COE JUNE 2016 for Category A Round 1
LIM KAH KHENG (jkklim@hotmail.com)
Last Updated - 2016-06-08
Introduction
The previous May model round 2 predicted model of 47536 managed to have an close approximate of May Round 2 Actual Premium 47020, with differences of $518. The model will now add in new data and attempt to forecast the June 2016 COE Premium Category A Round 1.
Data source is from here.
Determine COE Premium for NEW vehicle bid
## Warning in TentativeRoughFix(boruta.train): There are no Tentative attributes! Returning original
## object.## meanImp medianImp minImp maxImp normHits decision
## PQP 48.33234 48.94760 44.55557 50.87981 1 Confirmed
## QUOTA 21.94294 21.93750 19.69719 23.26416 1 Confirmed
## BIDS 16.62142 16.60232 15.79419 17.41780 1 Confirmed
## [1] "PQP" "QUOTA"We will create some linear regression model equations to forecast PREMIUM based on these variables.
##
## Call:
## lm(formula = PREMIUM ~ PQP + QUOTA, data = traindata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18872 -3359 -542 3819 17090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.157e+04 2.040e+03 5.672 6.98e-08 ***
## PQP 8.563e-01 2.944e-02 29.082 < 2e-16 ***
## QUOTA -3.628e+00 9.928e-01 -3.655 0.000355 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5874 on 151 degrees of freedom
## Multiple R-squared: 0.8654, Adjusted R-squared: 0.8637
## F-statistic: 485.6 on 2 and 151 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = PREMIUM ~ PQP, data = traindata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17987.8 -3414.2 -592.2 3987.7 17867.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.289e+03 1.737e+03 4.197 4.58e-05 ***
## PQP 8.829e-01 2.967e-02 29.763 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6109 on 152 degrees of freedom
## Multiple R-squared: 0.8535, Adjusted R-squared: 0.8526
## F-statistic: 885.8 on 1 and 152 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = PREMIUM ~ PQP + QUOTA + BIDS, data = traindata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18170.6 -3563.3 -347.1 3764.1 16716.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11494.9871 2039.4858 5.636 8.37e-08 ***
## PQP 0.8610 0.0297 28.991 < 2e-16 ***
## QUOTA -0.4822 2.9180 -0.165 0.869
## BIDS -2.1524 1.8775 -1.146 0.253
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5868 on 150 degrees of freedom
## Multiple R-squared: 0.8666, Adjusted R-squared: 0.8639
## F-statistic: 324.9 on 3 and 150 DF, p-value: < 2.2e-16The PQP is a 3 month moving average and it is 46454 for June 2016 (number given by LTA site)
Using H2O algorithm with GBM (For data-scientist only )
library(h2o)## Loading required package: statmod##
## ----------------------------------------------------------------------
##
## Your next step is to start H2O:
## > h2o.init()
##
## For H2O package documentation, ask for help:
## > ??h2o
##
## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit http://docs.h2o.ai
##
## ----------------------------------------------------------------------##
## Attaching package: 'h2o'## The following objects are masked from 'package:stats':
##
## sd, var## The following objects are masked from 'package:base':
##
## %*%, %in%, &&, ||, apply, as.factor, as.numeric, colnames, colnames<-, ifelse,
## is.character, is.factor, is.numeric, log, log10, log1p, log2, round, signif, trunclocalH2O <- h2o.init(nthreads = -1)##
## H2O is not running yet, starting it now...
##
## Note: In case of errors look at the following log files:
## C:\Users\admin\AppData\Local\Temp\Rtmpwp73tI/h2o_admin_started_from_r.out
## C:\Users\admin\AppData\Local\Temp\Rtmpwp73tI/h2o_admin_started_from_r.err
##
##
## Starting H2O JVM and connecting: Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 1 seconds 205 milliseconds
## H2O cluster version: 3.8.1.3
## H2O cluster name: H2O_started_from_R_admin_gcz764
## H2O cluster total nodes: 1
## H2O cluster total memory: 7.10 GB
## H2O cluster total cores: 8
## H2O cluster allowed cores: 8
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## R Version: R version 3.3.0 (2016-05-03)h2o.init()## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 1 seconds 485 milliseconds
## H2O cluster version: 3.8.1.3
## H2O cluster name: H2O_started_from_R_admin_gcz764
## H2O cluster total nodes: 1
## H2O cluster total memory: 7.10 GB
## H2O cluster total cores: 8
## H2O cluster allowed cores: 8
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## R Version: R version 3.3.0 (2016-05-03)#split data into datafame
samp <- sample(nrow(traindata), 0.7 * nrow(traindata))
training <- traindata[samp, ]
testing <- traindata[-samp, ]
#convert to H2O frame
train.h2o <- as.h2o(traindata); test.h2o <- as.h2o(testing)##
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|==========================================================================================| 100%### values below for columns
y.dep <- 4 #interested in PREMIUM COLUMNS
x.indep <- c(5:7) # use all varibles COLUMNS from PQP + BIDS + QUOTA
#GBM
system.time(
gbm.model <- h2o.gbm(y=y.dep, x=x.indep, training_frame = train.h2o, ntrees = 1000, max_depth = 4, learn_rate = 0.01, seed = 1122)
)##
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|==========================================================================================| 100%## user system elapsed
## 0.14 0.00 4.70h2o.varimp(gbm.model)## Variable Importances:
## variable relative_importance scaled_importance percentage
## 1 PQP 1612447219712.000000 1.000000 0.849386
## 2 BIDS 176865198080.000000 0.109687 0.093167
## 3 QUOTA 109056024576.000000 0.067634 0.057447#h2o.performance(gbm.model)
# predict against test data
#predict.gbm <- as.data.frame(h2o.predict(gbm.model, test.h2o))
###############################################################
# i want to put in my figures to predict, so i put in PQP
##############################################################
mypqpdata <- data.frame(PQP=46454)
#convert to h20 frame
result_premium <- as.h2o(mypqpdata)##
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|==========================================================================================| 100%predict.gbm <- as.data.frame(h2o.predict(gbm.model, result_premium))##
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