Dataset Overview

This dataset focuses on studying people’s choices between two common types of vacations: mountains and beaches. It gives information on different things like age, location, and interests that can affect people’s preferences. Users can use various machine learning techniques to predict whether people prefer mountains or beaches by analyzing this dataset.

Column Description

1. Age: Age of the respondent (integer).
2. Gender: Gender identity (e.g., male, female, non-binary).
3. Income: Annual income of the respondent in an unspecified currency (integer).
4. Education_Level: Education level (e.g., high school, bachelor, master).
5. Travel_Frequency: Frequency of travel per year (integer).
6. Preferred_Activities: Respondent’s preferred activity (e.g., skiing, swimming, hiking).
7. Vacation_Budget: Budget allocated for vacations (integer).
8. Location: Type of area the respondent lives in (e.g., urban, suburban, rural).
9. Proximity_to_Mountains: Distance to the nearest mountain range (integer).
10. Proximity_to_Beaches: Distance to the nearest beach (integer).
11. Favorite_Season: Respondent’s preferred season (e.g., summer, winter, fall).
12. Pets: Whether the respondent has pets (binary: 0 for no, 1 for yes).
13. Environmental_Concerns: Level of environmental concerns (binary: 0 for no concern, 1 for concern).
14. Preference: Respondent’s preference for vacation destination (binary: 1 for mountains, 0 for beaches).

Activity preference by Demographics

Budget Distribution by Location

Proximity and Season Preferences