Basic Info:

The Dataset represents the County Health Ranking of all states taking into account the various factors The County Health Rankings can be used to highlight regional variations in health, increase public understanding of the various factors that affect health, and inspire actions to improve community health. The Rankings capitalizes on our innate desire to compete by enabling comparisons across adjacent or comparable counties within states.

Dataset Information:

The CSV file contains the rankings and data details for the measures used in the 2022/23 County Health Rankings.
1) Outcomes and Factors Rankings --Ranks are all calculated and reported WITHIN states
2)**Outcomes and Factors SubRankings** --Ranks are all calculated and reported WITHIN states
3) Ranked Measure Data --The measures themselves are listed in bold.
4) Ranked Measure Sources & Years
5) Additional Measure Data --These are supplemental measures reported on the Rankings web site but not used in calculating the rankings.
6) Additional Measure Sources & Years

The Data Types of all Columns are automatically set to "Object" To change it just use data.apply(pd.to_numeric)

Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

The dataset contains relevance scores for websites recommended to different users, and comprises of 30, 000 user-website pairs. For a user i and website j, the data contains a 136-dimensional feature vector uj i, which consists of user i’s attributes corresponding to website j, such as length of stay or number of clicks on the website. Furthermore, for each user-website pair, the dataset also contains a relevance score, i.e. how relevant the website was to the user.

Overview

This dataset compiles a decade (2016–2025) of official National Institutional Ranking Framework (NIRF) data, published by the Government of India (Ministry of Education). It includes rankings across all major categories such as Engineering, Management, Universities, Colleges, Medical, Law, Architecture, and Overall.

All data has been web scraped directly from the official NIRF India website (https://www.nirfindia.org ) to maintain accuracy and consistency. This dataset provides a longitudinal view of India’s higher education performance, enabling detailed trend analysis, performance comparison, and educational insights.

Dataset Summary

Each year (2016–2025) contains rankings with detailed parameters for every institution. The typical columns include: | Column Name | Description | | ------------------------------------------- | ----------------------------------------------- | | Institute Name | Full name of the institution | | Category | Type of ranking (Engineering, Management, etc.) | | Rank | Official NIRF rank of the institution | | Score | Overall score assigned by NIRF | | City | Location of the institution | | State | State in which the institution is located | | TLR (Teaching, Learning & Resources) | Measures teaching quality and infrastructure | | RP (Research and Professional Practice) | Reflects research output and innovation | | GO (Graduation Outcomes) | Evaluates student results and placements | | OI (Outreach & Inclusivity) | Captures diversity and social inclusivity | | PR (Perception) | Public and academic perception score | | Year | Year of ranking (2016–2025) |

Why This Dataset Matters

• Trend Analysis: Study how institutions have improved or declined across years.
• Educational Insights: Identify which states or categories perform consistently better.
• Machine Learning: Build predictive models to forecast future rankings based on parameters.
• Data Visualization: Create dashboards showing ranking evolution across India.
• Policy Research: Support educational planning, funding, and quality assessment studies.

Example Use Cases

Comparing IITs, IIMs, NITs, and private universities over 10 years Measuring correlation between NIRF scores and research output Visualizing rank progression of top 100 institutions Clustering institutions by category or score Predicting rank changes using regression or time-series analysis

Data Source All data extracted from: 🔗 Official NIRF India Website: https://www.nirfindia.org

personality-database.com is ranked #8564 in US with 5.27M Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

audio-database.com is ranked #30373 in RU with 235.02K Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

This map shows the access to mental health providers in every county and state in the United States according to the 2024 County Health Rankings & Roadmaps data for counties, states, and the nation. It translates the numbers to explain how many additional mental health providers are needed in each county and state. According to the data, in the United States overall there are 319 people per mental health provider in the U.S. The maps clearly illustrate that access to mental health providers varies widely across the country.The data comes from this County Health Rankings 2024 layer. An updated layer is usually published each year, which allows comparisons from year to year. This map contains layers for 2024 and also for 2022 as a comparison. County Health Rankings & Roadmaps (CHR&R), a program of the University of Wisconsin Population Health Institute with support provided by the Robert Wood Johnson Foundation, draws attention to why there are differences in health within and across communities by measuring the health of nearly all counties in the nation. This map's layers contain 2024 CHR&R data for nation, state, and county levels. The CHR&R Annual Data Release is compiled using county-level measures from a variety of national and state data sources. CHR&R provides a snapshot of the health of nearly every county in the nation. A wide range of factors influence how long and how well we live, including: opportunities for education, income, safe housing and the right to shape policies and practices that impact our lives and futures. Health Outcomes tell us how long people live on average within a community, and how people experience physical and mental health in a community. Health Factors represent the things we can improve to support longer and healthier lives. They are indicators of the future health of our communities. Some example measures are:Life ExpectancyAccess to Exercise OpportunitiesUninsuredFlu VaccinationsChildren in PovertySchool Funding AdequacySevere Housing Cost BurdenBroadband AccessTo see a full list of variables, definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details of this layer. For full documentation, visit the Measures page on the CHR&R website. Notable changes in the 2024 CHR&R Annual Data Release:Measures of birth and death now provide more detailed race categories including a separate category for ‘Native Hawaiian or Other Pacific Islander’ and a ‘Two or more races’ category where possible. Find more information on the CHR&R website.Ranks are no longer calculated nor included in the dataset. CHR&R introduced a new graphic to the County Health Snapshots on their website that shows how a county fares relative to other counties in a state and nation. Data Processing:County Health Rankings data and metadata were prepared and formatted for Living Atlas use by the CHR&R team. 2021 U.S. boundaries are used in this dataset for a total of 3,143 counties. Analytic data files can be downloaded from the CHR&R website.

Interest-Expense Time Series for Future PLC. Future plc, together with its subsidiaries, publishes and distributes content for technology, gaming, sports, fashion, beauty, homes, wealth, and knowledge sectors in the United States and the United Kingdom. It operates through Media and Magazine segments. The company offers content on various platforms, websites, social platforms, videos, email newsletters, and events; magazines; and eCommerce, a retailer or service provider's website to make a purchase. It also provides content marketing, publishing, price comparison website, comparison shopping, B2B, energy auto switching, and digital media publishing services. Future plc was founded in 1985 and is based in Bath, the United Kingdom.

sw-database.com is ranked #58032 in FR with 82.5K Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

These datasets include ratings as well as social (or trust) relationships between users. Data are from LibraryThing (a book review website) and epinions (general consumer reviews).

Metadata includes

• reviews

• price paid (epinions)

• helpfulness votes (librarything)

• flags (librarything)

About the Dataset

This dataset contains Quality of Life indices for various countries around the globe, extracted from the Numbeo website. The data provides valuable metrics for comparing countries based on several aspects of living standards, which can assist in decisions such as choosing a place to live or analyzing global trends in quality of life.

OBS: The code to generate this dataset is presented on: https://www.kaggle.com/code/marcelobatalhah/web-scrapping-quality-of-life-index

Columns in the Dataset

1. Rank:
   The global rank of the country based on its Quality of Life Index according to Year (1 = highest quality of life).

2. Country:
   The name of the country.

3. Quality of Life Index:
   A composite index that evaluates the overall quality of life in a country by combining other indices, such as Safety, Purchasing Power, and Health Care.

4. Purchasing Power Index:
   Measures the relative purchasing power of the average consumer in a country compared to New York City (baseline = 100).

5. Safety Index:
   Indicates the safety level of a country. A higher score suggests a safer environment.

6. Health Care Index:
   Evaluates the quality and accessibility of healthcare in the country.

7. Cost of Living Index:
   Measures the relative cost of living in a country compared to New York City (baseline = 100).

8. Property Price to Income Ratio:
   Compares the affordability of real estate by dividing the average property price by the average income.

9. Traffic Commute Time Index:
   Reflects the average time spent commuting due to traffic.

10. Pollution Index:
    Rates the level of pollution in the country (air, water, etc.).

11. Climate Index:
    Rates the favorability of the climate in the country (higher = more favorable).

12. Year:
    Year when the metrics were extracted.

Key Insights from the Dataset

• The Quality of Life Index aggregates multiple indicators, making it a useful single metric to compare countries.
• Specific indices such as Safety Index or Health Care Index allow for focused analysis on areas like security or healthcare quality.
• Cost of Living Index and Purchasing Power Index can help determine the affordability of living in each country.

How the Data Was Collected

• The dataset was built using web scraping techniques in Python.
• The data was extracted from the "Quality of Life Rankings by Country" page on Numbeo.
• Libraries used:
  • requests for retrieving webpage content.
  • BeautifulSoup for parsing the HTML and extracting relevant information.
  • pandas for organizing and storing the data in a structured format.

Possible Applications

1. Relocation Decision Making:
   Use the dataset to compare countries and identify destinations with high quality of life, safety, and healthcare.

2. Global Analysis:
   Perform exploratory data analysis (EDA) to identify trends and correlations across quality of life metrics.

3. Visualization:
   Plot global maps, bar charts, or other visualizations to better understand the data.

4. Predictive Modeling:
   Use this dataset as a base for machine learning tasks, like predicting Quality of Life Index based on other metrics.

Historical Dataset of Snake River Online is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2021-2023),Total Classroom Teachers Trends Over Years (2021-2023),Distribution of Students By Grade Trends,Asian Student Percentage Comparison Over Years (2022-2023),Hispanic Student Percentage Comparison Over Years (2021-2023),White Student Percentage Comparison Over Years (2021-2023),Two or More Races Student Percentage Comparison Over Years (2021-2023),Diversity Score Comparison Over Years (2021-2023),Reading and Language Arts Proficiency Comparison Over Years (2021-2022),Math Proficiency Comparison Over Years (2021-2023),Overall School Rank Trends Over Years (2021-2023)

Taxi and Hackney Carriage Ranks in York. For further information please visit City of York Council's website. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

Host country of organization for 86 websites in study.

Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.

The USA Hotels Dataset from Booking.com is a rich collection of data related to hotels across the United States, extracted from Booking.com. This dataset includes essential information about hotel listings, such as hotel names, locations, prices, star ratings, customer reviews, and amenities offered. It's an ideal resource for researchers, data analysts, and businesses looking to explore the hospitality industry, analyze customer preferences, and understand pricing patterns in the U.S. hotel market.

Access 3 million+ US hotel reviews — submit your request today.

Key Features:

• Hotel Information: Includes hotel names, addresses, star ratings, and descriptions.
• Pricing Data: Nightly rates, discounts, and price variations by room type and season.
• Customer Reviews: Aggregated ratings and detailed user feedback from verified guests.
• Amenities: Detailed list of amenities provided by each hotel (e.g., Wi-Fi, parking, spa, swimming pool).
• Geographical Information: Hotel locations including city, state, and proximity to major landmarks.

Use Cases:

• Sentiment Analysis: Analyze customer reviews to gauge hotel service quality and guest satisfaction.
• Price Analysis: Compare pricing across different hotels, locations, and time periods to identify trends.
• Recommendation Systems: Build recommendation engines based on customer ratings, reviews, and preferences.
• Tourism and Hospitality Research: Understand patterns in hotel demand and services across various U.S. cities.

A. Market Research and Analysis: Utilize the Tripadvisor dataset to conduct in-depth market research and analysis in the travel and hospitality industry. Identify emerging trends, popular destinations, and customer preferences. Gain a competitive edge by understanding your target audience's needs and expectations.

B. Competitor Analysis: Compare and contrast your hotel or travel services with competitors on Tripadvisor. Analyze their ratings, customer reviews, and performance metrics to identify strengths and weaknesses. Use these insights to enhance your offerings and stand out in the market.

C. Reputation Management: Monitor and manage your hotel's online reputation effectively. Track and analyze customer reviews and ratings on Tripadvisor to identify improvement areas and promptly address negative feedback. Positive reviews can be leveraged for marketing and branding purposes.

D. Pricing and Revenue Optimization: Leverage the Tripadvisor dataset to analyze pricing strategies and revenue trends in the hospitality sector. Understand seasonal demand fluctuations, pricing patterns, and revenue optimization opportunities to maximize your hotel's profitability.

E. Customer Sentiment Analysis: Conduct sentiment analysis on Tripadvisor reviews to gauge customer satisfaction and sentiment towards your hotel or travel service. Use this information to improve guest experiences, address pain points, and enhance overall customer satisfaction.

F. Content Marketing and SEO: Create compelling content for your hotel or travel website based on the popular keywords, topics, and interests identified in the Tripadvisor dataset. Optimize your content to improve search engine rankings and attract more potential guests.

G. Personalized Marketing Campaigns: Use the data to segment your target audience based on preferences, travel habits, and demographics. Develop personalized marketing campaigns that resonate with different customer segments, resulting in higher engagement and conversions.

H. Investment and Expansion Decisions: Access historical and real-time data on hotel performance and market dynamics from Tripadvisor. Utilize this information to make data-driven investment decisions, identify potential areas for expansion, and assess the feasibility of new ventures.

I. Predictive Analytics: Utilize the dataset to build predictive models that forecast future trends in the travel industry. Anticipate demand fluctuations, understand customer behavior, and make proactive decisions to stay ahead of the competition.

J. Business Intelligence Dashboards: Create interactive and insightful dashboards that visualize key performance metrics from the Tripadvisor dataset. These dashboards can help executives and stakeholders get a quick overview of the hotel's performance and make data-driven decisions.

Incorporating the Tripadvisor dataset into your business processes will enhance your understanding of the travel market, facilitate data-driven decision-making, and provide valuable insights to drive success in the competitive hospitality industry

Website type for the 86 websites in study.

Free-Cash-Flow-To-Equity Time Series for Future PLC. Future plc, together with its subsidiaries, publishes and distributes content for technology, gaming, sports, fashion, beauty, homes, wealth, and knowledge sectors in the United States and the United Kingdom. It operates through Media and Magazine segments. The company offers content on various platforms, websites, social platforms, videos, email newsletters, and events; magazines; and eCommerce, a retailer or service provider's website to make a purchase. It also provides content marketing, publishing, price comparison website, comparison shopping, B2B, energy auto switching, and digital media publishing services. Future plc was founded in 1985 and is based in Bath, the United Kingdom.

Total-Stockholder-Equity Time Series for KakakuCom Inc. Kakaku.com, Inc., together with its subsidiaries, engages in the provision of purchase support, restaurant review, and other services in Japan. The company operates Kakaku.com, that provides prices, specifications, and user reviews, on various products and services, such as computers, home appliances, fashion, interior goods, and finance and communications; and Tabelog.com, a restaurant search and reservation site. It also operates Kyushu Box and Jobcube, job classified websites; Smaity, a residential real estate website; 4 travel, a travel review and comparison site; Sumaity, an online travel site; icotto, an online travel Information media site; Bus Comparison Navi, a comparison search site for nationwide express buses and night buses and bus tours; and low-price trips, a price comparison site for domestic travel and overseas airline tickets. In addition, the company operates kinarino; eiga.com; and webCG. Kakaku.com, Inc. was incorporated in 1997 and is headquartered in Tokyo, Japan.