Somerville Happiness Survey
Donated on 5/23/2018
A data extract of a non-federal dataset posted here https://catalog.data.gov/dataset/somerville-happiness-survey-responses-2011-2013-2015
Dataset Characteristics
Tabular
Subject Area
Health and Medicine
Associated Tasks
Classification
Feature Type
Integer
# Instances
143
# Features
-
Dataset Information
Additional Information
It is a case of supervised learning with the use of Receiver Operating Characteristic (ROC) to select the minimal set of attributes preserving or increasing predictability of the data.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no |
0 to 7 of 7
Additional Variable Information
D = decision attribute (D) with values 0 (unhappy) and 1 (happy) X1 = the availability of information about the city services X2 = the cost of housing X3 = the overall quality of public schools X4 = your trust in the local police X5 = the maintenance of streets and sidewalks X6 = the availability of social community events Attributes X1 to X6 have values 1 to 5.
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset somerville_happiness_survey = fetch_ucirepo(id=479) # data (as pandas dataframes) X = somerville_happiness_survey.data.features y = somerville_happiness_survey.data.targets # metadata print(somerville_happiness_survey.metadata) # variable information print(somerville_happiness_survey.variables)
Koczkodaj,Waldemar. (2018). Somerville Happiness Survey. UCI Machine Learning Repository. https://doi.org/10.24432/C5PW36.
@misc{misc_somerville_happiness_survey_479,
author = {Koczkodaj,Waldemar},
title = {{Somerville Happiness Survey}},
year = {2018},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5PW36}
}Creators
Waldemar Koczkodaj
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.