Data
HappinessRank_2015

HappinessRank_2015

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Author: Sustainable Development Solutions Network Source: [Kaggle](https://www.kaggle.com/unsdsn/world-happiness) - 2017 Please cite: None indicated The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015 (represented here), and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness. The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for the years 2013-2016 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others. ### Attribute description The following columns: GDP per Capita, Family, Life Expectancy, Freedom, Generosity, Trust Government Corruption describe the extent to which these factors contribute in evaluating the happiness in each country. The Dystopia Residual metric actually is the Dystopia Happiness Score(1.85) + the Residual value or the unexplained value for each country as stated in the previous answer. If you add all these factors up, you get the happiness score so it might be un-reliable to model them to predict Happiness Scores.

12 features

Happiness Score (target)numeric157 unique values
0 missing
Country (row identifier)nominal158 unique values
0 missing
Regionnominal10 unique values
0 missing
Happiness Ranknumeric157 unique values
0 missing
Standard Errornumeric153 unique values
0 missing
Economy (GDP per Capita)numeric158 unique values
0 missing
Familynumeric158 unique values
0 missing
Health (Life Expectancy)numeric157 unique values
0 missing
Freedomnumeric158 unique values
0 missing
Trust (Government Corruption)numeric157 unique values
0 missing
Generositynumeric158 unique values
0 missing
Dystopia Residualnumeric158 unique values
0 missing

62 properties

158
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
10
Number of numeric attributes.
2
Number of nominal attributes.
0
Percentage of binary attributes.
0.26
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.2
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
5.99
Maximum kurtosis among attributes of the numeric type.
0.05
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
1.48
Third quartile of kurtosis among attributes of the numeric type.
79.49
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
83.33
Percentage of numeric attributes.
2.92
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
10
The minimal number of distinct values among attributes of the nominal type.
16.67
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
10
The maximum number of distinct values among attributes of the nominal type.
-1.01
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
1.1
Third quartile of skewness among attributes of the numeric type.
1.98
Maximum skewness among attributes of the numeric type.
0.02
Minimum standard deviation of attributes of the numeric type.
-0.8
First quartile of kurtosis among attributes of the numeric type.
0.7
Third quartile of standard deviation of attributes of the numeric type.
45.75
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
0.21
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
0.69
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
-0.49
First quartile of skewness among attributes of the numeric type.
9.03
Mean of means among attributes of the numeric type.
0.13
First quartile of standard deviation of attributes of the numeric type.
0.97
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.07
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.08
Number of attributes divided by the number of instances.
10
Average number of distinct values among the attributes of the nominal type.
0.74
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.18
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
4.88
Mean standard deviation of attributes of the numeric type.
-0.12
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

2 tasks

2 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Happiness Score
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