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Author: Fabio Mendoza Palechor, Alexis de la Hoz Manotas Source: [UCI]( - 2019 Please cite: [Paper]( Estimation of obesity levels based on eating habits and physical condition dataset This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. The data contains 17 attributes and 2111 records, the records are labeled with the class variable NObesity (Obesity Level), that allows classification of the data using the values of Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II and Obesity Type III. 77% of the data was generated synthetically using the Weka tool and the SMOTE filter, 23% of the data was collected directly from users through a web platform. ### Attribute information - Gender - Age - Height - Weight - family_history_with_overweight - FAVC (frequent high caloric food) - FCVC (amount of vegetables per meal) - NCP (how many main meals a day) - CAEC (eating any food between meals) - SMOKE (smoking) - CH2O (how much water someone's drinking) - SCC (monitoring the calories daily) - FAF (physical activity) - TUE (time spend on technological devices) - CALC (frequency of drinking alcohol) - MTRANS (transportation method) - NObeyesdad

17 features

Gendernominal2 unique values
0 missing
Agenumeric1402 unique values
0 missing
Heightnumeric1574 unique values
0 missing
Weightnumeric1525 unique values
0 missing
family_history_with_overweightnominal2 unique values
0 missing
FAVCnominal2 unique values
0 missing
FCVCnumeric810 unique values
0 missing
NCPnumeric635 unique values
0 missing
CAECnominal4 unique values
0 missing
SMOKEnominal2 unique values
0 missing
CH2Onumeric1268 unique values
0 missing
SCCnominal2 unique values
0 missing
FAFnumeric1190 unique values
0 missing
TUEnumeric1129 unique values
0 missing
CALCnominal4 unique values
0 missing
MTRANSnominal5 unique values
0 missing
NObeyesdadnominal7 unique values
0 missing

19 properties

Number of instances (rows) of the dataset.
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
Number of missing values in the dataset.
Number of instances with at least one value missing.
Number of numeric attributes.
Number of nominal attributes.
Percentage of binary attributes.
Percentage of instances having missing values.
Average class difference between consecutive instances.
Percentage of missing values.
Number of attributes divided by the number of instances.
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
Number of binary attributes.

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