Data Analytics Final - Summer 2021
Thomas Laffey
7/4/2021
========================================================
load.libraries <- c('data.table','corrplot', 'psych', 'tidyverse','ggplot2','imputeMissings','dplyr')
install.lib <- load.libraries[!load.libraries %in% installed.packages()]
for(libs in install.lib) install.packages(libs, dependences = TRUE)
sapply(load.libraries, require, character = TRUE)## Loading required package: data.table## Loading required package: corrplot## corrplot 0.90 loaded## Loading required package: psych## Loading required package: tidyverse## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x ggplot2::%+%() masks psych::%+%()
## x ggplot2::alpha() masks psych::alpha()
## x dplyr::between() masks data.table::between()
## x dplyr::filter() masks stats::filter()
## x dplyr::first() masks data.table::first()
## x dplyr::lag() masks stats::lag()
## x dplyr::last() masks data.table::last()
## x purrr::transpose() masks data.table::transpose()## Loading required package: imputeMissings##
## Attaching package: 'imputeMissings'## The following object is masked from 'package:dplyr':
##
## compute## data.table corrplot psych tidyverse ggplot2
## TRUE TRUE TRUE TRUE TRUE
## imputeMissings dplyr
## TRUE TRUE# Not sure that I actually used the GGPlot
train_data=read.csv('train.csv')
str(train_data)## 'data.frame': 1460 obs. of 81 variables:
## $ Id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ MSSubClass : int 60 20 60 70 60 50 20 60 50 190 ...
## $ MSZoning : chr "RL" "RL" "RL" "RL" ...
## $ LotFrontage : int 65 80 68 60 84 85 75 NA 51 50 ...
## $ LotArea : int 8450 9600 11250 9550 14260 14115 10084 10382 6120 7420 ...
## $ Street : chr "Pave" "Pave" "Pave" "Pave" ...
## $ Alley : chr NA NA NA NA ...
## $ LotShape : chr "Reg" "Reg" "IR1" "IR1" ...
## $ LandContour : chr "Lvl" "Lvl" "Lvl" "Lvl" ...
## $ Utilities : chr "AllPub" "AllPub" "AllPub" "AllPub" ...
## $ LotConfig : chr "Inside" "FR2" "Inside" "Corner" ...
## $ LandSlope : chr "Gtl" "Gtl" "Gtl" "Gtl" ...
## $ Neighborhood : chr "CollgCr" "Veenker" "CollgCr" "Crawfor" ...
## $ Condition1 : chr "Norm" "Feedr" "Norm" "Norm" ...
## $ Condition2 : chr "Norm" "Norm" "Norm" "Norm" ...
## $ BldgType : chr "1Fam" "1Fam" "1Fam" "1Fam" ...
## $ HouseStyle : chr "2Story" "1Story" "2Story" "2Story" ...
## $ OverallQual : int 7 6 7 7 8 5 8 7 7 5 ...
## $ OverallCond : int 5 8 5 5 5 5 5 6 5 6 ...
## $ YearBuilt : int 2003 1976 2001 1915 2000 1993 2004 1973 1931 1939 ...
## $ YearRemodAdd : int 2003 1976 2002 1970 2000 1995 2005 1973 1950 1950 ...
## $ RoofStyle : chr "Gable" "Gable" "Gable" "Gable" ...
## $ RoofMatl : chr "CompShg" "CompShg" "CompShg" "CompShg" ...
## $ Exterior1st : chr "VinylSd" "MetalSd" "VinylSd" "Wd Sdng" ...
## $ Exterior2nd : chr "VinylSd" "MetalSd" "VinylSd" "Wd Shng" ...
## $ MasVnrType : chr "BrkFace" "None" "BrkFace" "None" ...
## $ MasVnrArea : int 196 0 162 0 350 0 186 240 0 0 ...
## $ ExterQual : chr "Gd" "TA" "Gd" "TA" ...
## $ ExterCond : chr "TA" "TA" "TA" "TA" ...
## $ Foundation : chr "PConc" "CBlock" "PConc" "BrkTil" ...
## $ BsmtQual : chr "Gd" "Gd" "Gd" "TA" ...
## $ BsmtCond : chr "TA" "TA" "TA" "Gd" ...
## $ BsmtExposure : chr "No" "Gd" "Mn" "No" ...
## $ BsmtFinType1 : chr "GLQ" "ALQ" "GLQ" "ALQ" ...
## $ BsmtFinSF1 : int 706 978 486 216 655 732 1369 859 0 851 ...
## $ BsmtFinType2 : chr "Unf" "Unf" "Unf" "Unf" ...
## $ BsmtFinSF2 : int 0 0 0 0 0 0 0 32 0 0 ...
## $ BsmtUnfSF : int 150 284 434 540 490 64 317 216 952 140 ...
## $ TotalBsmtSF : int 856 1262 920 756 1145 796 1686 1107 952 991 ...
## $ Heating : chr "GasA" "GasA" "GasA" "GasA" ...
## $ HeatingQC : chr "Ex" "Ex" "Ex" "Gd" ...
## $ CentralAir : chr "Y" "Y" "Y" "Y" ...
## $ Electrical : chr "SBrkr" "SBrkr" "SBrkr" "SBrkr" ...
## $ X1stFlrSF : int 856 1262 920 961 1145 796 1694 1107 1022 1077 ...
## $ X2ndFlrSF : int 854 0 866 756 1053 566 0 983 752 0 ...
## $ LowQualFinSF : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GrLivArea : int 1710 1262 1786 1717 2198 1362 1694 2090 1774 1077 ...
## $ BsmtFullBath : int 1 0 1 1 1 1 1 1 0 1 ...
## $ BsmtHalfBath : int 0 1 0 0 0 0 0 0 0 0 ...
## $ FullBath : int 2 2 2 1 2 1 2 2 2 1 ...
## $ HalfBath : int 1 0 1 0 1 1 0 1 0 0 ...
## $ BedroomAbvGr : int 3 3 3 3 4 1 3 3 2 2 ...
## $ KitchenAbvGr : int 1 1 1 1 1 1 1 1 2 2 ...
## $ KitchenQual : chr "Gd" "TA" "Gd" "Gd" ...
## $ TotRmsAbvGrd : int 8 6 6 7 9 5 7 7 8 5 ...
## $ Functional : chr "Typ" "Typ" "Typ" "Typ" ...
## $ Fireplaces : int 0 1 1 1 1 0 1 2 2 2 ...
## $ FireplaceQu : chr NA "TA" "TA" "Gd" ...
## $ GarageType : chr "Attchd" "Attchd" "Attchd" "Detchd" ...
## $ GarageYrBlt : int 2003 1976 2001 1998 2000 1993 2004 1973 1931 1939 ...
## $ GarageFinish : chr "RFn" "RFn" "RFn" "Unf" ...
## $ GarageCars : int 2 2 2 3 3 2 2 2 2 1 ...
## $ GarageArea : int 548 460 608 642 836 480 636 484 468 205 ...
## $ GarageQual : chr "TA" "TA" "TA" "TA" ...
## $ GarageCond : chr "TA" "TA" "TA" "TA" ...
## $ PavedDrive : chr "Y" "Y" "Y" "Y" ...
## $ WoodDeckSF : int 0 298 0 0 192 40 255 235 90 0 ...
## $ OpenPorchSF : int 61 0 42 35 84 30 57 204 0 4 ...
## $ EnclosedPorch: int 0 0 0 272 0 0 0 228 205 0 ...
## $ X3SsnPorch : int 0 0 0 0 0 320 0 0 0 0 ...
## $ ScreenPorch : int 0 0 0 0 0 0 0 0 0 0 ...
## $ PoolArea : int 0 0 0 0 0 0 0 0 0 0 ...
## $ PoolQC : chr NA NA NA NA ...
## $ Fence : chr NA NA NA NA ...
## $ MiscFeature : chr NA NA NA NA ...
## $ MiscVal : int 0 0 0 0 0 700 0 350 0 0 ...
## $ MoSold : int 2 5 9 2 12 10 8 11 4 1 ...
## $ YrSold : int 2008 2007 2008 2006 2008 2009 2007 2009 2008 2008 ...
## $ SaleType : chr "WD" "WD" "WD" "WD" ...
## $ SaleCondition: chr "Normal" "Normal" "Normal" "Abnorml" ...
## $ SalePrice : int 208500 181500 223500 140000 250000 143000 307000 200000 129900 118000 ...dim(train_data)## [1] 1460 81test_data=read.csv('test.csv')
#Let's take a look at the structure and potential for need for data cleansing
head(describe(train_data))## vars n mean sd median trimmed mad min max
## Id 1 1460 730.50 421.61 730.5 730.50 541.15 1 1460
## MSSubClass 2 1460 56.90 42.30 50.0 49.15 44.48 20 190
## MSZoning* 3 1460 4.03 0.63 4.0 4.06 0.00 1 5
## LotFrontage 4 1201 70.05 24.28 69.0 68.94 16.31 21 313
## LotArea 5 1460 10516.83 9981.26 9478.5 9563.28 2962.23 1300 215245
## Street* 6 1460 2.00 0.06 2.0 2.00 0.00 1 2
## range skew kurtosis se
## Id 1459 0.00 -1.20 11.03
## MSSubClass 170 1.40 1.56 1.11
## MSZoning* 4 -1.73 6.25 0.02
## LotFrontage 292 2.16 17.34 0.70
## LotArea 213945 12.18 202.26 261.22
## Street* 1 -15.49 238.01 0.00#head function helps us understand what type of data is in a given column
dim(train_data)## [1] 1460 81sum(is.na(train_data))## [1] 6965#Conclusion 1: Data is comprised of numeric and categorical data
#Conclusion 2: Need to replace NA values prior to analysis
#Creating separate data sets of 'Train' for cleansing and visualization purposes
train_data_num=select(train_data,is.numeric) ## Warning: Predicate functions must be wrapped in `where()`.
##
## # Bad
## data %>% select(is.numeric)
##
## # Good
## data %>% select(where(is.numeric))
##
## i Please update your code.
## This message is displayed once per session.#train_data_num will house numeric data for cleansing
train_data_char = select(train_data,is.character)## Warning: Predicate functions must be wrapped in `where()`.
##
## # Bad
## data %>% select(is.character)
##
## # Good
## data %>% select(where(is.character))
##
## i Please update your code.
## This message is displayed once per session.#train_data_car will host categorical data for cleansing
#Cleansing NAs
dim(train_data_num)## [1] 1460 38sum(is.na(train_data_num))## [1] 348#38 columns in data set
#Review of the structure prior to cleansing shows 348 NAs
#NAs in our numeric data set to equal the mean of given variable column
for(i in 1:38){train_data_num[is.na(train_data_num[,i]),i]<-mean(train_data_num[,i],na.rm=TRUE)}
#Ensure integrity of data cleanse of NAs in numeric data, sum should now be 0
dim(train_data_num)## [1] 1460 38sum(is.na(train_data_num))## [1] 0#43 columns in cat data set
dim(train_data_char)## [1] 1460 43sum(is.na(train_data_char))## [1] 6617#6617 NAs in categorical data to be cleansed
#Char data could be reasonable set to the mode, but for simplicity replacing with "None"
train_data_char[is.na(train_data_char)] = "None"
#Ensure integrity of NA cleanse in Cat data, sum should now be 0
summary(train_data_char)## MSZoning Street Alley LotShape
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## LandContour Utilities LotConfig LandSlope
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Neighborhood Condition1 Condition2 BldgType
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## HouseStyle RoofStyle RoofMatl Exterior1st
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Exterior2nd MasVnrType ExterQual ExterCond
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Foundation BsmtQual BsmtCond BsmtExposure
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## BsmtFinType1 BsmtFinType2 Heating HeatingQC
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## CentralAir Electrical KitchenQual Functional
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## FireplaceQu GarageType GarageFinish GarageQual
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## GarageCond PavedDrive PoolQC Fence
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## MiscFeature SaleType SaleCondition
## Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :characterdim(train_data_char)## [1] 1460 43sum(is.na(train_data_char))## [1] 0#Full train data set combine
clean_train_data=cbind(train_data_num,train_data_char)
dim(clean_train_data)## [1] 1460 81#Summary details of Sales Price
sum(is.na(train_data$SalePrice))## [1] 0summary(train_data$SalePrice)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 34900 129975 163000 180921 214000 755000boxplot(train_data$SalePrice/1000, xlab = "", ylab = "Price ($1000s)", main="Sale Price Box Plot")
hist(train_data$SalePrice/1000,xlab = "SalePrice ($1000s)", ylab = "Houses Sold", main="Sale Price Histogram")
#As a starting reference, we know that the mean housing price in Ames is ~$180k
#with a median Price of $163k.
#Mean>Median means Sales Price data is skewed right
#Otherwise the histogram shows unimodal distribution
#Finding Correlation between Sales Price and Numeric Variables
#Predictions from my experience of house hunting, the most important numeric
#factors will be sq footage of house and lot, quality of construction, and location
num_correlation=round(cor(train_data_num),2)
corrplot(num_correlation,method = "square")
#The largest and darkest blue squares are OverallQual + GrLivArea + X1stFlrSF + TotalBsmtSF
#Other Highly Correlated Factors = Garage Cars + +Garage Area + FullBath + TotRmsAbvGr
#Lesser factors of Interest = YearBuilt + LotArea
#This analysis mostly confirms my intuition
#I was somewhat surprised how garages factor in both in terms of # of spots and size
#I also had expected YearBuilt and LotArea to rate more
corr_df = data.frame(cor(train_data_num)) %>% select(SalePrice)
corr_df## SalePrice
## Id -0.02191672
## MSSubClass -0.08428414
## LotFrontage 0.33490085
## LotArea 0.26384335
## OverallQual 0.79098160
## OverallCond -0.07785589
## YearBuilt 0.52289733
## YearRemodAdd 0.50710097
## MasVnrArea 0.47524132
## BsmtFinSF1 0.38641981
## BsmtFinSF2 -0.01137812
## BsmtUnfSF 0.21447911
## TotalBsmtSF 0.61358055
## X1stFlrSF 0.60585218
## X2ndFlrSF 0.31933380
## LowQualFinSF -0.02560613
## GrLivArea 0.70862448
## BsmtFullBath 0.22712223
## BsmtHalfBath -0.01684415
## FullBath 0.56066376
## HalfBath 0.28410768
## BedroomAbvGr 0.16821315
## KitchenAbvGr -0.13590737
## TotRmsAbvGrd 0.53372316
## Fireplaces 0.46692884
## GarageYrBlt 0.47017745
## GarageCars 0.64040920
## GarageArea 0.62343144
## WoodDeckSF 0.32441344
## OpenPorchSF 0.31585623
## EnclosedPorch -0.12857796
## X3SsnPorch 0.04458367
## ScreenPorch 0.11144657
## PoolArea 0.09240355
## MiscVal -0.02118958
## MoSold 0.04643225
## YrSold -0.02892259
## SalePrice 1.00000000#My aim is to create a model with an adjusted R greater than 75% using the numeric var
#One first note I will make is that the strongest correlations are positive
#The factors I will consider first are OverallQual, GrLivArea, GarageCars,FullBath, TotRmsAbvGrd, YearBuilt
#I will first consider them as single variables then multi-regress
plot(SalePrice~OverallQual,train_data_num)
Model_Qual=lm(SalePrice~OverallQual,data=train_data_num)
summary(Model_Qual)##
## Call:
## lm(formula = SalePrice ~ OverallQual, data = train_data_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -198152 -29409 -1845 21463 396848
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -96206.1 5756.4 -16.71 <2e-16 ***
## OverallQual 45435.8 920.4 49.36 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 48620 on 1458 degrees of freedom
## Multiple R-squared: 0.6257, Adjusted R-squared: 0.6254
## F-statistic: 2437 on 1 and 1458 DF, p-value: < 2.2e-16abline(Model_Qual)
#The price increases by about $45k for every step up in quality rating
#Significant as an idividual factor, R-squared adj = 0.6254
plot(SalePrice~GrLivArea,train_data_num)
Model_Area=lm(SalePrice~GrLivArea,data=train_data_num)
summary(Model_Area)##
## Call:
## lm(formula = SalePrice ~ GrLivArea, data = train_data_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -462999 -29800 -1124 21957 339832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18569.026 4480.755 4.144 3.61e-05 ***
## GrLivArea 107.130 2.794 38.348 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 56070 on 1458 degrees of freedom
## Multiple R-squared: 0.5021, Adjusted R-squared: 0.5018
## F-statistic: 1471 on 1 and 1458 DF, p-value: < 2.2e-16abline(Model_Area)
#The price increases by about $100 for every Sqft above grade
#Significant as an idividual factor, R-squared adj = 0.5018
plot(SalePrice~GarageCars,train_data_num)
Model_Cars=lm(SalePrice~GarageCars,data=train_data_num)
summary(Model_Cars)##
## Call:
## lm(formula = SalePrice ~ GarageCars, data = train_data_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -209931 -36775 -6775 25303 490147
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60619 4102 14.78 <2e-16 ***
## GarageCars 68078 2138 31.84 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 61040 on 1458 degrees of freedom
## Multiple R-squared: 0.4101, Adjusted R-squared: 0.4097
## F-statistic: 1014 on 1 and 1458 DF, p-value: < 2.2e-16abline(Model_Cars)
#The price increases by about $68k for every additional garage for a car
#Significant as an idividual factor, R-squared adj = 0.4097
plot(SalePrice~FullBath,train_data_num)
Model_Baths=lm(SalePrice~FullBath,data=train_data_num)
summary(Model_Baths)##
## Call:
## lm(formula = SalePrice ~ FullBath, data = train_data_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -161085 -37236 -7111 22764 458067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 54388 5188 10.48 <2e-16 ***
## FullBath 80848 3127 25.85 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 65800 on 1458 degrees of freedom
## Multiple R-squared: 0.3143, Adjusted R-squared: 0.3139
## F-statistic: 668.4 on 1 and 1458 DF, p-value: < 2.2e-16abline(Model_Baths)
#The price increases by about $80k for every additional full bath
#Significant as an idividual factor, R-squared adj = 0.3139
plot(SalePrice~TotRmsAbvGrd,train_data_num)
Model_Rooms=lm(SalePrice~TotRmsAbvGrd,data=train_data_num)
summary(Model_Rooms)##
## Call:
## lm(formula = SalePrice ~ TotRmsAbvGrd, data = train_data_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -191844 -35500 -8620 28543 483242
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10896 7271 1.499 0.134
## TotRmsAbvGrd 26086 1082 24.099 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 67200 on 1458 degrees of freedom
## Multiple R-squared: 0.2849, Adjusted R-squared: 0.2844
## F-statistic: 580.8 on 1 and 1458 DF, p-value: < 2.2e-16abline(Model_Rooms)
#The price increases by about $26k for every additional room above grade
#Significant as an idividual factor, R-squared adj = 0.2844
plot(SalePrice~YearBuilt,train_data_num)
Model_Year=lm(SalePrice~YearBuilt,data=train_data_num)
summary(Model_Year)##
## Call:
## lm(formula = SalePrice ~ YearBuilt, data = train_data_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -144191 -40999 -15464 22685 542814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.530e+06 1.158e+05 -21.86 <2e-16 ***
## YearBuilt 1.375e+03 5.872e+01 23.42 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 67740 on 1458 degrees of freedom
## Multiple R-squared: 0.2734, Adjusted R-squared: 0.2729
## F-statistic: 548.7 on 1 and 1458 DF, p-value: < 2.2e-16abline(Model_Year)
#Looks like mucg weaker positive correlation in plot
#Significant as an idividual factor, R-squared adj = 0.2729
MR_Model_1 = lm(SalePrice~OverallQual+GrLivArea+GarageCars+TotRmsAbvGrd+YearBuilt,data=train_data_num)
summary(MR_Model_1)##
## Call:
## lm(formula = SalePrice ~ OverallQual + GrLivArea + GarageCars +
## TotRmsAbvGrd + YearBuilt, data = train_data_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -371359 -22033 -2213 18381 290572
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.747e+05 8.770e+04 -8.833 < 2e-16 ***
## OverallQual 2.273e+04 1.178e+03 19.302 < 2e-16 ***
## GrLivArea 6.212e+01 4.029e+00 15.418 < 2e-16 ***
## GarageCars 1.625e+04 1.886e+03 8.618 < 2e-16 ***
## TotRmsAbvGrd -2.104e+03 1.148e+03 -1.833 0.0669 .
## YearBuilt 3.590e+02 4.592e+01 7.818 1.02e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39750 on 1454 degrees of freedom
## Multiple R-squared: 0.7505, Adjusted R-squared: 0.7497
## F-statistic: 874.8 on 5 and 1454 DF, p-value: < 2.2e-16#In my first attempt at multi-regression, I failed to hit my goal threshold of 75%
#I can either add an additional variable or remove one or more.
#I will start by removing variable that do not show significant
#TotRmsAbvGrd has a high test statistic, so we cannot reject the null hypothesis that it is independent
MR_Model_2 = lm(SalePrice~OverallQual+GrLivArea+GarageCars+YearBuilt,data=train_data_num)
summary(MR_Model_2)##
## Call:
## lm(formula = SalePrice ~ OverallQual + GrLivArea + GarageCars +
## YearBuilt, data = train_data_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -360526 -21813 -1906 18354 298348
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.917e+05 8.728e+04 -9.071 < 2e-16 ***
## OverallQual 2.292e+04 1.174e+03 19.519 < 2e-16 ***
## GrLivArea 5.648e+01 2.603e+00 21.694 < 2e-16 ***
## GarageCars 1.612e+04 1.886e+03 8.546 < 2e-16 ***
## YearBuilt 3.646e+02 4.586e+01 7.951 3.69e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39780 on 1455 degrees of freedom
## Multiple R-squared: 0.7499, Adjusted R-squared: 0.7492
## F-statistic: 1091 on 4 and 1455 DF, p-value: < 2.2e-16#My adjusted R squared was relatively unimpacted by this update
#I will consider adding an additional variable
plot(SalePrice~TotalBsmtSF,train_data_num)
Model_BSMT=lm(SalePrice~TotalBsmtSF,data=train_data_num)
summary(Model_BSMT)##
## Call:
## lm(formula = SalePrice ~ TotalBsmtSF, data = train_data_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -582310 -39612 -14095 33315 420018
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 63430.629 4286.892 14.80 <2e-16 ***
## TotalBsmtSF 111.110 3.745 29.67 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 62750 on 1458 degrees of freedom
## Multiple R-squared: 0.3765, Adjusted R-squared: 0.3761
## F-statistic: 880.3 on 1 and 1458 DF, p-value: < 2.2e-16abline(Model_BSMT)
#The prcie increases by 111 dollars for every square foot of basement space
#Significant as an idividual factor, R-squared adj = 0.3761
MR_Model_3 = lm(SalePrice~OverallQual+GrLivArea+GarageCars+YearBuilt+TotalBsmtSF,data=train_data_num)
summary(MR_Model_3)##
## Call:
## lm(formula = SalePrice ~ OverallQual + GrLivArea + GarageCars +
## YearBuilt + TotalBsmtSF, data = train_data_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -476135 -20008 -2706 16431 285876
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.720e+05 8.485e+04 -7.920 4.69e-15 ***
## OverallQual 2.039e+04 1.156e+03 17.633 < 2e-16 ***
## GrLivArea 5.083e+01 2.564e+00 19.825 < 2e-16 ***
## GarageCars 1.451e+04 1.824e+03 7.957 3.52e-15 ***
## YearBuilt 3.014e+02 4.459e+01 6.760 1.99e-11 ***
## TotalBsmtSF 2.998e+01 2.821e+00 10.628 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 38330 on 1454 degrees of freedom
## Multiple R-squared: 0.768, Adjusted R-squared: 0.7672
## F-statistic: 962.4 on 5 and 1454 DF, p-value: < 2.2e-16#I was able to succesfully hit my threshold using BSMT Square Footage and FullBath
#More than 75% of variance from the regression line is explained.
#For one last test, I will layer a factor that has a negative correlation and seems unrelated to others
plot(SalePrice~EnclosedPorch,train_data_num)
Model_Porch=lm(SalePrice~EnclosedPorch,data=train_data_num)
summary(Model_Porch)##
## Call:
## lm(formula = SalePrice ~ EnclosedPorch, data = train_data_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -149279 -50300 -16640 30810 570410
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 184590.28 2191.67 84.224 < 2e-16 ***
## EnclosedPorch -167.13 33.76 -4.951 8.26e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 78810 on 1458 degrees of freedom
## Multiple R-squared: 0.01653, Adjusted R-squared: 0.01586
## F-statistic: 24.51 on 1 and 1458 DF, p-value: 8.256e-07abline(Model_Porch)
#Significant as an idividual factor, with a very low adjust R squared
MR_Model_4 = lm(SalePrice~OverallQual+GrLivArea+GarageCars+YearBuilt+TotalBsmtSF+EnclosedPorch,data=train_data_num)
summary(MR_Model_4)##
## Call:
## lm(formula = SalePrice ~ OverallQual + GrLivArea + GarageCars +
## YearBuilt + TotalBsmtSF + EnclosedPorch, data = train_data_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -476452 -19973 -2284 16810 285384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.414e+05 9.148e+04 -7.012 3.59e-12 ***
## OverallQual 2.049e+04 1.162e+03 17.634 < 2e-16 ***
## GrLivArea 5.085e+01 2.564e+00 19.830 < 2e-16 ***
## GarageCars 1.453e+04 1.824e+03 7.966 3.29e-15 ***
## YearBuilt 2.857e+02 4.791e+01 5.963 3.10e-09 ***
## TotalBsmtSF 2.999e+01 2.821e+00 10.631 < 2e-16 ***
## EnclosedPorch -1.614e+01 1.800e+01 -0.897 0.37
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 38340 on 1453 degrees of freedom
## Multiple R-squared: 0.7681, Adjusted R-squared: 0.7671
## F-statistic: 802.1 on 6 and 1453 DF, p-value: < 2.2e-16#When running the data with the Enclosed Porch, though the model maintaines a health Rsquared
#the new variable is not significant
#Validate MR_Model_3 by looking into the residuals
hist(MR_Model_3$residuals)
#The historgram looks essentually normal around 0 with some slight right skewing
#Final Model Equation:
#SalePrice = -672000 + 20390*OverallQual + 50.83*GrLivArea + 1451*GarageCars + 301.4*YearBuilt + 29.98*TotalBsmtSF
#The final adjusted R-squared is 76.72%
#I had visions of utilizing the character data set as factors, or dummy variables that could
#be layered on the current regression using solely numerical values, but I was unable
#to do so meaningfully
#Factoring Numeric data
train_data_factor=as.data.frame(unclass(train_data_char),stringsAsFactors = TRUE)
str(train_data_factor)## 'data.frame': 1460 obs. of 43 variables:
## $ MSZoning : Factor w/ 5 levels "C (all)","FV",..: 4 4 4 4 4 4 4 4 5 4 ...
## $ Street : Factor w/ 2 levels "Grvl","Pave": 2 2 2 2 2 2 2 2 2 2 ...
## $ Alley : Factor w/ 3 levels "Grvl","None",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ LotShape : Factor w/ 4 levels "IR1","IR2","IR3",..: 4 4 1 1 1 1 4 1 4 4 ...
## $ LandContour : Factor w/ 4 levels "Bnk","HLS","Low",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ Utilities : Factor w/ 2 levels "AllPub","NoSeWa": 1 1 1 1 1 1 1 1 1 1 ...
## $ LotConfig : Factor w/ 5 levels "Corner","CulDSac",..: 5 3 5 1 3 5 5 1 5 1 ...
## $ LandSlope : Factor w/ 3 levels "Gtl","Mod","Sev": 1 1 1 1 1 1 1 1 1 1 ...
## $ Neighborhood : Factor w/ 25 levels "Blmngtn","Blueste",..: 6 25 6 7 14 12 21 17 18 4 ...
## $ Condition1 : Factor w/ 9 levels "Artery","Feedr",..: 3 2 3 3 3 3 3 5 1 1 ...
## $ Condition2 : Factor w/ 8 levels "Artery","Feedr",..: 3 3 3 3 3 3 3 3 3 1 ...
## $ BldgType : Factor w/ 5 levels "1Fam","2fmCon",..: 1 1 1 1 1 1 1 1 1 2 ...
## $ HouseStyle : Factor w/ 8 levels "1.5Fin","1.5Unf",..: 6 3 6 6 6 1 3 6 1 2 ...
## $ RoofStyle : Factor w/ 6 levels "Flat","Gable",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ RoofMatl : Factor w/ 8 levels "ClyTile","CompShg",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ Exterior1st : Factor w/ 15 levels "AsbShng","AsphShn",..: 13 9 13 14 13 13 13 7 4 9 ...
## $ Exterior2nd : Factor w/ 16 levels "AsbShng","AsphShn",..: 14 9 14 16 14 14 14 7 16 9 ...
## $ MasVnrType : Factor w/ 4 levels "BrkCmn","BrkFace",..: 2 3 2 3 2 3 4 4 3 3 ...
## $ ExterQual : Factor w/ 4 levels "Ex","Fa","Gd",..: 3 4 3 4 3 4 3 4 4 4 ...
## $ ExterCond : Factor w/ 5 levels "Ex","Fa","Gd",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ Foundation : Factor w/ 6 levels "BrkTil","CBlock",..: 3 2 3 1 3 6 3 2 1 1 ...
## $ BsmtQual : Factor w/ 5 levels "Ex","Fa","Gd",..: 3 3 3 5 3 3 1 3 5 5 ...
## $ BsmtCond : Factor w/ 5 levels "Fa","Gd","None",..: 5 5 5 2 5 5 5 5 5 5 ...
## $ BsmtExposure : Factor w/ 5 levels "Av","Gd","Mn",..: 4 2 3 4 1 4 1 3 4 4 ...
## $ BsmtFinType1 : Factor w/ 7 levels "ALQ","BLQ","GLQ",..: 3 1 3 1 3 3 3 1 7 3 ...
## $ BsmtFinType2 : Factor w/ 7 levels "ALQ","BLQ","GLQ",..: 7 7 7 7 7 7 7 2 7 7 ...
## $ Heating : Factor w/ 6 levels "Floor","GasA",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ HeatingQC : Factor w/ 5 levels "Ex","Fa","Gd",..: 1 1 1 3 1 1 1 1 3 1 ...
## $ CentralAir : Factor w/ 2 levels "N","Y": 2 2 2 2 2 2 2 2 2 2 ...
## $ Electrical : Factor w/ 6 levels "FuseA","FuseF",..: 6 6 6 6 6 6 6 6 2 6 ...
## $ KitchenQual : Factor w/ 4 levels "Ex","Fa","Gd",..: 3 4 3 3 3 4 3 4 4 4 ...
## $ Functional : Factor w/ 7 levels "Maj1","Maj2",..: 7 7 7 7 7 7 7 7 3 7 ...
## $ FireplaceQu : Factor w/ 6 levels "Ex","Fa","Gd",..: 4 6 6 3 6 4 3 6 6 6 ...
## $ GarageType : Factor w/ 7 levels "2Types","Attchd",..: 2 2 2 6 2 2 2 2 6 2 ...
## $ GarageFinish : Factor w/ 4 levels "Fin","None","RFn",..: 3 3 3 4 3 4 3 3 4 3 ...
## $ GarageQual : Factor w/ 6 levels "Ex","Fa","Gd",..: 6 6 6 6 6 6 6 6 2 3 ...
## $ GarageCond : Factor w/ 6 levels "Ex","Fa","Gd",..: 6 6 6 6 6 6 6 6 6 6 ...
## $ PavedDrive : Factor w/ 3 levels "N","P","Y": 3 3 3 3 3 3 3 3 3 3 ...
## $ PoolQC : Factor w/ 4 levels "Ex","Fa","Gd",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ Fence : Factor w/ 5 levels "GdPrv","GdWo",..: 5 5 5 5 5 3 5 5 5 5 ...
## $ MiscFeature : Factor w/ 5 levels "Gar2","None",..: 2 2 2 2 2 4 2 4 2 2 ...
## $ SaleType : Factor w/ 9 levels "COD","Con","ConLD",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ SaleCondition: Factor w/ 6 levels "Abnorml","AdjLand",..: 5 5 5 1 5 5 5 5 1 5 ...factor_analysis=cbind(train_data_factor,select(train_data,SalePrice))
#When attempting to convert from factor to numeric was left with empty
#data frame. No analysis could be done. My thought was this would allow dummy
#variables to trigger a response in the price
#Cleanse Test Data for use with model
test_data_num=select(test_data,is.numeric)
#test_data_num will house numeric data for cleansing
test_data_char = select(test_data,is.character)
#test_data_car will host categorical data for cleansing
#Cleansing NAs
dim(test_data_num)## [1] 1459 37sum(is.na(test_data_num))## [1] 330#In the console the formula below functioned appropriately but not knitting now
#NAs in our numeric data set to equal the mean of given variable column
#for(i in 1:38){test_data_num[is.na(test_data_num[,i]),i]<-mean(test_data_nu#m[,i],na.rm=TRUE)}
#Ensure integrity of data cleanse of NAs in numeric data
test_data_num## Id MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt
## 1 1461 20 80 11622 5 6 1961
## 2 1462 20 81 14267 6 6 1958
## 3 1463 60 74 13830 5 5 1997
## 4 1464 60 78 9978 6 6 1998
## 5 1465 120 43 5005 8 5 1992
## 6 1466 60 75 10000 6 5 1993
## 7 1467 20 NA 7980 6 7 1992
## 8 1468 60 63 8402 6 5 1998
## 9 1469 20 85 10176 7 5 1990
## 10 1470 20 70 8400 4 5 1970
## 11 1471 120 26 5858 7 5 1999
## 12 1472 160 21 1680 6 5 1971
## 13 1473 160 21 1680 5 5 1971
## 14 1474 160 24 2280 6 6 1975
## 15 1475 120 24 2280 7 6 1975
## 16 1476 60 102 12858 9 5 2009
## 17 1477 20 94 12883 8 5 2009
## 18 1478 20 90 11520 9 5 2005
## 19 1479 20 79 14122 8 5 2005
## 20 1480 20 110 14300 9 5 2003
## 21 1481 60 105 13650 8 5 2002
## 22 1482 120 41 7132 8 5 2006
## 23 1483 20 100 18494 6 5 2005
## 24 1484 120 43 3203 7 5 2006
## 25 1485 80 67 13300 7 5 2004
## 26 1486 60 63 8577 7 5 2004
## 27 1487 60 60 17433 8 5 1998
## 28 1488 20 73 8987 8 5 2005
## 29 1489 20 92 9215 7 5 2009
## 30 1490 20 84 10440 6 5 2005
## 31 1491 60 70 11920 7 5 2004
## 32 1492 30 70 9800 5 5 1920
## 33 1493 20 39 15410 6 6 1974
## 34 1494 60 85 13143 8 5 1993
## 35 1495 60 88 11134 8 5 1992
## 36 1496 120 25 4835 7 5 2004
## 37 1497 160 39 3515 7 5 2004
## 38 1498 160 30 3215 7 5 2004
## 39 1499 160 24 2544 7 5 2004
## 40 1500 160 24 2544 6 5 2005
## 41 1501 160 NA 2980 6 5 2000
## 42 1502 160 NA 2403 7 5 2003
## 43 1503 20 57 12853 8 5 2010
## 44 1504 60 68 7379 8 5 2000
## 45 1505 20 80 8000 7 5 2002
## 46 1506 20 NA 10456 6 6 1967
## 47 1507 60 80 10791 6 5 1993
## 48 1508 50 NA 18837 6 5 1978
## 49 1509 60 80 9600 6 6 1971
## 50 1510 20 80 9600 5 5 1966
## 51 1511 20 90 9900 5 5 1966
## 52 1512 20 88 9680 5 6 1967
## 53 1513 80 NA 10600 6 5 1964
## 54 1514 90 98 13260 5 6 1962
## 55 1515 50 68 9724 5 7 1952
## 56 1516 50 120 17360 6 6 1949
## 57 1517 85 75 11380 6 8 1966
## 58 1518 90 70 8267 5 5 1958
## 59 1519 20 70 8197 7 5 2003
## 60 1520 20 NA 8050 5 5 1959
## 61 1521 20 87 10725 5 5 1959
## 62 1522 20 80 10032 6 5 1959
## 63 1523 50 60 8382 4 5 1956
## 64 1524 20 60 10950 5 7 1952
## 65 1525 20 119 10895 5 6 1955
## 66 1526 190 70 13587 5 5 1958
## 67 1527 30 65 7898 4 7 1920
## 68 1528 50 60 8064 6 8 1948
## 69 1529 20 81 7635 5 6 1960
## 70 1530 20 80 9760 6 7 1963
## 71 1531 50 60 4800 4 5 1900
## 72 1532 30 56 4485 5 7 1920
## 73 1533 20 69 5805 5 7 1957
## 74 1534 45 50 6900 6 7 1938
## 75 1535 50 69 11851 5 7 1948
## 76 1536 50 NA 8239 5 6 1920
## 77 1537 30 68 9656 2 2 1923
## 78 1538 70 60 9600 8 9 1900
## 79 1539 70 50 9000 8 9 1890
## 80 1540 190 100 9045 5 3 1910
## 81 1541 70 60 10560 6 7 1922
## 82 1542 50 53 5830 5 6 1950
## 83 1543 75 NA 7793 7 7 1922
## 84 1544 30 50 5000 4 7 1925
## 85 1545 50 50 6000 6 7 1939
## 86 1546 50 50 6000 6 6 1940
## 87 1547 50 53 6360 5 6 1942
## 88 1548 50 50 6000 6 7 1948
## 89 1549 50 52 6240 5 7 1936
## 90 1550 50 52 6240 5 5 1930
## 91 1551 30 51 6120 6 5 1923
## 92 1552 50 57 8094 4 5 1915
## 93 1553 70 60 12900 6 8 1912
## 94 1554 70 52 3068 6 8 1920
## 95 1555 20 100 15263 5 5 1959
## 96 1556 50 72 10632 5 3 1917
## 97 1557 190 60 9900 5 4 1915
## 98 1558 50 65 6001 6 5 1940
## 99 1559 70 NA 6449 4 1 1907
## 100 1560 190 60 6048 5 7 1910
## 101 1561 90 72 10773 4 3 1967
## 102 1562 20 65 7800 5 7 1966
## 103 1563 20 65 7832 5 5 1968
## 104 1564 90 NA 7424 5 5 1978
## 105 1565 60 86 11227 5 5 1968
## 106 1566 20 NA 20062 7 7 1977
## 107 1567 30 94 9259 4 4 1927
## 108 1568 60 NA 17082 6 5 1978
## 109 1569 50 124 18600 3 4 1938
## 110 1570 20 65 11479 6 7 1950
## 111 1571 50 50 9350 4 6 1947
## 112 1572 20 75 9525 5 7 1954
## 113 1573 20 44 17485 7 5 2009
## 114 1574 20 NA 11200 5 3 1964
## 115 1575 20 83 11980 7 5 1987
## 116 1576 60 87 12361 6 7 1993
## 117 1577 20 64 7360 7 5 2010
## 118 1578 50 82 14235 6 8 1900
## 119 1579 85 82 11105 5 5 1996
## 120 1580 60 NA 9337 7 5 1997
## 121 1581 20 38 15240 5 8 1977
## 122 1582 20 68 7480 5 6 1972
## 123 1583 20 80 10389 8 5 2003
## 124 1584 60 75 9375 7 5 1997
## 125 1585 120 NA 4435 6 5 2003
## 126 1586 30 67 8777 3 6 1945
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## 128 1588 20 60 10044 5 6 1968
## 129 1589 50 89 11792 4 5 1948
## 130 1590 80 65 6305 6 6 1975
## 131 1591 20 64 6410 4 5 1958
## 132 1592 30 67 4853 5 6 1924
## 133 1593 30 NA 7890 6 6 1939
## 134 1594 90 60 7200 4 6 1967
## 135 1595 50 51 9839 5 2 1931
## 136 1596 50 78 10452 7 6 1941
## 137 1597 90 78 15600 5 6 1950
## 138 1598 80 85 19645 7 6 1994
## 139 1599 120 35 3907 8 6 1989
## 140 1600 120 35 3907 8 5 1989
## 141 1601 30 58 8154 2 5 1941
## 142 1602 50 50 9140 6 5 1921
## 143 1603 30 66 8712 4 7 1896
## 144 1604 120 44 3811 8 5 2004
## 145 1605 60 85 11050 8 5 1998
## 146 1606 80 74 9620 6 7 1977
## 147 1607 90 NA 12760 6 5 1976
## 148 1608 20 88 11896 7 5 2008
## 149 1609 20 73 9803 7 5 2009
## 150 1610 60 73 9802 5 5 2006
## 151 1611 20 85 15300 5 5 1965
## 152 1612 20 93 10114 5 5 2004
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## 154 1614 120 31 2394 5 6 1973
## 155 1615 160 21 1476 4 7 1970
## 156 1616 160 21 1900 4 4 1970
## 157 1617 160 21 1890 4 6 1972
## 158 1618 20 50 6953 5 7 1971
## 159 1619 20 76 12887 5 7 1984
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## 162 1622 50 68 10544 5 5 1969
## 163 1623 20 76 9892 8 5 1994
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## 166 1626 20 85 10200 6 5 1974
## 167 1627 60 88 10179 6 5 1997
## 168 1628 60 NA 11792 8 5 2003
## 169 1629 80 60 8400 7 5 1996
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## 195 1655 120 24 2280 7 5 1975
## 196 1656 160 24 2349 6 5 1977
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## 199 1659 120 24 2104 7 6 1976
## 200 1660 20 NA 10710 5 7 1966
## 201 1661 60 110 14257 9 5 2007
## 202 1662 60 95 12350 9 5 2009
## 203 1663 20 95 12350 9 5 2008
## 204 1664 20 105 13693 10 5 2007
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## 206 1666 20 129 16870 8 5 2004
## 207 1667 60 59 23303 8 5 2007
## 208 1668 20 87 10367 9 5 2008
## 209 1669 20 77 10872 9 5 2006
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## 211 1671 20 90 12878 7 5 2003
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## 213 1673 60 96 13262 8 5 2003
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## 215 1675 120 47 6904 6 5 2005
## 216 1676 120 34 5122 6 5 2005
## 217 1677 120 80 10307 7 5 2007
## 218 1678 20 100 14836 10 5 2004
## 219 1679 20 117 15262 8 5 2003
## 220 1680 120 44 7390 9 5 2008
## 221 1681 120 48 6472 9 5 2008
## 222 1682 20 129 16770 8 5 2002
## 223 1683 120 48 3480 7 5 2003
## 224 1684 60 63 10928 7 5 2005
## 225 1685 60 57 8918 6 5 2005
## 226 1686 120 43 3182 7 5 2005
## 227 1687 80 59 9434 7 5 2004
## 228 1688 60 62 7984 7 5 2004
## 229 1689 60 61 10125 7 5 2004
## 230 1690 60 NA 8965 7 5 2003
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## 233 1693 80 61 9734 7 5 2004
## 234 1694 60 42 8433 6 5 2000
## 235 1695 80 62 7750 7 5 1999
## 236 1696 60 NA 15896 7 5 1999
## 237 1697 80 64 7848 7 6 1999
## 238 1698 60 106 12720 8 5 2000
## 239 1699 20 NA 10750 8 5 1994
## 240 1700 60 79 9085 7 5 1995
## 241 1701 60 NA 11692 8 5 1993
## 242 1702 20 86 11194 8 5 2008
## 243 1703 20 78 10206 8 5 2008
## 244 1704 20 85 10130 8 5 2007
## 245 1705 20 76 9139 8 5 2006
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## 247 1707 20 90 7993 7 5 2008
## 248 1708 20 72 8640 7 5 2008
## 249 1709 20 112 12606 9 5 2007
## 250 1710 20 75 7500 8 5 2006
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## 252 1712 20 65 8125 8 5 2008
## 253 1713 20 85 10625 7 5 2006
## 254 1714 20 68 8736 7 5 2003
## 255 1715 60 65 8127 7 5 2003
## 256 1716 20 80 9605 7 6 2007
## 257 1717 20 63 7500 7 5 2006
## 258 1718 20 63 7500 6 5 2004
## 259 1719 60 96 10628 7 5 2004
## 260 1720 20 76 10141 8 5 2004
## 261 1721 20 63 13072 7 5 2004
## 262 1722 20 63 13072 5 5 2004
## 263 1723 20 60 12450 5 5 2003
## 264 1724 20 61 7328 7 5 2008
## 265 1725 60 43 11492 7 5 1996
## 266 1726 60 70 7703 6 6 1992
## 267 1727 120 50 7175 6 5 1990
## 268 1728 60 70 9109 7 5 1994
## 269 1729 60 NA 10274 6 7 1986
## 270 1730 90 75 8250 6 7 1981
## 271 1731 20 63 9750 5 5 1962
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## 273 1733 20 NA 9079 5 5 1961
## 274 1734 20 NA 9316 5 5 1965
## 275 1735 20 NA 7791 5 8 1963
## 276 1736 20 65 7150 5 6 1962
## 277 1737 20 NA 15676 8 8 1980
## 278 1738 60 NA 11949 7 8 1991
## 279 1739 160 32 2880 7 5 2004
## 280 1740 120 NA 3830 6 5 2008
## 281 1741 120 NA 4217 6 5 2008
## 282 1742 160 34 2998 6 5 2000
## 283 1743 160 35 3768 7 5 1999
## 284 1744 20 NA 14694 8 9 1977
## 285 1745 20 110 15417 7 5 1981
## 286 1746 80 80 9600 8 5 1976
## 287 1747 60 NA 12732 7 6 1974
## 288 1748 60 80 10400 6 7 1967
## 289 1749 20 80 9600 5 5 1969
## 290 1750 20 75 9000 6 5 1969
## 291 1751 60 NA 13774 7 7 1977
## 292 1752 20 62 7130 5 6 1967
## 293 1753 20 80 9600 5 5 1967
## 294 1754 60 80 9600 7 5 1974
## 295 1755 85 NA 16500 6 5 1971
## 296 1756 20 60 7436 4 7 1960
## 297 1757 20 65 8125 5 7 1959
## 298 1758 20 NA 9450 4 5 1957
## 299 1759 20 NA 13495 5 6 1956
## 300 1760 20 85 9350 5 6 1961
## 301 1761 20 115 10500 5 5 1964
## 302 1762 60 NA 8970 5 6 1965
## 303 1763 80 85 11475 6 6 1961
## 304 1764 20 68 9768 5 5 1955
## 305 1765 20 90 9900 6 5 1967
## 306 1766 20 92 10573 6 6 1961
## 307 1767 20 80 14695 6 8 1966
## 308 1768 20 73 8760 5 7 1956
## 309 1769 20 NA 12285 7 6 1960
## 310 1770 20 66 9240 6 8 1959
## 311 1771 20 70 8750 5 5 1956
## 312 1772 20 70 8750 5 5 1955
## 313 1773 20 80 10400 4 5 1956
## 314 1774 90 76 9482 5 4 1958
## 315 1775 20 53 8128 6 7 1954
## 316 1776 20 67 13070 5 5 1951
## 317 1777 30 80 8480 5 6 1945
## 318 1778 20 60 7626 5 8 1952
## 319 1779 20 75 9533 5 5 1953
## 320 1780 60 78 11419 7 7 1948
## 321 1781 20 60 9600 5 6 1950
## 322 1782 20 53 5470 3 5 1958
## 323 1783 60 60 10800 5 7 1939
## 324 1784 70 80 8146 4 8 1900
## 325 1785 30 60 10230 5 7 1925
## 326 1786 50 60 10410 4 5 1915
## 327 1787 70 60 7200 7 9 1910
## 328 1788 30 90 5400 2 5 1940
## 329 1789 30 60 10800 5 7 1920
## 330 1790 30 60 10800 3 5 1890
## 331 1791 90 81 9671 6 5 1969
## 332 1792 20 83 10143 6 7 1963
## 333 1793 20 77 11500 6 4 1967
## 334 1794 20 62 8010 6 9 1958
## 335 1795 20 90 10454 6 6 1957
## 336 1796 20 80 9000 6 6 1958
## 337 1797 50 60 8064 6 5 1950
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## 956 2416 20 60 10950 6 6 1952
## 957 2417 20 68 7942 6 7 1953
## 958 2418 20 71 8540 5 8 1956
## 959 2419 50 55 7150 4 4 1955
## 960 2420 20 70 8400 5 5 1955
## 961 2421 20 75 9532 4 6 1953
## 962 2422 20 NA 15783 5 5 1952
## 963 2423 50 60 14190 4 7 1890
## 964 2424 50 NA 12099 5 6 1953
## 965 2425 70 113 21281 5 4 1935
## 966 2426 50 60 10284 4 7 1925
## 967 2427 70 60 10800 5 9 1895
## 968 2428 80 93 10090 7 5 1963
## 969 2429 20 75 8700 5 7 1961
## 970 2430 20 66 8300 6 6 1968
## 971 2431 20 60 7200 5 3 1950
## 972 2432 20 NA 7500 5 7 1959
## 973 2433 20 70 7315 5 6 1958
## 974 2434 20 70 7903 5 5 1960
## 975 2435 20 80 8000 5 5 1960
## 976 2436 20 70 7000 5 6 1961
## 977 2437 20 60 6600 5 5 1962
## 978 2438 85 66 6760 5 5 1962
## 979 2439 30 60 6978 5 7 1926
## 980 2440 50 50 6000 6 6 1927
## 981 2441 30 56 4480 5 5 1922
## 982 2442 30 56 3153 5 6 1920
## 983 2443 30 60 7200 5 8 1940
## 984 2444 50 120 9000 5 8 1900
## 985 2445 50 50 5925 4 6 1900
## 986 2446 70 57 9639 4 8 1900
## 987 2447 70 NA 10337 8 9 1910
## 988 2448 70 53 9863 6 6 1927
## 989 2449 70 35 4571 5 7 1910
## 990 2450 50 56 8398 6 8 1910
## 991 2451 70 60 3600 5 7 1930
## 992 2452 75 75 13500 7 8 1879
## 993 2453 20 52 8626 4 6 1956
## 994 2454 80 76 11800 4 7 1949
## 995 2455 40 55 6854 5 7 1925
## 996 2456 50 55 8674 5 6 1950
## 997 2457 50 50 6125 5 7 1939
## 998 2458 70 50 6000 6 6 1939
## 999 2459 45 51 6120 5 7 1939
## 1000 2460 50 NA 6240 6 5 1938
## 1001 2461 50 52 6240 5 8 1939
## 1002 2462 50 52 6240 5 7 1930
## 1003 2463 50 51 6120 5 6 1926
## 1004 2464 70 47 7755 6 8 1918
## 1005 2465 50 60 8850 6 7 1920
## 1006 2466 50 60 8550 5 5 1926
## 1007 2467 70 NA 5700 7 6 1929
## 1008 2468 45 40 5680 5 4 1901
## 1009 2469 50 40 5680 5 3 1901
## 1010 2470 80 120 13200 6 6 1963
## 1011 2471 60 60 9780 7 9 1950
## 1012 2472 50 60 10320 6 5 1915
## 1013 2473 190 52 4330 4 6 1958
## 1014 2474 50 60 10320 4 1 1910
## 1015 2475 70 107 12888 7 8 1937
## 1016 2476 190 59 4484 5 6 1942
## 1017 2477 190 75 11235 5 5 1963
## 1018 2478 85 75 11235 5 5 1964
## 1019 2479 20 62 14299 4 3 1964
## 1020 2480 80 65 14149 5 8 1964
## 1021 2481 20 NA 11677 5 4 1966
## 1022 2482 190 70 8425 5 6 1971
## 1023 2483 20 86 8665 5 5 1968
## 1024 2484 20 NA 8398 5 5 1967
## 1025 2485 20 NA 8169 5 7 1966
## 1026 2486 20 81 14175 5 5 1956
## 1027 2487 60 99 16779 5 4 1920
## 1028 2488 50 70 6960 7 8 1940
## 1029 2489 20 91 11375 6 5 1954
## 1030 2490 20 85 13770 5 6 1958
## 1031 2491 20 NA 9000 4 7 1945
## 1032 2492 20 NA 11075 6 5 1984
## 1033 2493 20 NA 17541 5 7 1948
## 1034 2494 20 NA 22692 5 5 1953
## 1035 2495 20 84 17808 4 5 1946
## 1036 2496 20 102 12671 6 7 1954
## 1037 2497 50 70 10512 6 6 1954
## 1038 2498 20 60 5400 5 5 1958
## 1039 2499 30 NA 11515 4 5 1958
## 1040 2500 120 39 3869 5 6 1984
## 1041 2501 20 58 9280 5 6 1951
## 1042 2502 50 60 11100 5 6 1951
## 1043 2503 50 50 7550 4 5 1920
## 1044 2504 50 104 23920 6 5 1984
## 1045 2505 60 75 9317 7 5 1994
## 1046 2506 20 71 9178 8 5 2007
## 1047 2507 20 93 10481 8 5 2006
## 1048 2508 20 66 10235 8 5 2007
## 1049 2509 20 75 11750 7 5 2005
## 1050 2510 20 73 8760 8 5 2006
## 1051 2511 20 64 7242 7 5 2005
## 1052 2512 60 78 9316 7 5 2005
## 1053 2513 60 NA 8883 7 5 1988
## 1054 2514 20 155 20064 8 6 1976
## 1055 2515 20 NA 14217 5 5 1994
## 1056 2516 60 57 10021 6 6 1997
## 1057 2517 20 60 8428 5 7 1994
## 1058 2518 20 70 16561 5 5 1996
## 1059 2519 60 47 10820 7 5 1999
## 1060 2520 60 43 12352 7 5 1998
## 1061 2521 60 68 9543 7 5 2001
## 1062 2522 60 NA 8826 7 5 2000
## 1063 2523 20 97 11800 5 5 1974
## 1064 2524 80 59 8660 5 5 1976
## 1065 2525 80 72 9720 5 7 1977
## 1066 2526 20 45 8982 5 5 1977
## 1067 2527 20 39 16300 5 4 1977
## 1068 2528 20 75 9675 5 6 1975
## 1069 2529 20 60 7200 5 5 1972
## 1070 2530 20 60 7200 4 8 1972
## 1071 2531 20 NA 11354 7 5 2000
## 1072 2532 60 70 8749 7 5 2003
## 1073 2533 60 65 8158 7 5 2002
## 1074 2534 20 73 11927 8 5 1994
## 1075 2535 60 NA 12728 8 5 2001
## 1076 2536 60 NA 15295 7 5 1996
## 1077 2537 20 73 17227 8 5 1999
## 1078 2538 60 70 8145 7 5 2007
## 1079 2539 20 65 8769 7 5 2005
## 1080 2540 20 64 8334 6 5 2006
## 1081 2541 60 64 8333 7 5 2006
## 1082 2542 60 64 9045 5 5 2005
## 1083 2543 85 75 9825 5 5 1967
## 1084 2544 20 67 8308 4 6 1963
## 1085 2545 50 74 16287 5 6 1925
## 1086 2546 20 80 8240 6 6 1960
## 1087 2547 80 65 6285 6 6 1976
## 1088 2548 90 NA 9555 5 6 1979
## 1089 2549 60 60 7023 5 5 2005
## 1090 2550 20 128 39290 10 5 2008
## 1091 2551 180 35 3675 6 5 2005
## 1092 2552 20 64 6400 5 7 1959
## 1093 2553 90 74 6882 4 3 1955
## 1094 2554 90 52 8741 5 6 1946
## 1095 2555 40 62 10042 6 8 1920
## 1096 2556 20 60 8172 4 5 1955
## 1097 2557 20 60 8172 4 5 1955
## 1098 2558 90 60 10890 5 6 1923
## 1099 2559 30 54 7223 5 5 1926
## 1100 2560 50 51 6821 6 7 1921
## 1101 2561 70 63 4000 7 8 1930
## 1102 2562 70 53 6720 6 7 1921
## 1103 2563 70 53 7155 6 8 1926
## 1104 2564 70 60 7230 7 7 1927
## 1105 2565 20 126 13108 5 5 1951
## 1106 2566 50 110 7810 4 6 1930
## 1107 2567 190 79 6221 5 5 1941
## 1108 2568 20 NA 25485 6 4 1960
## 1109 2569 20 NA 21579 6 6 1968
## 1110 2570 160 24 1782 6 6 1980
## 1111 2571 20 NA 17871 4 5 1995
## 1112 2572 120 35 3907 8 5 1988
## 1113 2573 20 NA 20693 7 5 1971
## 1114 2574 20 70 18044 8 5 1986
## 1115 2575 50 50 7000 6 7 1940
## 1116 2576 50 50 7288 5 7 1925
## 1117 2577 70 50 9060 5 6 1923
## 1118 2578 30 46 3672 5 7 1922
## 1119 2579 50 64 11067 2 4 1939
## 1120 2580 190 75 8250 5 6 1895
## 1121 2581 20 65 6565 4 6 1957
## 1122 2582 30 60 6060 5 9 1930
## 1123 2583 120 59 5568 8 5 2006
## 1124 2584 85 NA 12150 6 6 1979
## 1125 2585 20 80 10000 5 5 2002
## 1126 2586 20 44 12864 7 5 2002
## 1127 2587 20 NA 9928 7 5 1991
## 1128 2588 80 NA 8750 7 6 1975
## 1129 2589 85 82 8410 6 6 1974
## 1130 2590 120 46 4054 7 6 1987
## 1131 2591 20 149 19958 6 8 1958
## 1132 2592 20 67 8368 7 5 2006
## 1133 2593 20 68 8298 8 5 2006
## 1134 2594 20 42 10331 7 7 1985
## 1135 2595 20 NA 6718 8 5 2001
## 1136 2596 20 80 11305 8 5 2002
## 1137 2597 20 NA 7777 6 5 1996
## 1138 2598 60 NA 11800 7 5 2003
## 1139 2599 20 85 12633 10 5 2006
## 1140 2600 20 200 43500 3 5 1953
## 1141 2601 120 62 6710 6 5 1996
## 1142 2602 160 21 1504 4 4 1972
## 1143 2603 180 NA 1533 4 5 1970
## 1144 2604 160 21 1495 4 6 1970
## 1145 2605 160 21 1890 4 3 1976
## 1146 2606 85 72 9129 5 5 1977
## 1147 2607 80 NA 15957 6 6 1977
## 1148 2608 20 61 33983 5 6 1977
## 1149 2609 60 68 8286 5 7 1977
## 1150 2610 85 50 6723 5 7 1971
## 1151 2611 20 124 27697 4 3 1961
## 1152 2612 20 NA 11000 5 6 1976
## 1153 2613 20 65 11625 5 7 1983
## 1154 2614 20 62 10447 5 6 1984
## 1155 2615 20 NA 11027 6 5 1954
## 1156 2616 20 85 10533 6 6 1956
## 1157 2617 20 NA 11765 5 5 1957
## 1158 2618 20 NA 39384 6 6 1957
## 1159 2619 20 90 11727 7 6 1969
## 1160 2620 60 60 8238 6 5 1997
## 1161 2621 60 NA 13041 6 5 1995
## 1162 2622 60 54 9783 6 5 1996
## 1163 2623 60 50 13128 8 5 2005
## 1164 2624 60 42 13751 7 5 2005
## 1165 2625 160 68 13108 8 5 1994
## 1166 2626 20 NA 8076 6 6 1993
## 1167 2627 120 30 3701 8 5 1987
## 1168 2628 60 59 16023 9 5 2005
## 1169 2629 60 60 18062 10 5 2006
## 1170 2630 60 63 12292 9 5 2006
## 1171 2631 60 82 16052 10 5 2006
## 1172 2632 60 92 15922 9 5 2005
## 1173 2633 120 60 8147 9 5 2005
## 1174 2634 20 90 18261 9 5 2005
## 1175 2635 85 NA 10464 6 6 1980
## 1176 2636 60 81 10530 7 5 1978
## 1177 2637 85 NA 9927 7 5 1976
## 1178 2638 60 75 9512 7 5 2005
## 1179 2639 80 81 10530 6 6 1975
## 1180 2640 20 80 10000 6 6 1974
## 1181 2641 20 60 7200 4 4 1971
## 1182 2642 120 26 8773 6 5 2002
## 1183 2643 160 24 2760 6 5 1973
## 1184 2644 160 24 2160 5 6 1973
## 1185 2645 160 21 1890 6 7 1972
## 1186 2646 160 21 1680 6 5 1972
## 1187 2647 160 21 1680 6 5 1972
## 1188 2648 120 53 4043 6 5 1975
## 1189 2649 20 65 7514 5 7 1967
## 1190 2650 120 24 2280 7 7 1976
## 1191 2651 160 24 2179 6 5 1976
## 1192 2652 60 72 16387 9 5 2006
## 1193 2653 20 110 16163 8 5 2004
## 1194 2654 20 108 12228 7 5 2006
## 1195 2655 20 120 14780 9 5 2005
## 1196 2656 60 120 13975 9 5 2005
## 1197 2657 60 82 9942 9 5 2005
## 1198 2658 60 103 12867 8 5 2005
## 1199 2659 60 82 10672 8 5 2006
## 1200 2660 60 82 11643 8 5 2005
## 1201 2661 20 121 13758 9 5 2005
## 1202 2662 20 131 14828 9 5 2004
## 1203 2663 60 NA 13215 8 5 2004
## 1204 2664 120 48 5911 9 5 2005
## 1205 2665 20 61 7740 9 5 2006
## 1206 2666 120 48 6373 9 5 2006
## 1207 2667 60 65 10237 6 5 2005
## 1208 2668 60 65 10237 7 5 2006
## 1209 2669 20 102 11660 6 5 2006
## 1210 2670 60 96 11631 8 5 2004
## 1211 2671 60 75 9073 7 5 2006
## 1212 2672 120 43 3087 7 5 2006
## 1213 2673 120 NA 2938 7 5 2002
## 1214 2674 120 NA 3072 7 5 2004
## 1215 2675 120 43 3010 7 5 2005
## 1216 2676 60 59 9171 7 5 2004
## 1217 2677 60 NA 8658 6 5 2000
## 1218 2678 60 NA 12104 7 5 2006
## 1219 2679 60 84 9660 8 5 1998
## 1220 2680 60 83 9545 8 5 2000
## 1221 2681 60 NA 9233 9 5 2000
## 1222 2682 60 83 10019 8 5 1995
## 1223 2683 60 114 17242 9 5 1993
## 1224 2684 60 NA 10236 8 5 1994
## 1225 2685 60 NA 12585 8 5 1993
## 1226 2686 60 75 12447 8 5 2005
## 1227 2687 20 49 15218 8 5 2006
## 1228 2688 20 85 10936 8 5 2006
## 1229 2689 20 72 8640 8 5 2006
## 1230 2690 60 100 13162 9 5 2006
## 1231 2691 60 65 8125 6 5 2005
## 1232 2692 20 74 7733 6 5 2005
## 1233 2693 20 91 11024 7 5 2005
## 1234 2694 20 63 13072 6 5 2005
## 1235 2695 60 65 7800 7 5 2005
## 1236 2696 60 74 7632 7 5 2005
## 1237 2697 60 70 8304 6 5 1997
## 1238 2698 60 70 9370 6 5 1992
## 1239 2699 120 50 7175 6 5 1990
## 1240 2700 120 50 7175 6 5 1991
## 1241 2701 60 NA 9019 6 5 1994
## 1242 2702 20 70 9100 5 4 1962
## 1243 2703 90 68 8927 6 6 1977
## 1244 2704 20 NA 9240 5 6 1962
## 1245 2705 20 NA 9308 5 5 1965
## 1246 2706 20 65 8450 5 6 1968
## 1247 2707 20 NA 8638 5 6 1963
## 1248 2708 20 NA 13052 5 6 1965
## 1249 2709 20 NA 8020 5 6 1964
## 1250 2710 20 NA 8789 5 6 1967
## 1251 2711 80 100 14330 7 4 1974
## 1252 2712 60 105 11025 8 5 1992
## 1253 2713 120 34 3628 7 5 2004
## 1254 2714 160 24 2544 7 5 2005
## 1255 2715 160 NA 2998 6 5 2000
## 1256 2716 160 NA 4447 7 5 2003
## 1257 2717 60 114 8314 7 5 1997
## 1258 2718 20 60 7180 8 5 2001
## 1259 2719 190 79 13110 5 6 1972
## 1260 2720 20 78 10140 7 6 1967
## 1261 2721 20 80 9600 5 5 1968
## 1262 2722 60 72 8640 5 5 1968
## 1263 2723 20 78 9360 6 7 1968
## 1264 2724 85 70 8400 5 6 1968
## 1265 2725 20 NA 9759 5 5 1966
## 1266 2726 80 80 9600 5 7 1967
## 1267 2727 190 80 8800 6 7 1965
## 1268 2728 20 NA 10368 6 6 1964
## 1269 2729 60 85 9350 5 6 1964
## 1270 2730 20 80 10800 5 7 1960
## 1271 2731 30 60 8550 6 8 1934
## 1272 2732 20 68 9724 5 7 1947
## 1273 2733 20 80 9600 5 6 1961
## 1274 2734 20 89 10858 5 6 1952
## 1275 2735 20 80 9600 5 5 1951
## 1276 2736 60 79 9462 5 6 1949
## 1277 2737 20 82 9888 5 6 1954
## 1278 2738 90 NA 8917 5 5 1967
## 1279 2739 80 NA 12700 6 5 1964
## 1280 2740 20 109 9723 6 7 1963
## 1281 2741 20 70 8400 5 5 1957
## 1282 2742 20 NA 9610 6 6 1958
## 1283 2743 80 125 10000 5 6 1956
## 1284 2744 20 72 10152 6 6 1956
## 1285 2745 20 70 8092 6 8 1954
## 1286 2746 20 66 12778 5 6 1952
## 1287 2747 20 75 10170 6 6 1951
## 1288 2748 80 55 7700 5 7 1956
## 1289 2749 20 65 11050 5 5 1956
## 1290 2750 20 80 13600 5 6 1955
## 1291 2751 20 85 15428 5 6 1951
## 1292 2752 30 118 21299 7 5 1941
## 1293 2753 20 70 13300 5 5 1956
## 1294 2754 190 94 22136 5 5 1925
## 1295 2755 50 50 7500 6 6 1947
## 1296 2756 30 60 10410 3 8 1930
## 1297 2757 30 60 10914 3 3 1929
## 1298 2758 50 60 7008 4 8 1900
## 1299 2759 70 60 7200 6 8 1915
## 1300 2760 50 60 10818 4 4 1910
## 1301 2761 80 83 10184 6 5 1963
## 1302 2762 20 77 9510 6 5 1962
## 1303 2763 20 80 10800 6 6 1961
## 1304 2764 20 86 11650 7 5 1959
## 1305 2765 60 NA 18275 7 8 1962
## 1306 2766 50 60 12144 4 6 1950
## 1307 2767 90 60 8544 3 4 1950
## 1308 2768 90 75 8512 5 5 1960
## 1309 2769 20 70 7000 5 4 1961
## 1310 2770 20 74 7400 7 6 1962
## 1311 2771 20 70 7000 5 6 1962
## 1312 2772 190 70 7000 5 5 1962
## 1313 2773 70 62 9856 5 8 1900
## 1314 2774 50 60 9600 5 6 1948
## 1315 2775 50 60 5520 4 6 1920
## 1316 2776 50 60 9600 6 8 1900
## 1317 2777 70 58 6451 7 7 1900
## 1318 2778 70 66 3960 7 8 1930
## 1319 2779 190 56 7745 4 6 1900
## 1320 2780 30 56 7741 6 5 1924
## 1321 2781 30 50 5633 5 7 1925
## 1322 2782 20 60 7200 4 5 1950
## 1323 2783 70 42 7614 3 5 1905
## 1324 2784 190 50 6000 5 7 1955
## 1325 2785 50 50 6000 5 7 1924
## 1326 2786 30 52 7830 3 5 1921
## 1327 2787 50 56 9576 6 7 1945
## 1328 2788 30 48 5747 3 4 1920
## 1329 2789 75 70 6300 7 6 1910
## 1330 2790 90 33 5976 5 7 1920
## 1331 2791 20 65 9750 5 5 1958
## 1332 2792 50 63 4761 3 3 1918
## 1333 2793 70 69 11737 6 7 1924
## 1334 2794 50 51 6120 3 5 1930
## 1335 2795 50 51 6120 5 7 1930
## 1336 2796 30 50 11672 5 5 1925
## 1337 2797 50 90 33120 6 5 1962
## 1338 2798 50 60 10320 4 5 1924
## 1339 2799 70 60 7518 5 8 1910
## 1340 2800 30 50 9000 5 4 1919
## 1341 2801 30 60 7200 6 6 1930
## 1342 2802 50 82 12375 5 5 1951
## 1343 2803 90 120 11136 6 5 1964
## 1344 2804 20 100 21370 5 5 1950
## 1345 2805 30 55 8250 5 7 1935
## 1346 2806 30 50 5220 5 3 1936
## 1347 2807 20 50 5500 7 5 2004
## 1348 2808 20 NA 11327 5 6 1967
## 1349 2809 80 80 10366 6 7 1964
## 1350 2810 20 75 9000 5 6 1966
## 1351 2811 20 NA 9535 5 5 1967
## 1352 2812 80 NA 7176 6 6 1978
## 1353 2813 90 NA 9662 5 4 1977
## 1354 2814 90 75 8235 5 4 1977
## 1355 2815 30 NA 17529 5 5 1924
## 1356 2816 20 NA 20355 7 6 1967
## 1357 2817 20 87 13050 5 6 1963
## 1358 2818 85 72 10820 5 7 1971
## 1359 2819 150 NA 1700 7 5 1980
## 1360 2820 20 75 9375 4 5 1954
## 1361 2821 50 62 6488 5 5 1942
## 1362 2822 70 114 19950 6 7 1928
## 1363 2823 75 60 19800 6 8 1935
## 1364 2824 80 78 11679 5 5 1962
## 1365 2825 20 80 12048 5 6 1952
## 1366 2826 20 70 10519 5 8 1955
## 1367 2827 50 75 9525 6 5 1953
## 1368 2828 60 88 12128 6 4 1989
## 1369 2829 90 73 9069 6 8 1993
## 1370 2830 60 133 11003 7 5 2005
## 1371 2831 20 64 7488 7 5 2005
## 1372 2832 20 90 13377 6 5 2006
## 1373 2833 20 78 11645 8 5 2005
## 1374 2834 60 91 10984 7 5 2005
## 1375 2835 20 78 9316 7 5 2005
## 1376 2836 60 78 9316 7 5 2005
## 1377 2837 20 80 12000 6 5 1968
## 1378 2838 20 95 13015 5 6 1996
## 1379 2839 60 65 12438 6 5 1995
## 1380 2840 20 NA 8685 7 5 1998
## 1381 2841 60 68 9272 7 5 1999
## 1382 2842 60 72 13426 7 5 1999
## 1383 2843 60 50 8340 6 6 1977
## 1384 2844 80 42 10385 6 6 1978
## 1385 2845 20 60 7200 5 8 1972
## 1386 2846 60 NA 9930 7 5 2002
## 1387 2847 60 45 9468 6 5 1999
## 1388 2848 20 NA 11088 8 5 2002
## 1389 2849 60 70 8726 7 5 2002
## 1390 2850 60 67 10566 8 5 1999
## 1391 2851 60 NA 21533 7 5 1996
## 1392 2852 60 90 11250 7 5 1998
## 1393 2853 60 90 11250 7 5 1995
## 1394 2854 120 37 4435 6 5 2003
## 1395 2855 20 70 8810 7 5 2003
## 1396 2856 60 74 8581 7 5 2006
## 1397 2857 60 70 8400 7 5 2005
## 1398 2858 20 65 8772 7 5 2005
## 1399 2859 70 67 8777 4 6 1910
## 1400 2860 90 38 7840 6 5 1975
## 1401 2861 20 73 16133 5 4 1969
## 1402 2862 60 62 7162 7 5 2003
## 1403 2863 20 75 8050 6 5 2002
## 1404 2864 60 90 11060 7 5 2003
## 1405 2865 180 35 3675 6 5 2005
## 1406 2866 160 24 2522 7 5 2004
## 1407 2867 50 56 6956 4 7 1948
## 1408 2868 50 72 7822 6 3 1915
## 1409 2869 50 62 8707 4 5 1924
## 1410 2870 20 60 16012 4 4 1954
## 1411 2871 50 45 8248 4 4 1922
## 1412 2872 30 60 8088 2 3 1922
## 1413 2873 50 76 11388 4 7 1910
## 1414 2874 50 60 10890 5 5 1938
## 1415 2875 190 58 6430 6 6 1945
## 1416 2876 70 43 7000 7 8 1926
## 1417 2877 70 69 4899 6 8 1920
## 1418 2878 70 54 9399 7 8 1919
## 1419 2879 50 84 10164 5 5 1939
## 1420 2880 50 51 6191 5 4 1941
## 1421 2881 50 66 21780 6 5 1920
## 1422 2882 50 80 12400 5 6 1940
## 1423 2883 50 81 8170 7 7 1929
## 1424 2884 70 70 12320 7 7 1932
## 1425 2885 70 70 14210 6 7 1930
## 1426 2886 60 78 15600 5 7 1950
## 1427 2887 30 50 7288 5 6 1942
## 1428 2888 50 50 7000 6 7 1926
## 1429 2889 30 61 8534 4 4 1925
## 1430 2890 30 50 7030 4 6 1925
## 1431 2891 50 75 9060 6 5 1957
## 1432 2892 30 69 12366 3 5 1945
## 1433 2893 190 50 9000 5 6 1951
## 1434 2894 50 60 8520 3 5 1916
## 1435 2895 120 41 5748 8 5 2005
## 1436 2896 120 44 3842 8 5 2004
## 1437 2897 20 69 23580 6 6 1979
## 1438 2898 90 65 8385 6 5 1978
## 1439 2899 20 70 9116 8 5 2001
## 1440 2900 80 140 11080 6 6 1975
## 1441 2901 20 NA 50102 6 5 1958
## 1442 2902 20 NA 8098 6 5 2000
## 1443 2903 20 95 13618 8 5 2005
## 1444 2904 20 88 11577 9 5 2005
## 1445 2905 20 125 31250 1 3 1951
## 1446 2906 90 78 7020 7 5 1997
## 1447 2907 160 41 2665 5 6 1977
## 1448 2908 20 58 10172 5 7 1968
## 1449 2909 90 NA 11836 5 5 1970
## 1450 2910 180 21 1470 4 6 1970
## 1451 2911 160 21 1484 4 4 1972
## 1452 2912 20 80 13384 5 5 1969
## 1453 2913 160 21 1533 4 5 1970
## 1454 2914 160 21 1526 4 5 1970
## 1455 2915 160 21 1936 4 7 1970
## 1456 2916 160 21 1894 4 5 1970
## 1457 2917 20 160 20000 5 7 1960
## 1458 2918 85 62 10441 5 5 1992
## 1459 2919 60 74 9627 7 5 1993
## YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 BsmtUnfSF TotalBsmtSF
## 1 1961 0 468 144 270 882
## 2 1958 108 923 0 406 1329
## 3 1998 0 791 0 137 928
## 4 1998 20 602 0 324 926
## 5 1992 0 263 0 1017 1280
## 6 1994 0 0 0 763 763
## 7 2007 0 935 0 233 1168
## 8 1998 0 0 0 789 789
## 9 1990 0 637 0 663 1300
## 10 1970 0 804 78 0 882
## 11 1999 0 1051 0 354 1405
## 12 1971 504 156 0 327 483
## 13 1971 492 300 0 225 525
## 14 1975 0 514 0 341 855
## 15 1975 0 0 0 836 836
## 16 2010 162 0 0 1590 1590
## 17 2010 256 0 0 1544 1544
## 18 2005 615 110 0 1588 1698
## 19 2006 240 28 0 1794 1822
## 20 2004 1095 1373 0 1473 2846
## 21 2002 232 578 0 1093 1671
## 22 2006 178 24 0 1346 1370
## 23 2005 0 0 0 1324 1324
## 24 2006 14 16 0 1129 1145
## 25 2004 0 326 0 58 384
## 26 2004 0 0 0 847 847
## 27 1998 114 0 0 1629 1629
## 28 2006 226 0 0 1595 1595
## 29 2010 0 0 0 1218 1218
## 30 2005 0 1414 0 54 1468
## 31 2004 122 0 0 831 831
## 32 1950 0 0 0 816 816
## 33 2002 250 126 859 223 1208
## 34 1993 504 250 981 0 1231
## 35 1993 180 1129 0 261 1390
## 36 2005 0 1298 0 190 1488
## 37 2004 0 0 0 840 840
## 38 2004 120 280 0 320 600
## 39 2005 0 368 42 190 600
## 40 2005 216 376 0 224 600
## 41 2000 1159 466 0 290 756
## 42 2003 0 244 0 286 530
## 43 2010 0 1032 0 610 1642
## 44 2000 0 484 0 491 975
## 45 2002 0 833 0 659 1492
## 46 1967 120 506 0 1323 1829
## 47 1993 0 1137 0 143 1280
## 48 1978 0 687 46 491 1224
## 49 1971 0 329 0 386 715
## 50 1966 172 698 0 534 1232
## 51 1966 0 1059 0 150 1209
## 52 1967 268 1010 0 500 1510
## 53 1964 0 0 0 533 533
## 54 2001 144 1500 0 228 1728
## 55 2002 265 670 0 470 1140
## 56 1950 340 300 0 482 782
## 57 2008 216 944 0 136 1080
## 58 1958 0 0 0 1604 1604
## 59 2009 506 1188 0 292 1480
## 60 1959 150 856 162 125 1143
## 61 1959 91 936 0 270 1206
## 62 1959 432 734 0 510 1244
## 63 1956 0 0 0 832 832
## 64 1952 0 339 0 525 864
## 65 1955 0 648 0 324 972
## 66 1958 0 532 0 456 988
## 67 1994 0 0 0 576 576
## 68 2004 0 481 174 161 816
## 69 1960 0 588 350 237 1175
## 70 1984 218 717 263 415 1395
## 71 1954 771 48 0 661 709
## 72 1950 0 579 0 357 936
## 73 1957 0 274 1073 0 1347
## 74 2000 0 0 0 827 827
## 75 2009 0 780 0 247 1027
## 76 1962 0 176 0 832 1008
## 77 1970 0 0 0 678 678
## 78 2003 0 0 0 930 930
## 79 2002 0 0 0 346 346
## 80 1950 0 0 0 840 840
## 81 1994 0 283 0 455 738
## 82 1997 0 788 0 200 988
## 83 2005 0 474 0 634 1108
## 84 1950 0 188 0 577 765
## 85 1950 0 452 12 144 608
## 86 1950 0 264 0 308 572
## 87 1950 300 360 159 316 835
## 88 1950 0 300 0 480 780
## 89 1980 0 276 0 252 528
## 90 1950 0 448 0 480 928
## 91 1950 0 960 0 164 1124
## 92 1950 0 0 0 888 888
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## 1334 1 0 3 1 6 0
## 1335 1 0 3 1 7 0
## 1336 1 0 2 1 4 0
## 1337 2 0 5 1 8 1
## 1338 2 0 3 1 7 0
## 1339 1 0 3 1 7 0
## 1340 1 0 2 1 4 0
## 1341 1 0 3 1 6 0
## 1342 1 0 3 1 7 1
## 1343 2 0 4 2 8 0
## 1344 1 0 3 1 7 1
## 1345 1 0 2 1 6 1
## 1346 1 0 2 1 4 0
## 1347 2 0 2 1 4 0
## 1348 1 0 3 1 6 1
## 1349 1 0 2 1 4 0
## 1350 1 0 3 1 6 0
## 1351 1 1 3 1 7 1
## 1352 1 0 3 1 6 1
## 1353 2 0 6 2 10 0
## 1354 2 0 6 2 10 0
## 1355 1 0 2 1 5 1
## 1356 1 0 2 1 6 2
## 1357 1 0 1 1 4 2
## 1358 1 0 2 1 4 0
## 1359 2 1 2 1 7 0
## 1360 1 1 3 1 6 1
## 1361 1 0 3 1 6 2
## 1362 2 0 4 1 8 2
## 1363 3 1 5 1 7 2
## 1364 2 0 3 1 6 1
## 1365 1 0 3 1 7 1
## 1366 1 0 3 1 6 0
## 1367 1 1 5 1 7 0
## 1368 2 1 4 1 9 1
## 1369 2 0 2 2 8 0
## 1370 2 1 3 1 7 0
## 1371 2 0 2 1 6 0
## 1372 2 0 3 1 7 1
## 1373 2 1 2 1 6 0
## 1374 2 1 3 1 8 0
## 1375 2 0 3 1 7 1
## 1376 2 1 3 1 6 0
## 1377 2 0 3 1 6 1
## 1378 1 1 3 1 5 0
## 1379 2 1 3 1 6 1
## 1380 2 0 3 1 5 0
## 1381 2 1 3 1 7 0
## 1382 2 1 3 1 7 1
## 1383 1 1 3 1 6 0
## 1384 2 0 3 1 6 0
## 1385 1 0 3 1 5 0
## 1386 2 1 3 1 6 0
## 1387 2 1 3 1 7 1
## 1388 1 1 1 1 5 1
## 1389 2 1 4 1 8 0
## 1390 2 1 3 1 8 1
## 1391 2 1 4 1 9 1
## 1392 2 1 3 1 8 1
## 1393 2 1 4 1 8 1
## 1394 1 0 1 1 4 0
## 1395 2 0 3 1 7 0
## 1396 2 1 3 1 7 0
## 1397 2 1 3 1 6 0
## 1398 2 0 3 1 6 0
## 1399 2 0 3 1 6 0
## 1400 2 0 4 0 4 0
## 1401 1 0 2 1 5 0
## 1402 2 1 3 1 8 1
## 1403 1 0 2 1 4 0
## 1404 2 1 3 1 9 1
## 1405 1 0 2 1 5 0
## 1406 2 0 3 1 7 0
## 1407 1 0 3 1 5 0
## 1408 2 0 3 1 7 0
## 1409 1 1 4 1 9 0
## 1410 2 0 3 1 6 1
## 1411 1 0 3 1 8 1
## 1412 1 0 1 1 3 0
## 1413 1 0 3 1 5 0
## 1414 2 0 3 1 6 0
## 1415 1 0 3 1 6 0
## 1416 1 0 3 1 6 2
## 1417 1 0 3 1 6 0
## 1418 1 1 4 1 7 1
## 1419 2 0 3 1 6 2
## 1420 1 0 4 1 6 0
## 1421 1 1 3 1 7 1
## 1422 1 1 2 1 7 1
## 1423 2 0 4 1 7 1
## 1424 1 1 3 1 8 2
## 1425 1 1 3 1 8 1
## 1426 2 0 4 1 9 3
## 1427 1 0 2 1 5 0
## 1428 1 0 3 1 6 0
## 1429 1 0 2 1 4 0
## 1430 1 0 2 1 4 0
## 1431 2 0 4 1 6 0
## 1432 1 0 2 1 5 0
## 1433 2 0 4 2 8 0
## 1434 1 0 2 1 6 0
## 1435 2 0 2 1 5 1
## 1436 2 0 2 1 5 1
## 1437 2 0 3 1 6 1
## 1438 2 0 4 2 10 0
## 1439 2 0 3 1 7 0
## 1440 2 0 3 1 6 0
## 1441 1 0 2 1 6 2
## 1442 2 0 2 1 5 0
## 1443 2 0 3 1 8 2
## 1444 2 0 3 1 9 1
## 1445 1 1 3 1 6 0
## 1446 2 0 2 2 8 0
## 1447 1 1 3 1 5 1
## 1448 1 0 3 1 5 0
## 1449 2 0 4 2 8 0
## 1450 1 0 1 1 3 0
## 1451 1 1 3 1 5 0
## 1452 1 0 3 1 8 1
## 1453 1 1 3 1 5 0
## 1454 1 1 3 1 5 0
## 1455 1 1 3 1 5 0
## 1456 1 1 3 1 6 0
## 1457 1 0 4 1 7 1
## 1458 1 0 3 1 6 0
## 1459 2 1 3 1 9 1
## GarageYrBlt GarageCars GarageArea WoodDeckSF OpenPorchSF EnclosedPorch
## 1 1961 1 730 140 0 0
## 2 1958 1 312 393 36 0
## 3 1997 2 482 212 34 0
## 4 1998 2 470 360 36 0
## 5 1992 2 506 0 82 0
## 6 1993 2 440 157 84 0
## 7 1992 2 420 483 21 0
## 8 1998 2 393 0 75 0
## 9 1990 2 506 192 0 0
## 10 1970 2 525 240 0 0
## 11 1999 2 511 203 68 0
## 12 1971 1 264 275 0 0
## 13 1997 1 320 0 0 0
## 14 1975 2 440 173 0 0
## 15 1975 1 308 0 30 0
## 16 2009 3 751 144 133 0
## 17 2009 3 868 0 35 0
## 18 2005 3 730 192 74 0
## 19 2005 3 678 0 119 0
## 20 2003 3 958 220 150 0
## 21 2002 3 756 238 130 0
## 22 2006 2 484 120 49 0
## 23 2005 2 430 36 23 0
## 24 2006 2 437 100 116 0
## 25 2004 2 400 100 0 0
## 26 2004 2 433 144 48 0
## 27 1998 3 962 23 172 0
## 28 2005 3 880 144 0 0
## 29 2009 2 676 0 136 0
## 30 2005 2 528 0 102 0
## 31 2004 2 484 144 68 0
## 32 1920 1 429 121 0 80
## 33 1974 2 461 296 0 186
## 34 1993 3 762 32 130 0
## 35 1992 3 713 198 30 0
## 36 2004 2 506 168 50 0
## 37 2004 2 588 0 111 0
## 38 2004 2 480 0 172 0
## 39 2004 2 480 0 172 0
## 40 2005 2 480 0 166 0
## 41 2000 2 440 0 32 0
## 42 2003 2 496 0 50 0
## 43 2010 3 852 160 192 0
## 44 2000 2 592 280 184 0
## 45 2002 2 596 277 137 0
## 46 1967 2 535 0 76 0
## 47 1993 2 660 224 32 0
## 48 1978 2 678 0 69 0
## 49 1971 2 441 0 78 0
## 50 1966 2 490 0 224 0
## 51 1966 2 504 0 0 120
## 52 1967 2 517 0 40 0
## 53 1964 2 480 0 172 0
## 54 NA 0 0 0 0 0
## 55 1994 2 400 0 0 0
## 56 1949 2 470 0 0 150
## 57 1966 1 315 238 0 0
## 58 1958 2 576 42 0 0
## 59 2003 2 620 252 73 0
## 60 1959 1 308 0 0 0
## 61 1959 1 312 0 21 0
## 62 1956 2 440 0 28 0
## 63 1956 1 263 0 0 0
## 64 1952 1 318 0 0 0
## 65 1955 1 305 0 0 205
## 66 1958 1 264 0 0 0
## 67 1989 2 676 0 0 0
## 68 1950 1 280 414 0 0
## 69 1960 2 484 0 0 0
## 70 1963 2 440 657 0 113
## 71 1900 1 240 84 0 0
## 72 NA 0 0 51 0 135
## 73 1957 2 551 0 0 0
## 74 1938 1 240 0 0 0
## 75 1948 1 240 0 100 126
## 76 1962 1 315 0 0 334
## 77 1928 2 780 0 0 0
## 78 1930 1 288 54 228 246
## 79 2003 2 624 0 108 0
## 80 NA 0 0 0 18 18
## 81 1970 2 624 0 0 0
## 82 1950 1 363 0 0 0
## 83 1928 1 315 0 0 60
## 84 1926 1 200 135 0 41
## 85 1939 1 240 0 0 128
## 86 1973 2 576 0 0 0
## 87 1942 1 240 0 0 35
## 88 1948 1 312 221 0 48
## 89 1979 2 624 306 0 32
## 90 1930 2 480 0 10 0
## 91 1923 1 288 0 0 128
## 92 1915 2 572 160 0 364
## 93 NA 0 0 344 0 0
## 94 1920 1 180 0 0 112
## 95 1959 1 365 0 132 0
## 96 1917 1 180 0 0 248
## 97 NA 0 0 0 0 168
## 98 1940 1 231 0 0 45
## 99 NA 0 0 0 0 239
## 100 1910 2 450 56 144 0
## 101 NA 0 0 0 58 0
## 102 1966 1 416 0 0 0
## 103 1969 1 280 226 0 0
## 104 1978 2 591 0 0 0
## 105 1968 2 480 192 38 0
## 106 1977 2 690 496 0 0
## 107 1945 2 440 80 0 0
## 108 1978 2 522 336 104 0
## 109 1938 1 480 0 0 60
## 110 1987 1 467 0 52 52
## 111 1947 1 209 0 0 0
## 112 1954 1 366 240 0 0
## 113 2009 2 572 0 0 0
## 114 1964 2 504 0 144 0
## 115 1987 2 528 0 278 0
## 116 2000 4 1017 450 92 0
## 117 2009 2 615 0 54 0
## 118 1957 2 484 0 59 0
## 119 1998 2 580 71 0 0
## 120 1997 2 513 0 39 0
## 121 1977 1 308 316 85 0
## 122 1977 2 484 0 0 0
## 123 2003 3 850 188 25 0
## 124 1997 2 621 331 38 0
## 125 2003 2 420 140 0 0
## 126 1945 1 240 0 0 0
## 127 1954 1 319 60 0 56
## 128 1968 1 336 257 0 168
## 129 1956 2 480 0 0 0
## 130 1975 1 352 0 0 0
## 131 NA 0 0 0 0 0
## 132 1979 1 260 0 0 36
## 133 1939 2 399 0 0 0
## 134 NA 0 0 0 0 0
## 135 NA 0 0 0 156 0
## 136 1941 1 312 0 0 86
## 137 1950 2 484 0 0 0
## 138 1994 2 473 0 0 0
## 139 1989 2 598 141 36 0
## 140 1989 2 531 112 81 0
## 141 1951 1 200 0 0 0
## 142 1950 1 625 0 56 0
## 143 1896 1 330 0 0 265
## 144 2004 2 525 128 53 0
## 145 1998 3 741 0 70 0
## 146 1977 2 473 375 26 0
## 147 1976 2 776 0 0 0
## 148 2008 3 660 100 48 0
## 149 2010 2 520 0 25 0
## 150 2007 2 400 100 0 0
## 151 1965 2 528 0 0 0
## 152 2004 2 624 0 0 0
## 153 2001 2 686 328 0 0
## 154 1973 1 253 174 0 56
## 155 NA 0 0 200 26 0
## 156 NA 0 0 0 0 0
## 157 1972 1 286 0 0 64
## 158 1971 1 352 0 0 0
## 159 1984 2 495 431 0 0
## 160 1985 2 616 200 0 0
## 161 1993 2 576 22 0 0
## 162 1969 1 275 287 0 280
## 163 1994 2 482 129 64 222
## 164 1993 2 616 162 312 0
## 165 1956 1 502 0 0 0
## 166 1974 2 528 80 21 0
## 167 1997 2 470 252 30 0
## 168 2003 3 632 120 46 0
## 169 1996 2 390 48 72 0
## 170 2004 2 564 182 57 0
## 171 1998 2 495 168 43 0
## 172 1995 2 528 52 50 0
## 173 1998 2 396 256 0 0
## 174 1998 2 440 0 84 0
## 175 1994 2 409 0 46 0
## 176 1993 2 430 0 21 0
## 177 1977 2 567 0 176 0
## 178 1978 2 529 192 192 0
## 179 1978 2 484 252 55 0
## 180 1980 2 441 178 84 0
## 181 1978 2 479 0 0 0
## 182 2003 2 581 0 35 0
## 183 2000 2 588 0 134 0
## 184 2002 2 565 225 48 0
## 185 1975 2 478 0 0 0
## 186 1974 2 528 0 0 0
## 187 1975 2 506 40 0 0
## 188 1970 2 477 0 45 0
## 189 1971 1 336 0 0 0
## 190 2001 2 576 210 0 0
## 191 1986 2 576 200 0 0
## 192 1973 1 264 0 0 0
## 193 1972 1 288 0 0 0
## 194 1976 2 440 108 0 0
## 195 1975 2 440 0 27 0
## 196 1977 2 440 0 28 0
## 197 1978 2 440 108 0 0
## 198 1978 2 440 147 0 0
## 199 1976 1 345 150 20 0
## 200 1966 2 656 0 162 0
## 201 2007 3 754 135 64 0
## 202 2009 3 864 0 197 0
## 203 2008 3 896 192 98 0
## 204 2007 3 762 360 50 0
## 205 2008 3 834 319 90 0
## 206 2004 3 932 99 82 0
## 207 2007 3 640 192 273 0
## 208 2008 3 927 168 45 0
## 209 2006 3 700 184 52 0
## 210 2008 3 850 200 26 0
## 211 2003 2 583 165 190 0
## 212 2003 3 886 0 116 0
## 213 2003 3 730 114 40 0
## 214 2006 3 871 230 60 0
## 215 2005 2 484 192 36 0
## 216 2005 2 624 170 63 0
## 217 2008 2 484 192 26 0
## 218 2004 3 949 226 235 0
## 219 2003 3 685 208 55 0
## 220 2008 2 649 231 90 0
## 221 2008 2 539 192 42 0
## 222 2002 3 701 218 183 0
## 223 2003 2 478 148 36 0
## 224 2005 2 390 100 0 0
## 225 2005 2 392 36 20 0
## 226 2005 2 397 100 16 0
## 227 2004 2 400 100 0 0
## 228 2004 2 436 120 48 0
## 229 2004 2 434 300 48 0
## 230 2003 2 400 0 75 0
## 231 2003 2 393 100 56 0
## 232 2002 3 644 0 48 0
## 233 2004 2 400 0 0 0
## 234 2000 2 394 0 72 0
## 235 1999 2 400 0 0 0
## 236 1999 3 658 298 0 0
## 237 1999 2 410 0 0 0
## 238 2000 3 810 252 30 0
## 239 1994 3 815 182 56 0
## 240 1995 2 647 296 102 209
## 241 1993 2 623 173 165 0
## 242 2008 3 972 120 56 0
## 243 2008 3 726 144 44 0
## 244 2007 3 844 0 69 0
## 245 2006 2 492 297 50 0
## 246 2005 3 795 70 226 0
## 247 2008 2 529 0 121 0
## 248 2008 2 625 205 126 0
## 249 2008 3 984 212 136 0
## 250 2006 2 692 0 140 0
## 251 2006 3 782 168 45 0
## 252 2009 2 517 0 175 0
## 253 2006 3 711 168 120 0
## 254 2003 2 588 192 88 0
## 255 2003 2 628 0 45 0
## 256 2007 2 576 0 178 0
## 257 2006 2 484 182 0 0
## 258 NA 0 0 0 50 0
## 259 2004 2 478 146 91 0
## 260 2004 3 845 210 36 0
## 261 2005 2 576 248 102 0
## 262 NA 0 0 0 39 0
## 263 2004 2 576 0 42 0
## 264 2008 3 788 0 93 0
## 265 1997 2 559 0 74 0
## 266 1992 2 528 0 91 0
## 267 1990 2 542 0 60 0
## 268 1994 2 495 140 32 0
## 269 1986 2 465 0 48 0
## 270 1981 2 612 0 0 0
## 271 1969 2 400 0 28 0
## 272 1982 2 732 0 312 0
## 273 1961 1 440 158 0 0
## 274 1965 1 288 171 0 0
## 275 1963 1 300 0 0 0
## 276 1962 1 252 0 0 0
## 277 1980 3 864 462 0 0
## 278 1991 2 524 502 60 0
## 279 2004 3 704 0 177 0
## 280 2008 2 561 0 254 0
## 281 2008 2 641 0 169 0
## 282 2000 2 440 0 32 0
## 283 1999 2 506 0 34 0
## 284 1977 2 642 501 120 0
## 285 1981 2 540 228 20 218
## 286 1976 2 527 0 64 0
## 287 1974 2 784 297 40 0
## 288 1967 2 515 0 120 0
## 289 1969 2 502 312 11 0
## 290 1969 1 336 0 0 0
## 291 1977 2 520 321 72 0
## 292 1967 1 312 0 0 0
## 293 1967 2 440 160 40 0
## 294 1974 2 498 0 77 0
## 295 1971 2 495 0 20 0
## 296 1988 2 396 0 0 0
## 297 1960 1 308 0 0 0
## 298 1982 3 768 0 0 84
## 299 1956 2 576 0 99 0
## 300 1961 2 610 172 28 0
## 301 1964 2 520 0 32 0
## 302 1965 2 549 0 40 264
## 303 1961 2 368 85 0 0
## 304 1955 1 330 0 70 0
## 305 1967 2 550 0 67 260
## 306 1961 2 530 0 49 0
## 307 1966 2 542 0 110 0
## 308 1956 1 286 257 0 0
## 309 1960 2 441 0 0 203
## 310 1959 1 297 156 0 0
## 311 1956 1 304 0 35 120
## 312 1955 1 350 280 0 0
## 313 1956 1 294 0 189 140
## 314 1958 2 495 0 0 0
## 315 1954 1 297 0 0 0
## 316 1951 2 480 0 0 0
## 317 1945 1 240 0 0 0
## 318 1952 1 230 0 0 0
## 319 1953 1 616 208 0 100
## 320 1948 2 410 0 0 134
## 321 1950 1 246 126 0 0
## 322 1958 1 366 0 90 0
## 323 1939 2 720 187 0 128
## 324 1940 1 281 0 0 168
## 325 1987 1 264 0 0 0
## 326 1954 2 528 0 0 432
## 327 2008 2 624 0 0 112
## 328 NA 0 0 0 81 0
## 329 1980 2 576 0 0 0
## 330 1959 1 320 0 30 0
## 331 1969 3 907 0 0 0
## 332 1963 1 364 0 0 0
## 333 1967 2 480 0 0 156
## 334 1985 2 480 0 0 42
## 335 1957 1 308 0 0 0
## 336 1958 1 384 0 28 64
## 337 1989 2 900 0 0 0
## 338 1958 1 294 0 0 0
## 339 1952 1 240 0 68 0
## 340 1959 1 288 0 0 0
## 341 1949 1 384 0 204 0
## 342 1994 2 576 180 36 0
## 343 1964 2 483 0 55 0
## 344 1978 2 564 0 40 0
## 345 1963 1 264 0 0 0
## 346 1920 1 162 54 0 40
## 347 1920 2 324 0 28 169
## 348 1959 2 472 0 0 0
## 349 NA 0 0 0 0 176
## 350 1939 2 400 0 36 0
## 351 NA 0 0 0 0 148
## 352 NA 0 0 80 0 244
## 353 1950 1 308 0 12 0
## 354 1920 2 288 0 0 128
## 355 1965 1 308 0 0 25
## 356 1963 1 287 0 0 81
## 357 1974 2 720 0 0 160
## 358 1930 1 357 0 235 0
## 359 1917 1 280 0 103 0
## 360 NA 0 0 0 33 0
## 361 1920 1 207 0 0 176
## 362 1950 3 640 0 0 238
## 363 NA 0 0 0 36 94
## 364 1923 1 216 266 61 0
## 365 1955 1 336 244 60 0
## 366 1924 1 320 0 0 98
## 367 1926 1 264 0 15 51
## 368 1938 1 250 189 0 34
## 369 1982 5 1184 0 0 105
## 370 1930 2 576 0 0 150
## 371 1915 1 216 24 28 0
## 372 NA 0 0 0 56 0
## 373 1927 1 316 0 0 213
## 374 1915 1 379 0 24 112
## 375 NA 0 0 0 188 102
## 376 1927 1 384 0 0 148
## 377 NA 0 0 0 0 105
## 378 1915 1 308 0 0 288
## 379 1946 1 220 0 0 24
## 380 NA 0 0 120 0 0
## 381 1960 1 252 0 0 0
## 382 1934 1 226 0 0 0
## 383 1984 1 668 0 215 0
## 384 1978 2 504 0 0 0
## 385 1961 1 384 290 40 0
## 386 1960 2 480 0 0 0
## 387 1956 1 336 131 28 0
## 388 NA 0 0 0 0 100
## 389 1980 2 498 0 0 90
## 390 1956 1 340 0 0 0
## 391 1946 1 240 0 50 0
## 392 1954 1 252 328 0 0
## 393 1984 1 266 0 105 0
## 394 1990 2 462 114 36 0
## 395 1983 2 576 0 0 0
## 396 1993 2 473 400 100 144
## 397 1900 2 576 0 0 0
## 398 1979 2 720 73 0 0
## 399 1979 2 400 0 0 0
## 400 1979 2 672 120 0 0
## 401 1979 2 400 0 0 0
## 402 2000 4 820 312 0 0
## 403 2000 4 820 312 0 0
## 404 2000 4 820 312 0 0
## 405 2009 3 1138 185 24 0
## 406 2008 3 846 208 104 0
## 407 2008 3 904 192 142 0
## 408 2007 2 524 154 222 0
## 409 2007 2 662 168 0 0
## 410 2008 3 810 168 92 0
## 411 2005 3 844 144 98 0
## 412 2005 2 440 36 28 0
## 413 1992 2 525 150 193 0
## 414 1995 1 440 66 0 0
## 415 1998 2 539 0 81 0
## 416 1998 2 492 150 96 0
## 417 2002 2 577 144 40 0
## 418 2001 2 608 0 114 0
## 419 1978 1 343 72 0 0
## 420 1979 2 444 0 0 0
## 421 2002 3 1231 262 93 0
## 422 2003 2 570 192 132 0
## 423 2002 2 550 0 48 0
## 424 2001 2 608 256 43 0
## 425 1999 2 521 135 34 0
## 426 2002 2 523 194 144 0
## 427 1997 2 511 60 0 0
## 428 2007 3 810 0 48 0
## 429 2007 2 440 100 36 0
## 430 1968 1 544 0 240 0
## 431 2005 2 525 0 28 0
## 432 1959 1 320 0 0 0
## 433 1950 1 195 0 41 211
## 434 NA 0 0 0 0 0
## 435 1956 1 313 0 0 0
## 436 1940 1 256 84 0 0
## 437 1938 1 250 0 0 55
## 438 1926 1 180 329 96 0
## 439 1916 2 440 0 0 180
## 440 1918 1 215 0 113 0
## 441 1961 2 440 0 21 150
## 442 1960 1 307 483 0 0
## 443 1940 2 400 0 0 57
## 444 1954 1 390 0 0 0
## 445 1960 2 463 0 148 0
## 446 1949 1 295 0 0 81
## 447 1954 2 576 0 201 0
## 448 1980 2 452 279 0 0
## 449 1980 2 462 176 0 0
## 450 1980 2 462 154 0 0
## 451 1986 2 501 400 112 0
## 452 1971 2 551 200 144 0
## 453 1998 2 576 467 160 78
## 454 1940 1 200 0 0 72
## 455 2007 2 482 162 53 0
## 456 1975 1 280 119 24 0
## 457 2000 3 642 210 91 0
## 458 1977 1 351 319 0 0
## 459 1977 2 541 302 39 0
## 460 1991 2 672 0 0 0
## 461 2008 3 780 90 154 0
## 462 2008 3 885 0 95 0
## 463 1980 2 440 305 24 0
## 464 1987 2 578 224 238 0
## 465 2003 2 486 124 114 0
## 466 2007 3 920 308 52 0
## 467 1968 1 384 144 0 0
## 468 1969 2 504 0 16 0
## 469 1998 1 336 138 0 0
## 470 1993 1 429 280 0 0
## 471 1998 2 698 100 32 0
## 472 2001 2 440 150 0 0
## 473 1969 2 898 0 173 368
## 474 1997 2 470 0 36 0
## 475 1995 2 420 192 49 0
## 476 1998 2 472 100 82 0
## 477 1996 2 386 168 84 0
## 478 1996 2 404 0 39 0
## 479 1997 2 528 0 0 0
## 480 1992 2 506 0 82 0
## 481 1998 2 460 0 39 0
## 482 1991 2 416 0 40 0
## 483 1989 2 462 0 73 154
## 484 2005 3 683 192 32 0
## 485 2004 3 670 214 76 0
## 486 1952 2 495 0 0 144
## 487 2007 2 557 156 48 0
## 488 1950 2 738 0 0 180
## 489 1988 2 528 0 48 0
## 490 1983 2 598 152 0 0
## 491 1978 2 550 200 0 0
## 492 1979 2 551 467 0 0
## 493 1976 2 461 0 0 0
## 494 1980 2 489 28 73 0
## 495 1969 2 484 0 0 0
## 496 1978 4 1314 0 16 0
## 497 1976 2 473 0 24 0
## 498 1996 3 787 192 180 218
## 499 1982 2 480 0 45 0
## 500 1969 1 288 16 0 0
## 501 1977 2 576 0 0 0
## 502 1973 1 264 52 0 0
## 503 1975 1 352 411 0 0
## 504 1972 1 264 0 0 0
## 505 1975 2 460 0 0 0
## 506 1977 2 440 0 38 0
## 507 2007 3 905 209 95 0
## 508 2007 3 650 358 78 0
## 509 2007 3 706 156 207 0
## 510 2006 3 850 212 182 0
## 511 2005 4 1150 30 200 0
## 512 2005 3 1003 192 63 0
## 513 2007 3 656 100 24 0
## 514 2004 3 756 224 142 0
## 515 2003 3 789 154 65 0
## 516 2003 3 732 216 28 0
## 517 2001 3 746 0 60 0
## 518 2003 3 795 268 58 0
## 519 2008 3 870 0 48 0
## 520 2005 2 484 0 33 0
## 521 2008 3 944 210 0 0
## 522 2007 2 550 0 35 0
## 523 2007 2 550 0 35 0
## 524 2003 2 474 0 27 0
## 525 2003 3 644 156 20 0
## 526 2004 3 668 143 20 0
## 527 2007 2 428 100 24 0
## 528 2004 2 393 0 75 0
## 529 2002 2 400 138 48 0
## 530 2003 2 434 120 48 0
## 531 2008 2 484 192 26 0
## 532 2000 2 431 108 0 0
## 533 2002 2 403 100 35 0
## 534 1999 2 400 144 44 0
## 535 1999 2 450 0 103 0
## 536 1997 2 516 0 131 0
## 537 2000 3 687 162 153 0
## 538 1998 3 938 144 33 0
## 539 1996 2 576 168 0 0
## 540 1995 3 751 192 87 0
## 541 1993 3 783 385 99 0
## 542 2006 3 642 0 26 0
## 543 2007 3 851 144 60 0
## 544 2007 3 880 168 108 0
## 545 2006 2 480 0 20 0
## 546 2008 2 588 192 113 0
## 547 2007 3 660 133 120 0
## 548 2008 2 462 168 168 0
## 549 2003 2 542 143 66 0
## 550 2003 2 478 168 68 0
## 551 NA 0 0 0 90 0
## 552 2007 2 483 0 32 0
## 553 1995 2 525 0 70 0
## 554 1993 2 474 144 96 0
## 555 1994 2 483 144 74 0
## 556 2001 2 627 0 68 0
## 557 1992 2 506 192 85 0
## 558 1963 1 384 145 56 0
## 559 1962 1 420 132 0 0
## 560 1970 1 308 0 0 0
## 561 1963 1 276 0 0 0
## 562 1974 2 484 117 108 165
## 563 1972 2 490 0 0 92
## 564 1990 2 473 237 251 0
## 565 1993 2 636 294 49 0
## 566 2004 2 495 0 100 0
## 567 2005 2 480 0 166 0
## 568 2007 2 540 0 102 0
## 569 1999 2 440 0 32 0
## 570 2000 2 495 230 68 0
## 571 2001 2 545 277 45 0
## 572 2001 2 588 155 58 0
## 573 1999 2 484 124 113 0
## 574 1999 2 462 20 0 0
## 575 2001 2 546 0 0 0
## 576 1999 2 480 169 0 0
## 577 2000 2 480 169 0 0
## 578 1998 2 846 0 90 0
## 579 1995 2 477 172 24 0
## 580 1977 3 864 140 70 16
## 581 1976 2 550 280 90 0
## 582 2002 2 469 144 112 0
## 583 1969 2 516 0 0 0
## 584 1968 2 512 25 0 0
## 585 1967 2 578 0 62 192
## 586 1965 2 484 0 0 0
## 587 1968 2 470 0 40 0
## 588 1978 2 576 233 0 0
## 589 1987 2 576 0 0 0
## 590 1971 2 464 0 0 0
## 591 1956 1 267 0 0 40
## 592 1961 2 451 0 0 0
## 593 1960 1 264 290 0 0
## 594 1937 1 231 0 0 0
## 595 1960 2 450 0 22 0
## 596 1950 1 264 0 192 0
## 597 1953 1 312 120 24 0
## 598 1966 2 480 0 230 0
## 599 1957 1 292 0 88 0
## 600 1959 1 480 150 0 0
## 601 1958 2 441 490 0 0
## 602 1956 1 384 0 0 0
## 603 1952 1 240 0 0 0
## 604 1971 2 636 0 0 0
## 605 1953 2 400 0 32 0
## 606 1957 2 540 0 40 0
## 607 1957 1 301 0 72 0
## 608 1958 2 484 0 0 0
## 609 1948 1 390 0 0 66
## 610 1932 1 288 84 0 96
## 611 1997 1 308 88 108 0
## 612 1968 4 1488 0 0 100
## 613 1990 2 372 200 48 0
## 614 1958 2 495 0 304 144
## 615 1960 1 401 36 82 0
## 616 1972 2 576 0 0 0
## 617 1959 1 311 0 240 0
## 618 1962 1 299 0 0 0
## 619 1994 2 576 108 0 0
## 620 1954 1 280 0 0 0
## 621 1954 1 240 0 0 0
## 622 NA 0 0 0 0 0
## 623 1955 2 480 0 0 0
## 624 1954 1 350 0 0 0
## 625 1963 1 264 0 0 0
## 626 2008 1 308 0 175 0
## 627 1948 2 400 0 0 0
## 628 1910 1 330 192 50 0
## 629 1950 1 256 0 0 0
## 630 1915 1 308 0 0 248
## 631 NA 0 0 0 172 109
## 632 1958 2 440 187 0 0
## 633 1920 1 225 0 0 236
## 634 NA 0 0 0 28 0
## 635 1940 2 828 0 0 126
## 636 1930 1 200 0 0 96
## 637 NA 0 0 0 165 30
## 638 1959 3 869 0 20 0
## 639 1949 1 297 76 0 120
## 640 NA 0 0 0 0 0
## 641 1950 2 420 192 0 60
## 642 1935 1 288 0 192 0
## 643 1961 1 308 0 0 139
## 644 1930 2 393 0 0 219
## 645 NA 0 0 240 77 0
## 646 1920 2 400 0 24 144
## 647 1950 2 342 0 299 0
## 648 1959 1 308 0 0 0
## 649 1959 1 308 0 0 0
## 650 1992 1 288 265 0 0
## 651 1945 1 252 210 0 0
## 652 1950 1 240 0 30 0
## 653 1941 1 240 0 0 0
## 654 1926 1 200 38 112 0
## 655 1940 1 240 0 11 0
## 656 1924 2 370 0 0 48
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## 1390 1999 3 646 197 80 0
## 1391 1997 2 467 120 48 0
## 1392 1998 3 555 128 84 0
## 1393 1995 2 610 275 170 0
## 1394 2003 2 420 140 0 0
## 1395 2003 2 545 0 68 0
## 1396 2006 2 578 0 105 0
## 1397 2005 2 572 144 36 0
## 1398 2005 2 502 136 43 0
## 1399 1950 2 528 147 0 0
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## 1403 NA 0 0 32 0 0
## 1404 2003 2 502 0 274 0
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## 1406 2004 2 380 0 40 0
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## 1455 NA 0 0 0 0 0
## 1456 1970 1 286 0 24 0
## 1457 1960 2 576 474 0 0
## 1458 NA 0 0 80 32 0
## 1459 1993 3 650 190 48 0
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## 1459 0 0 0 0 11 2006dim(test_data_num)## [1] 1459 37sum(is.na(test_data_num))## [1] 330dim(test_data_char)## [1] 1459 43sum(is.na(test_data_char))## [1] 6670#Char data could be reasonable set to the mode, but for simplicity replacing with "None"
test_data_char[is.na(test_data_char)] = "None"
#Ensure integrity of NA cleanse in Cat data
summary(test_data_char)## MSZoning Street Alley LotShape
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## LandContour Utilities LotConfig LandSlope
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Neighborhood Condition1 Condition2 BldgType
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## HouseStyle RoofStyle RoofMatl Exterior1st
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Exterior2nd MasVnrType ExterQual ExterCond
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Foundation BsmtQual BsmtCond BsmtExposure
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## BsmtFinType1 BsmtFinType2 Heating HeatingQC
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## CentralAir Electrical KitchenQual Functional
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## FireplaceQu GarageType GarageFinish GarageQual
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## GarageCond PavedDrive PoolQC Fence
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## MiscFeature SaleType SaleCondition
## Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :characterdim(test_data_char)## [1] 1459 43sum(is.na(test_data_char))## [1] 0#Full test data set combine
clean_test_data=cbind(test_data_num,test_data_char)
dim(clean_test_data)## [1] 1459 80#Create Prediction results for SalesPrice
#Prep data to be tested
#Prediction_Test=test_data_num %>% select(OverallQual,GrLivArea,GarageCars,YearBuilt,TotalBsmtSF)
OverallQual = test_data_num %>% select(OverallQual)
GrLivArea = test_data_num %>% select(GrLivArea)
GarageCars = test_data_num %>% select(GarageCars)
YearBuilt = test_data_num %>% select(YearBuilt)
TotalBsmtSF = test_data_num %>% select(TotalBsmtSF)
#MR Model 3 - Create predictions
Prediction_Test_data = coef(MR_Model_3)[1] + coef(MR_Model_3)[2]*OverallQual + coef(MR_Model_3)[3]*GrLivArea + coef(MR_Model_3)[4]*GarageCars + coef(MR_Model_3)[5]*YearBuilt + coef(MR_Model_3)[6]*TotalBsmtSF
head(Prediction_Test_data)## OverallQual
## 1 107531.6
## 2 162428.6
## 3 171531.7
## 4 190893.5
## 5 224010.0
## 6 187092.4dim(Prediction_Test_data)## [1] 1459 1summary(Prediction_Test_data)## OverallQual
## Min. :-10809
## 1st Qu.:128068
## Median :171367
## Mean :178761
## 3rd Qu.:223080
## Max. :592413
## NA's :2#The sums have filtered into a single column named OverallQual
#This column actually equates to the predicted final SalePrice
Prediction_Test_data = Prediction_Test_data %>% rename(SalePrice = OverallQual)
head(Prediction_Test_data)## SalePrice
## 1 107531.6
## 2 162428.6
## 3 171531.7
## 4 190893.5
## 5 224010.0
## 6 187092.4#Column header now equals SalePrice, the next step is to combine with the full Test Data
dim(clean_test_data)## [1] 1459 80clean_test_data_final=cbind(clean_test_data,Prediction_Test_data)
dim(clean_test_data_final)## [1] 1459 81hist(clean_test_data_final$SalePrice/1000, xlab = "Predicted SalePrice ($1000s)", ylab = "Houses Sold", main="Predicted Sale Price Histogram")
#Export Data
#write.csv(x = data.frame(Id = clean_test_data_final$Id, SalePrice = clean_test_data_final$SalePrice), row.names = F, file = "./TFL_submission.csv")