grouping

Grouping/1. k-mean grouping.r

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+data(iris)
+a = iris
+a
+
+a$Species=NULL # 원래 데이터 제외
+
+kc=kmeans(a, 3) # k=3
+summary(kc)
+
+table(iris$Species, kc$cluster)
+
+plot(a[c("Sepal.Length", "Sepal.Width")], col=kc$cluster)

Prediction/2. timeseries.r

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+install.packages("WDI")
+library(WDI)
+
+gdp = WDI(country = "KR", indicator = c("NY.GDP.PCAP.CD", "NY.GDP.MKTP.CD"), start = 1960, end = 2017)
+
+head(gdp)
+names(gdp) = c("country", "iso2c", "iso3c", "year", "perCapGDP", "GDP")
+head(gdp)
+
+kr = gdp$perCapGDP[gdp$country=="Korea, Rep."]
+kr
+
+kr = ts(kr, start=min(gdp$year), end=max(gdp$year))
+kr
+
+install.packages("forecast")
+library(forecast)
+
+krts = auto.arima(x=kr)
+krts
+
+forecastResult = forecast(object=krts, h=5)
+forecastResult
+
+plot(forecastResult)

iris.df.ps

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