ParkerDMCH15HW
Nickolas Parker
11/7/2020
Intoduction
This document looks at clustering for house types.In particular, it looks at data from the Mount_Pleasant_Real_Estate.csv and the HosuePrices.csv. The goal is to create create clusters for houses and see if there are any similarities between the two csvs.
Mount_Pleasant_Real_Estate.csv
house[1:3,]## # A tibble: 3 x 24
## ID `List Price` `Duplex?` Bedrooms `Baths - Total` `Baths - Full`
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 115 369900 Yes 3 2.5 2
## 2 117 375000 Yes 3 2.5 2
## 3 5 769900 No 4 3.5 3
## # ... with 18 more variables: `Baths - Half` <dbl>, Stories <dbl>,
## # Subdivision <chr>, `Square Footage` <dbl>, `Year Built` <dbl>,
## # Acreage <dbl>, `New Owned?` <chr>, `House Style` <chr>, `Covered Parking
## # Spots` <dbl>, `Misc Exterior` <chr>, `Has Pool?` <chr>, `Has Dock?` <chr>,
## # `Fenced Yard` <chr>, `Screened Porch?` <chr>, Amenities <chr>, `Golf
## # Course?` <chr>, `Fireplace?` <chr>, `Number of Fireplaces` <dbl>set.seed(1)
grpHouse <- kmeans(house[,c("List Price","Square Footage")], centers=3, nstart=10)
grpHouse## K-means clustering with 3 clusters of sizes 16, 148, 81
##
## Cluster means:
## List Price Square Footage
## 1 1534312.5 4942.812
## 2 451724.2 2438.318
## 3 797579.1 3782.148
##
## Clustering vector:
## [1] 2 2 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [38] 2 2 2 2 2 2 2 3 3 3 3 3 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [75] 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
## [112] 1 1 1 1 2 2 2 2 2 1 2 2 2 2 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [149] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 3
## [223] 2 2 3 2 2 3 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 3
##
## Within cluster sum of squares by cluster:
## [1] 8.951311e+11 1.775633e+12 1.062754e+12
## (between_SS / total_SS = 84.3 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"o=order(grpHouse$cluster)
data.frame(house$ID[o],grpHouse$cluster[o])## house.ID.o. grpHouse.cluster.o.
## 1 135 1
## 2 136 1
## 3 137 1
## 4 138 1
## 5 139 1
## 6 140 1
## 7 142 1
## 8 143 1
## 9 144 1
## 10 150 1
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## 245 245 3plot(house$`List Price`, house$`Square Footage`, type="n", xlim=c(119900,2000000), xlab="List Price", ylab="Square Footage")
text(x=house$`List Price`, y=house$`Square Footage`, labels=house$ID, col=grpHouse$cluster+1)
set.seed(1)
grpHouse <- kmeans(house[,c("List Price","Square Footage")], centers=7, nstart=10)
o=order(grpHouse$cluster)
data.frame(house$ID[o],grpHouse$cluster[o])## house.ID.o. grpHouse.cluster.o.
## 1 149 1
## 2 153 1
## 3 154 1
## 4 155 1
## 5 165 1
## 6 3 1
## 7 8 1
## 8 10 1
## 9 20 1
## 10 39 1
## 11 51 1
## 12 53 1
## 13 63 1
## 14 2 2
## 15 64 2
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## 245 245 7plot(house$`List Price`, house$`Square Footage`, type="n", xlim=c(119900,2000000), xlab="List Price", ylab="Square Footage")
text(x=house$`List Price`, y=house$`Square Footage`, labels=house$ID, col=rainbow(7)[grpHouse$cluster])
## HousePrices.csv
price$Brick<-ifelse(price$Brick=="Yes",1,0)
price$Neighborhood <- ifelse(price$Neighborhood == "East",0,
ifelse(price$Neighborhood == "North",1,2))
price[1:3,]## # A tibble: 3 x 8
## HomeID Price SqFt Bedrooms Bathrooms Offers Brick Neighborhood
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 114300 1790 2 2 2 0 0
## 2 2 114200 2030 4 2 3 0 0
## 3 3 114800 1740 3 2 1 0 0set.seed(1)
grpPrice <- kmeans(price[,c("Price","SqFt")], centers=3, nstart=10)
grpHouse## K-means clustering with 7 clusters of sizes 13, 30, 48, 83, 52, 9, 10
##
## Cluster means:
## List Price Square Footage
## 1 202676.9 1226.692
## 2 889835.9 4024.800
## 3 719980.1 3621.208
## 4 529085.0 2780.012
## 5 390506.4 2195.827
## 6 1703555.6 5337.444
## 7 1256678.5 4284.000
##
## Clustering vector:
## [1] 5 5 3 3 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [38] 4 4 4 4 4 4 4 3 3 3 3 2 5 4 4 4 4 4 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 4 3
## [75] 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6
## [112] 6 7 7 7 5 5 5 5 1 7 4 1 1 1 2 5 1 4 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 7 7
## [149] 7 5 1 5 5 1 1 5 5 5 5 5 5 5 5 1 5 5 5 5 5 4 4 4 1 4 4 4 5 4 1 1 4 4 4 4 4
## [186] 4 1 4 4 4 2 2 2 2 2 2 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 2
## [223] 4 4 2 5 5 2 5 5 5 5 5 5 5 5 5 5 5 6 6 6 7 7 7
##
## Within cluster sum of squares by cluster:
## [1] 19574867214 94603319520 117755598618 159035680150 99066014324
## [6] 244996767968 145702188467
## (between_SS / total_SS = 96.3 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"o=order(grpPrice$cluster)
data.frame(price$HomeID[o],grpPrice$cluster[o])## price.HomeID.o. grpPrice.cluster.o.
## 1 15 1
## 2 20 1
## 3 30 1
## 4 31 1
## 5 45 1
## 6 61 1
## 7 63 1
## 8 70 1
## 9 71 1
## 10 78 1
## 11 82 1
## 12 83 1
## 13 86 1
## 14 88 1
## 15 95 1
## 16 100 1
## 17 104 1
## 18 117 1
## 19 1 2
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## 128 127 3plot(price$`Price`, price$`SqFt`, type="n", xlim=c(69100,211200), xlab="Price", ylab="SqFt")
text(x=price$`Price`, y=price$`SqFt`, labels=price$HomeID, col=grpPrice$cluster+1)
set.seed(1)
grpPrice <- kmeans(price[,c("Price","SqFt")], centers=7, nstart=10)
o=order(grpPrice$cluster)
data.frame(price$HomeID[o],grpPrice$cluster[o])## price.HomeID.o. grpPrice.cluster.o.
## 1 30 1
## 2 31 1
## 3 61 1
## 4 82 1
## 5 86 1
## 6 104 1
## 7 117 1
## 8 1 2
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## 37 4 3
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## 128 122 7plot(price$`Price`, price$`SqFt`, type="n", xlim=c(69100,211200), xlab="Price", ylab="SqFt")
text(x=price$`Price`, y=price$`SqFt`, labels=price$HomeID, col=rainbow(7)[grpPrice$cluster])
Conclusion
The plots for Mount_Pleasant_Real_Estate.csv and the HousePrices.csv showed that the relationship between Square Footage vs. Price looked somewhat linear as price increased with larger square footage. In regards to clustering, it was noticeable that there were more clusters with the Mount_Pleasant_Real_Estate.csv. Some of the clusters so dense that it made it difficult to identify what the ID was for each house. In those cases, they clusters looked a bright blob of color. Since the clustering was closer for houses in the Mount_Pleasant_Real_Estate.csv, that seemed to indicate that square footage and pricing was similar among them. This was a good indication that the housing for the Mount_Pleasant_Real_Estate.csv is more defined and easy to differentiate in comparison to the HousePrices.csv