# Life satisfaction and GDP per capita
## Life satisfaction
### Source
This dataset was obtained from the OECD's website at: http://stats.oecd.org/index.aspx?DataSetCode=BLI
### Data description
Int64Index: 3292 entries, 0 to 3291
Data columns (total 17 columns):
"LOCATION" 3292 non-null object
Country 3292 non-null object
INDICATOR 3292 non-null object
Indicator 3292 non-null object
MEASURE 3292 non-null object
Measure 3292 non-null object
INEQUALITY 3292 non-null object
Inequality 3292 non-null object
Unit Code 3292 non-null object
Unit 3292 non-null object
PowerCode Code 3292 non-null int64
PowerCode 3292 non-null object
Reference Period Code 0 non-null float64
Reference Period 0 non-null float64
Value 3292 non-null float64
Flag Codes 1120 non-null object
Flags 1120 non-null object
dtypes: float64(3), int64(1), object(13)
memory usage: 462.9+ KB
### Example usage using python Pandas
>>> life_sat = pd.read_csv("oecd_bli_2015.csv", thousands=',')
>>> life_sat_total = life_sat[life_sat["INEQUALITY"]=="TOT"]
>>> life_sat_total = life_sat_total.pivot(index="Country", columns="Indicator", values="Value")
>>> life_sat_total.info()
<class 'pandas.core.frame.DataFrame'>
Index: 37 entries, Australia to United States
Data columns (total 24 columns):
Air pollution 37 non-null float64
Assault rate 37 non-null float64
Consultation on rule-making 37 non-null float64
Dwellings without basic facilities 37 non-null float64
Educational attainment 37 non-null float64
Employees working very long hours 37 non-null float64
Employment rate 37 non-null float64
Homicide rate 37 non-null float64
Household net adjusted disposable income 37 non-null float64
Household net financial wealth 37 non-null float64
Housing expenditure 37 non-null float64
Job security 37 non-null float64
Life expectancy 37 non-null float64
Life satisfaction 37 non-null float64
Long-term unemployment rate 37 non-null float64
Personal earnings 37 non-null float64
Quality of support network 37 non-null float64
Rooms per person 37 non-null float64
Self-reported health 37 non-null float64
Student skills 37 non-null float64
Time devoted to leisure and personal care 37 non-null float64
Voter turnout 37 non-null float64
Water quality 37 non-null float64
Years in education 37 non-null float64
dtypes: float64(24)
memory usage: 7.2+ KB
## GDP per capita
### Source
Dataset obtained from the IMF's website at: http://goo.gl/j1MSKe
### Data description
Int64Index: 190 entries, 0 to 189
Data columns (total 7 columns):
Country 190 non-null object
Subject Descriptor 189 non-null object
Units 189 non-null object
Scale 189 non-null object
Country/Series-specific Notes 188 non-null object
2015 187 non-null float64
Estimates Start After 188 non-null float64
dtypes: float64(2), object(5)
memory usage: 11.9+ KB
### Example usage using python Pandas
>>> gdp_per_capita = pd.read_csv(
... datapath+"gdp_per_capita.csv", thousands=',', delimiter='\t',
... encoding='latin1', na_values="n/a", index_col="Country")
...
>>> gdp_per_capita.rename(columns={"2015": "GDP per capita"}, inplace=True)
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机器学习(ML)是计算机系统为了有效地执行特定任务,不使用明确的指令,而依赖模式和推理使用的算法和统计模型的科学研究。它被视为人工智能的一个子集。机器学习算法构建一个基于样本数据的数学模型,称为“训练数据”,以便在没有明确编程来执行任务的情况下进行预测或决策。[1][2]机器学习算法用于各种应用,例如电子邮件过滤和计算机视觉,在这些应用中,开发用于执行任务的特定指令的算法是不可行的。机器学习与计算统计学密切相关,计算统计学侧重于使用计算机进行预测。算法优化的研究为机器学习领域提供了方法、理论和应用领域。数据挖掘是机器学习中的一个研究领域,侧重于探索性数据分析到无监督学习。[3][4]在跨业务问题的应用中,机器学习也被称为预测分析。
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