To provide a detailed description of your dataset, let's go over each feature based on your dataset structure and the columns in the file. I'll also explain potential meanings for each column and what could be inferred from them.

Columns Description (Assuming from your Dataset)

Based on the columns mentioned in your dataset (Tamil_nadu_taxi_trips_cleaned.csv), here's a detailed description of each:

1. Date_Time:

   • Description: The timestamp representing the exact date and time of the taxi trip's start. It can be broken down into hour, day, month, and year to perform time-based analysis like peak travel hours or trends across months.
   • Type: DateTime
   • Potential Analysis: You could analyze trends based on time, such as determining peak traffic hours, fare variation by time of day, or the busiest travel days of the week.

2. Pickup_Location:

   • Description: This represents the geographical or categorical location where the taxi trip begins. The value could be a specific location name, zone, or area code.
   • Type: Categorical (encoded later into numerical form)
   • Potential Analysis: You can analyze the distribution of trips across different locations, identify popular pickup spots, or perform clustering on locations to find patterns.

3. Drop_Location:

   • Description: The destination location where the taxi trip ends. Like Pickup_Location, this could be represented as a location name or area code.
   • Type: Categorical (encoded later into numerical form)
   • Potential Analysis: This can be used for analyzing the most common destinations, calculating distances between pickup and drop locations, and evaluating demand for rides to certain areas.

4. Distance_km:

   • Description: The distance traveled during the trip in kilometers.
   • Type: Numeric
   • Potential Analysis: This feature is directly related to the fare prediction, as longer distances tend to result in higher fares. You can also analyze average trip distances, or correlate distances with time spent in traffic.

5. Fare_INR:

   • Description: The fare charged for the trip, represented in Indian Rupees (INR).
   • Type: Numeric
   • Potential Analysis: This is a key feature for fare prediction models. You could also analyze average fares, identify outliers (like unusually high or low fares), or see how fare correlates with other features such as distance, time of day, and number of passengers.

6. No_of_Passengers:

   • Description: The number of passengers on the trip.
   • Type: Numeric (integer)
   • Potential Analysis: You can analyze the frequency of trips with different numbers of passengers, check if the number of passengers impacts the fare, or evaluate how many shared rides or group trips occur.

7. Travel_Time_hrs:

   • Description: The duration of the taxi trip in hours.
   • Type: Numeric
   • Potential Analysis: This is an important feature for analyzing traffic conditions and travel efficiency. You can evaluate if longer travel times correlate with higher fares and whether travel time increases during rush hours.

8. Tips_INR:

   • Description: The amount of tip given by the passenger in INR.
   • Type: Numeric
   • Potential Analysis: You can analyze tipping patterns, see if there's a relationship between distance, fare, and tips, or identify passengers' tipping behavior based on time of day or specific locations.

9. Tourist_Place_Nearby:

   • Description: Indicates whether the pickup or drop location is near a tourist attraction.
   • Type: Categorical (likely a binary indicator, i.e., yes/no)
   • Potential Analysis: This feature could be used to analyze the impact of tourist locations on fare prices, distance, and passenger frequency. You can also identify if tourists are more likely to tip.

10. Weather_Condition:

    • Description: Represents the weather conditions during the trip (e.g., sunny, rainy, cloudy, etc.).
    • Type: Categorical (encoded later into numerical form)
    • Potential Analysis: Weather conditions may impact both travel times and fare amounts. For example, rainy weather could lead to longer travel times, affecting fare amounts.

11. Vehicle_Type:

    • Description: Specifies the type of vehicle used for the taxi trip (e.g., sedan, SUV, auto-rickshaw, etc.).
    • Type: Categorical (encoded later into numerical form)
    • Potential Analysis: Different vehicle types may result in varying fare structures. You can analyze how different vehicle types affect fare, travel time, and tipping behavior.

Steps for Dataset Analysis

1. Handling Missing Data:
   • As seen earlier, several columns had missing values (Date_Time, Pickup_Location, Drop_Location, Distance_km, etc.). Filling these appropriate...

DESCRIPTION: This dataset, titled "Travel Info", captures responses from individuals regarding their travel habits, preferences, and demographics. It contains the following key information: 1. Demographic Details: Includes respondents' names, age, gender, and country of residence. 2. Travel Frequency: Indicates how often each respondent travels in a year (e.g., 1-2 times, 6-10 times, more than 10 times). 3. Destination Preferences: Lists the types of destinations travelers prefer, such as mountains, beaches, cities, historical sites, and natural landscapes. 4. Mode of Travel: Captures preferred modes of transportation like flights, trains, buses, road trips, and cruises. 5. Accommodation Preferences: Details the types of accommodation chosen by travelers, including hotels, hostels, homestays/Airbnbs, resorts, and camping. 6. Trip Planning Methods: Explores how respondents usually plan their trips, including personal research, travel agencies, online travel platforms, and recommendations from friends and family. 7. Tour Preferences: Indicates whether travelers prefer guided tours or exploring destinations on their own. 8. International Travel Inclination: Captures whether respondents have a preference for international trips. 9. Travel Budget: Provides insight into the typical budget travelers allocate for their trips. 10. Traveler Type: Identifies whether respondents are solo travelers, family travelers, group travelers, or others. 11. Trip Duration: Indicates the average length of trips taken by respondents, ranging from 1-3 days to more than 14 days. This dataset is useful for analyzing travel behavior and preferences, understanding trends in travel destinations, and creating targeted travel services or recommendations. It can also be leveraged for marketing purposes or tourism research to better cater to different types of travelers.

Updates are delayed due to technical difficulties. How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics. The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day. Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air. The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed. These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

Annual estimates of visits and spending by UK residents abroad. Also includes data on nights, purpose, main country visited and mode of travel. Breakdowns by length of stay and nationality are covered. In 2019, new methods were introduced for this dataset. The 2009 to 2019 edition supersedes all previous time series editions of this dataset. We advise against using all editions listed before the 2019 edition.

Tourist Arrivals in the United States increased to 6893068 in August from 6275257 in July of 2025. This dataset provides - United States Tourist Arrivals- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides a comprehensive overview of international tourist arrivals for 2024, as predicted by various sources. The data includes information on the top tourist destinations worldwide, along with historical trends and future projections. It's sourced from reputable organizations like the World Bank and the World Tourism Organization.

Monthly estimates of overseas residents’ visits and spending and visits and spending abroad by UK or Great Britain residents. Also includes data on purpose of visit, area visited by UK residents and area of residence for overseas residents.

Dataset

This dataset was created by Yassine Regayeg

Released under Apache 2.0

Contents

Data

Age : Numeric, the customer’s age Employment Type : Character, the sector of employment GraduateOrNot : Character, whether the customer is a college graduate AnnualIncome : Numeric, the customer’s yearly income FamilyMembers : Numeric, the number of family members living with the customer ChronicDiseases : Numeric, whether the customer has any chronic conditions FrequentFlyer : Character, whether a customer books frequent tickets EverTravelledAbroad : Character, has the customer ever travelled abroad TravelInsurance : Numeric, whether the customer bought travel insurance

Justification:

Columns:

• Location: A ten- -character random string that represents the location's name.
• Nation: Selectively selected from a roster of nations.
• Type: Selectively selected from a range of travel-related categories.
• Visitor count: A random integer value that indicates how many people have visited.
• Rating: A random float number in the range of 1.0 and 5.0 is used to indicate the rating.
• Revenue: A float value chosen at random to indicate the revenue received.
• Accommodation_Available: Returns a value of "Yes" or "No" depending on the availability of accomodation.
• Target Size: During the loop, the file size is measured and rows are added until the file size reaches roughly 310.43 KB.

You will have a tourism_dataset.csv file, roughly 310.43 KB in size, after executing this code. Depending on the data distribution and file overhead, adjustments can be required.

Dataset

This dataset was created by sarah g

Contents

Users' Rating of 222 Different Countries: This dataset comprises user ratings and reviews of 222 different countries worldwide. Each entry in the dataset includes information such as the country's name, user-provided ratings (which could be numerical, star-based, or categorical), reviews (textual feedback or comments), and possibly additional metadata like the date of the review, the user's demographic information, and the platform or service where the review was submitted. This dataset is valuable for sentiment analysis, travel recommendation systems, and understanding public perceptions and experiences regarding various countries. Countries' Popularity Index and Average Number of Visitors: This dataset contains information regarding the popularity index and average number of visitors for multiple countries. The popularity index may be based on factors such as tourism statistics, travel trends, cultural events, economic indicators, or surveys. It quantifies the level of interest or appeal a country holds for travelers and tourists. Additionally, the dataset provides the average number of visitors per specified time period (e.g., per year, per month), offering insights into the tourism traffic and attractiveness of each country. This dataset is valuable for destination marketing, tourism planning, and analyzing travel behavior and preferences on a global scale.

Metadata * C02163V02608: Code for method of booking. * Method of Booking: Information about the method used for booking. * C02276V02746: Code for Domestic or Outbound. * Domestic or Outbound: Indicates whether the booking is for domestic travel (within the same country) or outbound travel (outside of the country). * TLIST(Q1): Code for Quarter of the Year. * Quarter: Indicates the quarter of the year in which the booking was made. * STATISTIC: Code of label for statistical measures * Statistic Label: Contains labels or identifiers for different statistical measures or metrics associated with the bookings. * UNIT: Specifies the unit of measurement for the statistic values in the dataset. For example. * VALUE: Contains the actual values of the statistics corresponding to the respective labels in the "STATISTIC" column.

This is the dataset of World's most visited Countries by international travellers. France has the most visitors in 2021 and dataset contains data of 50 countries. Spain is the second country for tourists.

Context

This data set is statistics data of inbound / outbound tourism, from 2008 to 2018. Original data is provided by OCED. Inbound Data : https://stats.oecd.org/Index.aspx?QueryId=95071 Outbound data : https://stats.oecd.org/Index.aspx?QueryId=95071 Please refer to the link above for detailed information of data.

With these data, it is possible to observe the state of movement of people between countries in tourism. It is assumed that these data can be one of the factors to understand the trend of COVID-19 infection.

Acknowledgements

OECD stats page For full index and others please visit: https://stats.oecd.org/Index.aspx?QueryId=95071 https://stats.oecd.org/Index.aspx?QueryId

Terms and Conditions for data is as follows.

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Dataset

This dataset was created by Soutan Basak

Contents

Context

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This survey explores the travel preferences of individuals, capturing key insights into how people plan, choose, and experience their journeys. The data collected provides valuable information on preferred travel modes, destinations, frequency, and motivations behind travel decisions. The analysis aims to understand emerging trends and patterns in travel behavior, helping inform future travel planning and service improvements.