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Lab exercises: working with real datasets, plotting, more regression, kNN and K- means…

This lab exercise covers working with real datasets, plotting, regression, kNN, and K-means clustering using R. It includes various scripts for analyzing and visualizing data, as well as working with different datasets.

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Lab exercises: working with real datasets, plotting, more regression, kNN and K- means…

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  1. Lab exercises: working with real datasets, plotting, more regression, kNN and K-means… Peter Fox Data Analytics – ITWS-4600/ITWS-6600 Week 5b, February 26, 2016

  2. Plot tools/ tips http://statmethods.net/advgraphs/layout.html http://flowingdata.com/2014/02/27/how-to-read-histograms-and-use-them-in-r/ pairs, gpairs, scatterplot.matrix, clustergram, etc. data() # precip, presidents, iris, swiss, sunspot.month (!), environmental, ethanol, ionosphere More script fragments in R will be available on the web site (http://aquarius.tw.rpi.edu/html/DA )

  3. Scripts Lab5b_pairs1_2016.R Lab5b_splom_2016.R Lab5b_gpairs1_2016.R Lab5b_mosaic_2016.R Lab5b_spm_2016.R Lab5b_wknn_2016.R Lab5b_kknn1_2016.R Lab5b_kknn2_2016.R Lab5b_kknn3_2016.R Lab5b_kmeans1_2016.R Lab5b_ctree2_2016.R Lab5b_nyt_2016.R Lab5b_bronx1_2016.R Lab5b_bronx2_2016.R Lab5b_nbayes1_2016.R Lab5b_nbayes2_2016.R Lab5b_nbayes3_2016.R

  4. K Nearest Neighbors (classification) Script – Lab5b_nyt_2016.R > nyt1<-read.csv(“nyt1.csv") … from week 3b slides or script > classif<-knn(train,test,cg,k=5) # > head(true.labels) [1] 1 0 0 1 1 0 > head(classif) [1] 1 1 1 1 0 0 Levels: 0 1 > ncorrect<-true.labels==classif > table(ncorrect)["TRUE"] # or > length(which(ncorrect)) > What do you conclude?

  5. Bronx 1 = Regression > plot(log(bronx$GROSS.SQUARE.FEET), log(bronx$SALE.PRICE) ) > m1<-lm(log(bronx$SALE.PRICE)~log(bronx$GROSS.SQUARE.FEET),data=bronx) You were reminded that log(0) is … not fun  THINK through what you are doing… Filtering is somewhat inevitable: > bronx<-bronx[which(bronx$GROSS.SQUARE.FEET>0 & bronx$LAND.SQUARE.FEET>0 & bronx$SALE.PRICE>0),] > m1<-lm(log(bronx$SALE.PRICE)~log(bronx$GROSS.SQUARE.FEET),data=bronx) • Lab5b_bronx1_2016.R

  6. Interpreting this! Call: lm(formula = log(SALE.PRICE) ~ log(GROSS.SQUARE.FEET), data = bronx) Residuals: Min 1Q Median 3Q Max -14.4529 0.0377 0.4160 0.6572 3.8159 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.0271 0.3088 22.75 <2e-16 *** log(GROSS.SQUARE.FEET) 0.7013 0.0379 18.50 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.95 on 2435 degrees of freedom Multiple R-squared: 0.1233, Adjusted R-squared: 0.1229 F-statistic: 342.4 on 1 and 2435 DF, p-value: < 2.2e-16

  7. Plots – tell me what they tell you!

  8. Solution model 2 > m2<-lm(log(bronx$SALE.PRICE)~log(bronx$GROSS.SQUARE.FEET)+log(bronx$LAND.SQUARE.FEET)+factor(bronx$NEIGHBORHOOD),data=bronx) > summary(m2) > plot(resid(m2)) # > m2a<-lm(log(bronx$SALE.PRICE)~0+log(bronx$GROSS.SQUARE.FEET)+log(bronx$LAND.SQUARE.FEET)+factor(bronx$NEIGHBORHOOD),data=bronx) > summary(m2a) > plot(resid(m2a))

  9. How do you interpret this residual plot?

  10. Solution model 3 and 4 > m3<-lm(log(bronx$SALE.PRICE)~0+log(bronx$GROSS.SQUARE.FEET)+log(bronx$LAND.SQUARE.FEET)+factor(bronx$NEIGHBORHOOD)+factor(bronx$BUILDING.CLASS.CATEGORY),data=bronx) > summary(m3) > plot(resid(m3)) # > m4<-lm(log(bronx$SALE.PRICE)~0+log(bronx$GROSS.SQUARE.FEET)+log(bronx$LAND.SQUARE.FEET)+factor(bronx$NEIGHBORHOOD)*factor(bronx$BUILDING.CLASS.CATEGORY),data=bronx) > summary(m4) > plot(resid(m4))

  11. And this one?

  12. Bronx 2 = complex example See Lab5b_bronx2_2016.R Manipulation Mapping knn kmeans

  13. KNN! Did you loop over k? { knnpred<-knn(mapcoord[trainid,3:4],mapcoord[testid,3:4],cl=mapcoord[trainid,2],k=5) knntesterr<-sum(knnpred!=mappred$class)/length(testid) } knntesterr [1] 0.1028037 0.1308411 0.1308411 0.1588785 0.1401869 0.1495327 0.1682243 0.1962617 0.1962617 0.1869159 What do youthink?

  14. Return object cluster A vector of integers (from 1:k) indicating the cluster to which each point is allocated. centers A matrix of cluster centres. totss The total sum of squares. withinss Vector of within-cluster sum of squares, one component per cluster. tot.withinss Total within-cluster sum of squares, i.e., sum(withinss). betweenss The between-cluster sum of squares, i.e. totss-tot.withinss. size The number of points in each cluster.

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