In-Vehicle Coupon Recommendation
Donated on 9/14/2020
This data studies whether a person will accept the coupon recommended to him in different driving scenarios
Dataset Characteristics
Multivariate
Subject Area
Business
Associated Tasks
Classification
Feature Type
Categorical, Integer
# Instances
12684
# Features
25
Dataset Information
Additional Information
This data was collected via a survey on Amazon Mechanical Turk. The survey describes different driving scenarios including the destination, current time, weather, passenger, etc., and then ask the person whether he will accept the coupon if he is the driver. For more information about the dataset, please refer to the paper: Wang, Tong, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, and Perry MacNeille. 'A bayesian framework for learning rule sets for interpretable classification.' The Journal of Machine Learning Research 18, no. 1 (2017): 2357-2393.
Has Missing Values?
Yes
Introductory Paper
By Wang, Tong, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, and Perry MacNeille. 2017
Published in The Journal of Machine Learning Research 18, no. 1
Variables Table
| Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
|---|---|---|---|---|---|---|
| destination | Feature | Categorical | no | |||
| passenger | Feature | Categorical | no | |||
| weather | Feature | Categorical | no | |||
| temperature | Feature | Integer | no | |||
| time | Feature | Categorical | no | |||
| coupon | Feature | Categorical | no | |||
| expiration | Feature | Categorical | no | |||
| gender | Feature | Categorical | Gender | no | ||
| age | Feature | Integer | Age | no | ||
| maritalStatus | Feature | Categorical | Marital Status | no |
0 to 10 of 26
Additional Variable Information
destination: No Urgent Place, Home, Work passanger: Alone, Friend(s), Kid(s), Partner (who are the passengers in the car) weather: Sunny, Rainy, Snowy temperature:55, 80, 30 time: 2PM, 10AM, 6PM, 7AM, 10PM coupon: Restaurant(<$20), Coffee House, Carry out & Take away, Bar, Restaurant($20-$50) expiration: 1d, 2h (the coupon expires in 1 day or in 2 hours) gender: Female, Male age: 21, 46, 26, 31, 41, 50plus, 36, below21 maritalStatus: Unmarried partner, Single, Married partner, Divorced, Widowed has_Children:1, 0 education: Some college - no degree, Bachelors degree, Associates degree, High School Graduate, Graduate degree (Masters or Doctorate), Some High School occupation: Unemployed, Architecture & Engineering, Student, Education&Training&Library, Healthcare Support, Healthcare Practitioners & Technical, Sales & Related, Management, Arts Design Entertainment Sports & Media, Computer & Mathematical, Life Physical Social Science, Personal Care & Service, Community & Social Services, Office & Administrative Support, Construction & Extraction, Legal, Retired, Installation Maintenance & Repair, Transportation & Material Moving, Business & Financial, Protective Service, Food Preparation & Serving Related, Production Occupations, Building & Grounds Cleaning & Maintenance, Farming Fishing & Forestry income: $37500 - $49999, $62500 - $74999, $12500 - $24999, $75000 - $87499, $50000 - $62499, $25000 - $37499, $100000 or More, $87500 - $99999, Less than $12500 Bar: never, less1, 1~3, gt8, nan4~8 (feature meaning: how many times do you go to a bar every month?) CoffeeHouse: never, less1, 4~8, 1~3, gt8, nan (feature meaning: how many times do you go to a coffeehouse every month?) CarryAway:n4~8, 1~3, gt8, less1, never (feature meaning: how many times do you get take-away food every month?) RestaurantLessThan20: 4~8, 1~3, less1, gt8, never (feature meaning: how many times do you go to a restaurant with an average expense per person of less than $20 every month?) Restaurant20To50: 1~3, less1, never, gt8, 4~8, nan (feature meaning: how many times do you go to a restaurant with average expense per person of $20 - $50 every month?) toCoupon_GEQ15min:0,1 (feature meaning: driving distance to the restaurant/bar for using the coupon is greater than 15 minutes) toCoupon_GEQ25min:0, 1 (feature meaning: driving distance to the restaurant/bar for using the coupon is greater than 25 minutes) direction_same:0, 1 (feature meaning: whether the restaurant/bar is in the same direction as your current destination) direction_opp:1, 0 (feature meaning: whether the restaurant/bar is in the same direction as your current destination) Y:1, 0 (whether the coupon is accepted)
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset in_vehicle_coupon_recommendation = fetch_ucirepo(id=603) # data (as pandas dataframes) X = in_vehicle_coupon_recommendation.data.features y = in_vehicle_coupon_recommendation.data.targets # metadata print(in_vehicle_coupon_recommendation.metadata) # variable information print(in_vehicle_coupon_recommendation.variables)
In-Vehicle Coupon Recommendation. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C5GS4P.
@misc{misc_in-vehicle_coupon_recommendation_603,
title = {{In-Vehicle Coupon Recommendation}},
year = {2020},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5GS4P}
}DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.