 2021(Integer) - Brand value in millions in 2021. 12. % Change(String) - % change in 2022 from 2021. 13. Rating in 2022(String) - Rating of the brand in 2022. 14. Rating in 2021(String) - Rating of the brand in 2021.

Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

Introducing Our Comprehensive Global B2B Contact Data Solution

In today’s rapidly evolving business landscape, having access to accurate, comprehensive, and actionable information is not just an advantage—it’s a necessity. Introducing our Global B2B Contact Data Solution, meticulously crafted to empower businesses worldwide by providing them with the tools they need to connect, expand, and thrive in the global market.

What Distinguishes Our Data?

Our Global B2B Contact Data is a cut above the rest, designed with a laser focus on identifying and connecting with pivotal decision-makers. With a database of over 220 million meticulously verified contacts, our data goes beyond mere numbers. Each entry includes business emails and phone numbers that have been thoroughly vetted for accuracy, ensuring that your outreach efforts are both meaningful and effective. This data is a key asset for businesses looking to forge strong connections that are crucial for global expansion and success.

Unparalleled Data Collection Process

Our commitment to quality begins with our data collection process, which is rooted in a robust and reliable approach: - Dynamic Publication Sites: We draw data from ten dynamic publication sites, serving as rich sources for the continuous and real-time creation of our global database. - Contact Discovery Team: Complementing this is our dedicated research powerhouse, the Contact Discovery Team, which conducts extensive investigations to ensure the accuracy and relevance of each contact. This dual-sourcing strategy guarantees that our Global B2B Contact Data is not only comprehensive but also trustworthy, offering you the reliability you need to make informed business decisions.

Versatility Across Diverse Industries

Our Global B2B Contact Data is designed with versatility in mind, making it an indispensable tool across a wide range of industries: - Finance: Enable precise targeting for investment opportunities, partnerships, and market expansion. - Manufacturing: Identify key players and suppliers in the global supply chain, facilitating streamlined operations and business growth. - Technology: Connect with innovators and leaders in tech to foster collaborations, drive innovation, and explore new markets. - Healthcare: Access critical decision-makers in healthcare for strategic partnerships, market penetration, and research collaborations. - Retail: Engage with industry leaders and stakeholders to enhance your retail strategies and expand your market reach. - Energy: Pinpoint decision-makers in the energy sector to explore new ventures, investments, and sustainability initiatives. - Transportation: Identify key contacts in logistics and transportation to optimize operations and expand into new territories. - Hospitality: Connect with executives and decision-makers in hospitality to drive business growth and market expansion. - And Beyond: Our data is applicable across virtually every industry, ensuring that no matter your sector, you have the tools needed to succeed.

Seamless Integration for Holistic Insights

Our Global B2B Contact Data is not just a standalone resource—it’s a vital component of a larger data ecosystem that offers a panoramic view of the business landscape. By seamlessly integrating into our wider data collection framework, our Global B2B Contact Data enables you to: - Access Supplementary Insights: Gain additional valuable insights that complement your primary data, providing a well-rounded understanding of market trends, competitive dynamics, and global key players. - Informed Decision-Making: Whether you’re identifying new market opportunities, analyzing industry trends, or planning global expansion, our data equips you with the insights needed to make strategic, data-driven decisions.

Fostering Global Connections

In today’s interconnected world, relationships are paramount. Our Global B2B Contact Data acts as a powerful conduit for establishing and nurturing these connections on a global scale. By honing in on decision-makers, our data ensures that you can effortlessly connect with the right individuals at the most opportune moments. Whether you’re looking to forge new partnerships, secure investments, or venture into uncharted B2B territories, our data empowers you to build meaningful and lasting business relationships.

Commitment to Privacy and Security

We understand that privacy and security are of utmost importance when it comes to handling data. That’s why we uphold the highest standards of privacy and security, ensuring that all data is managed ethically and in full compliance with global privacy regulations. Businesses can confidently leverage our data, knowing that it is handled with the utmost care and respect for legal requirements.

Continuous Enhancement for Superior Data Quality

Adaptability and continuous improvement are at the core of our ethos. We are committed to consistently enhancing our B2B Contact Data solutions by: - Refining Data C...

Success.ai presents an exclusive opportunity to connect directly with top-tier decision-makers in the finance sector through our CEO Contact Data, specifically designed for venture capital and private equity investors based in the USA. This tailored database is part of our expansive collection that draws from over 700 million global profiles, meticulously verified to ensure the highest quality and reliability.

Why Choose Success.ai’s CEO Contact Data?

Specialized Investor Profiles: Access detailed profiles of CEOs and senior executives from leading venture capital and private equity firms across the United States. Investment Insights: Gain valuable insights into investment trends, fund sizes, and sectors of interest directly from the decision-makers. Verified Contact Details: We provide up-to-date email addresses and phone numbers, ensuring that you reach the right people without the hassle of outdated information. Data Features:

Targeted Financial Sector Data: Directly target influential figures in the financial sector who have the authority to make investment decisions. Comprehensive Executive Information: Profiles include not just contact information but also professional backgrounds, areas of investment focus, and operational histories. Geographic Precision: Focus your outreach efforts on US-based investors with our geographically segmented data. Flexible Delivery and Integration: Choose from various delivery options including API access for real-time integration or static files for periodic campaign use, allowing for seamless incorporation into your CRM or marketing automation tools.

Competitive Pricing with Best Price Guarantee: Success.ai is committed to providing competitive pricing without compromising on quality, backed by our Best Price Guarantee.

Effective Use Cases for CEO Contact Data:

Fundraising Initiatives: Connect with venture capital and private equity firms for fundraising activities or financial endorsements. Partnership Development: Forge strategic partnerships and collaborations with leading investors in the industry. Event Invitations: Send personalized invites to investment summits, roundtables, and networking events catered to top financial executives. Market Analysis: Utilize executive insights to better understand the investment landscape and refine your market strategies. Quality Assurance and Compliance:

Rigorous Data Verification: Our data undergoes continuous verification processes to maintain accuracy and completeness. Compliance with Regulations: All data handling practices adhere to GDPR and other relevant data protection laws, ensuring ethical and lawful use. Support and Custom Solutions:

Client Support: Our team is available to assist with any queries or specific data needs you may have. Tailored Data Solutions: Customize data sets according to specific criteria such as investment size, sector focus, or geographic location. Start Connecting with Venture Leaders: Empower your business strategy and network building by accessing Success.ai’s CEO Contact Data for venture capital and private equity investors. Whether you're looking to initiate funding rounds, explore investment opportunities, or engage with top financial leaders, our reliable data will pave the way for meaningful connections and successful outcomes.

Contact Success.ai today to discover how our precise and comprehensive data can transform your business approach and help you achieve your strategic goals.

Success.ai offers a comprehensive, enterprise-ready B2B leads data solution, ideal for businesses seeking access to over 150 million verified employee profiles and 170 million work emails. Our data empowers organizations across industries to target key decision-makers, optimize recruitment, and fuel B2B marketing efforts. Whether you're looking for UK B2B data, B2B marketing data, or global B2B contact data, Success.ai provides the insights you need with pinpoint accuracy.

Tailored for B2B Sales, Marketing, Recruitment and more: Our B2B contact data and B2B email data solutions are designed to enhance your lead generation, sales, and recruitment efforts. Build hyper-targeted lists based on job title, industry, seniority, and geographic location. Whether you’re reaching mid-level professionals or C-suite executives, Success.ai delivers the data you need to connect with the right people.

API Features:

• Real-Time Updates: Our APIs deliver real-time updates, ensuring that the contact data your business relies on is always current and accurate.
• High Volume Handling: Designed to support up to 860k API calls per day, our system is built for scalability and responsiveness, catering to enterprises of all sizes.
• Flexible Integration: Easily integrate with CRM systems, marketing automation tools, and other enterprise applications to streamline your workflows and enhance productivity.

Benefits of the EU Premium Dataset:

Targeted Reach: Reach potential leads with detailed insights including email addresses, phone numbers, job titles, and more, specifically within the EU markets. Enhanced Lead Quality: Every profile is thoroughly verified, enhancing the quality of your outreach and increasing the likelihood of successful engagements. Best Price Guarantee: We are committed to providing these extensive services at the most competitive prices, ensuring that you receive the best value for your investment.

Key Categories Served: B2B sales leads – Identify decision-makers in key industries, B2B marketing data – Target professionals for your marketing campaigns, Recruitment data – Source top talent efficiently and reduce hiring times, CRM enrichment – Update and enhance your CRM with verified, updated data, Global reach – Coverage across 195 countries, including the United States, United Kingdom, Germany, India, Singapore, and more.

Global Coverage with Real-Time Accuracy: Success.ai’s dataset spans a wide range of industries such as technology, finance, healthcare, and manufacturing. With continuous real-time updates, your team can rely on the most accurate data available: 150M+ Employee Profiles: Access professional profiles worldwide with insights including full name, job title, seniority, and industry. 170M Verified Work Emails: Reach decision-makers directly with verified work emails, available across industries and geographies, including Singapore and UK B2B data. GDPR-Compliant: Our data is fully compliant with GDPR and other global privacy regulations, ensuring safe and legal use of B2B marketing data.

Key Data Points for Every Employee Profile: Every profile in Success.ai’s database includes over 20 critical data points, providing the information needed to power B2B sales and marketing campaigns: Full Name, Job Title, Company, Work Email, Location, Phone Number, LinkedIn Profile, Experience, Education, Technographic Data, Languages, Certifications, Industry, Publications & Awards.

Use Cases Across Industries: Success.ai’s B2B data solution is incredibly versatile and can support various enterprise use cases, including: B2B Marketing Campaigns: Reach high-value professionals in industries such as technology, finance, and healthcare. Enterprise Sales Outreach: Build targeted B2B contact lists to improve sales efforts and increase conversions. Talent Acquisition: Accelerate hiring by sourcing top talent with accurate and updated employee data, filtered by job title, industry, and location. Market Research: Gain insights into employment trends and company profiles to enrich market research. CRM Data Enrichment: Ensure your CRM stays accurate by integrating updated B2B contact data. Event Targeting: Create lists for webinars, conferences, and product launches by targeting professionals in key industries.

Use Cases for Success.ai's Contact Data - Targeted B2B Marketing: Create precise campaigns by targeting key professionals in industries like tech and finance. - Sales Outreach: Build focused sales lists of decision-makers and C-suite executives for faster deal cycles. - Recruiting Top Talent: Easily find and hire qualified professionals with updated employee profiles. - CRM Enrichment: Keep your CRM current with verified, accurate employee data. - Event Targeting: Create attendee lists for events by targeting relevant professionals in key sectors. - Market Research: Gain insights into employment trends and company profiles for better business decisions. - Executive Search: So...

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

Data Set Description This dataset simulates a retail environment with a million rows and 100+ columns, covering customer information, transactional data, product details, promotional information, and customer behavior metrics. It includes data for predicting total sales (regression) and customer churn (classification).

Detailed Column Descriptions Customer Information:

customer_id: Unique identifier for each customer. age: Age of the customer. gender: Gender of the customer (e.g., Male, Female, Other). income_bracket: Income bracket of the customer (e.g., Low, Medium, High). loyalty_program: Whether the customer is part of a loyalty program (Yes/No). membership_years: Number of years the customer has been a member. churned: Whether the customer has churned (Yes/No) - Target for classification. marital_status: Marital status of the customer. number_of_children: Number of children the customer has. education_level: Education level of the customer (e.g., High School, Bachelor's, Master's). occupation: Occupation of the customer. Transactional Data:

transaction_id: Unique identifier for each transaction. transaction_date: Date of the transaction. product_id: Unique identifier for each product. product_category: Category of the product (e.g., Electronics, Clothing, Groceries). quantity: Quantity of the product purchased. unit_price: Price per unit of the product. discount_applied: Discount applied on the transaction. payment_method: Payment method used (e.g., Credit Card, Debit Card, Cash). store_location: Location of the store where the purchase was made. Customer Behavior Metrics:

avg_purchase_value: Average value of purchases made by the customer. purchase_frequency: Frequency of purchases (e.g., Daily, Weekly, Monthly, Yearly). last_purchase_date: Date of the last purchase made by the customer. avg_discount_used: Average discount percentage used by the customer. preferred_store: Store location most frequently visited by the customer. online_purchases: Number of online purchases made by the customer. in_store_purchases: Number of in-store purchases made by the customer. avg_items_per_transaction: Average number of items per transaction. avg_transaction_value: Average value per transaction. total_returned_items: Total number of items returned by the customer. total_returned_value: Total value of returned items. Sales Data:

total_sales: Total sales amount for each customer over the last year - Target for regression. total_transactions: Total number of transactions made by each customer. total_items_purchased: Total number of items purchased by each customer. total_discounts_received: Total discounts received by each customer. avg_spent_per_category: Average amount spent per product category. max_single_purchase_value: Maximum value of a single purchase. min_single_purchase_value: Minimum value of a single purchase. Product Information:

product_name: Name of the product. product_brand: Brand of the product. product_rating: Customer rating of the product. product_review_count: Number of reviews for the product. product_stock: Stock availability of the product. product_return_rate: Rate at which the product is returned. product_size: Size of the product (if applicable). product_weight: Weight of the product (if applicable). product_color: Color of the product (if applicable). product_material: Material of the product (if applicable). product_manufacture_date: Manufacture date of the product. product_expiry_date: Expiry date of the product (if applicable). product_shelf_life: Shelf life of the product (if applicable). Promotional Data:

promotion_id: Unique identifier for each promotion. promotion_type: Type of promotion (e.g., Buy One Get One Free, 20% Off). promotion_start_date: Start date of the promotion. promotion_end_date: End date of the promotion. promotion_effectiveness: Effectiveness of the promotion (e.g., High, Medium, Low). promotion_channel: Channel through which the promotion was advertised (e.g., Online, In-store, Social Media). promotion_target_audience: Target audience for the promotion (e.g., New Customers, Returning Customers). Geographical Data:

customer_zip_code: Zip code of the customer's residence. customer_city: City of the customer's residence. customer_state: State of the customer's residence. store_zip_code: Zip code of the store. store_city: City where the store is located. store_state: State where the store is located. distance_to_store: Distance from the customer's residence to the store. Seasonal and Temporal Data:

holiday_season: Whether the transaction occurred during a holiday season (Yes/No). season: Season of the year (e.g., Winter, Spring, Summer, Fall). weekend: Whether the transaction occurred on a weekend (Yes/No). Customer Interaction Data:

customer_support_calls: Number of calls made to customer support. email_subscription...