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Travel Industry Churn Analytics
Data Science and Analytics
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Free
About
This dataset focuses on predicting customer churn for a Tour & Travel company. It provides key indicators to help build predictive models, save company money, and facilitate exploratory data analysis (EDA). This data is freely available for practice and was used during a mini-hackathon.
Columns
- Age: The age of the user.
- FrequentFlyer: Indicates whether the customer frequently takes flights (e.g., No, Yes).
- AnnualIncomeClass: Describes the user's annual income bracket (e.g., Middle Income, Low Income).
- ServicesOpted: The count of services opted by the user in recent years.
- AccountSyncedToSocialMedia: A boolean indicating if the customer's company account is synchronised with their social media (true/false).
- BookedHotelOrNot: A boolean indicating whether the customer booked lodgings or hotels using the company's services (true/false).
- Target: The dependent variable, where '1' signifies customer churns and '0' signifies the customer does not churn.
Distribution
This dataset is provided as a CSV file, named
Customertravel.csv, with a file size of 29.64 kB. It contains 7 columns and comprises 954 records.Usage
This dataset is ideal for:
- Building machine learning models to predict customer churn in the travel and tourism sector.
- Performing exploratory data analysis (EDA) to uncover insights into customer behaviour.
- Developing strategies to reduce customer attrition and save company revenue.
- Serving as a practice dataset for data science and machine learning enthusiasts.
- Participation in mini-hackathons and data challenges.
Coverage
The dataset's scope is primarily demographic, focusing on characteristics such as customer age (ranging from 27 to 38 years), frequent flyer status, and annual income class. Specific geographic locations or a defined time range for the data collection are not detailed in the provided information.
License
CC0: Public Domain
Who Can Use It
This dataset is suitable for:
- Data Scientists and Machine Learning Engineers aiming to build and evaluate churn prediction models.
- Business Analysts interested in customer behaviour and retention strategies.
- Students and Beginners in data science looking for practical experience with classification problems and EDA.
- Participants in hackathons or coding challenges focused on predictive analytics.
Dataset Name Suggestions
- Tour & Travel Customer Churn Prediction Dataset
- Travel Industry Churn Analytics
- Customer Retention in Tourism Data
- Predictive Travel Churn Indicators
Attributes
Original Data Source: Travel Industry Churn Analytics
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