| title | author | date | output | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
Consumer Brand Preference Predictions |
Jennifer Brosnahan |
9/12/2020 |
|
An Electronics Company would like for us to predict which computer brand customers from a new region will prefer, Acer or Sony. Doing so will help to determine inventory needs for new clientele and individual customer brand preferences for robust in-store and online marketing efforts.
The objective is to build predictive models and choose a model that can predict consumer computer brand preferene with at least 80% accuracy on test data. Ideal goal is a model that can predict brand preference with at least 90% level of certainty.
# Loading packages
library(tidyverse)
library(caret)
library(ggplot2)
library(corrplot)
library(openxlsx)
library(h2o)
library(knitr)
library(kableExtra)# training data
complete <- read.csv(file.path('C:/Users/jlbro/OneDrive/C3T2/C3T2', 'complete.csv'), stringsAsFactors = TRUE)
# testing data
incomplete <- read.csv(file.path('C:/Users/jlbro/OneDrive/C3T2/C3T2', 'incomplete.csv'), stringsAsFactors = TRUE)# check structure of training data
str(complete)## 'data.frame': 9898 obs. of 7 variables:
## $ salary : num 119807 106880 78021 63690 50874 ...
## $ age : int 45 63 23 51 20 56 24 62 29 41 ...
## $ elevel : int 0 1 0 3 3 3 4 3 4 1 ...
## $ car : int 14 11 15 6 14 14 8 3 17 5 ...
## $ zipcode: int 4 6 2 5 4 3 5 0 0 4 ...
## $ credit : num 442038 45007 48795 40889 352951 ...
## $ brand : int 0 1 0 1 0 1 1 1 0 1 ...
- 9898 total observations with 7 total features
- Six features are customer description variables, one feature is consumer brand preference, 'brand'
- The data description tells us the codes for brand: Acer=0 and Sony=1
# check descriptive stats
summary(complete)## salary age elevel car
## Min. : 20000 Min. :20.00 Min. :0.000 Min. : 1.00
## 1st Qu.: 52082 1st Qu.:35.00 1st Qu.:1.000 1st Qu.: 6.00
## Median : 84950 Median :50.00 Median :2.000 Median :11.00
## Mean : 84871 Mean :49.78 Mean :1.983 Mean :10.52
## 3rd Qu.:117162 3rd Qu.:65.00 3rd Qu.:3.000 3rd Qu.:15.75
## Max. :150000 Max. :80.00 Max. :4.000 Max. :20.00
## zipcode credit brand
## Min. :0.000 Min. : 0 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:120807 1st Qu.:0.0000
## Median :4.000 Median :250607 Median :1.0000
## Mean :4.041 Mean :249176 Mean :0.6217
## 3rd Qu.:6.000 3rd Qu.:374640 3rd Qu.:1.0000
## Max. :8.000 Max. :500000 Max. :1.0000
summary(complete$brand)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 1.0000 0.6217 1.0000 1.0000
# histogram of 'brand'
ggplot(complete) +
geom_histogram(aes(brand), stat = 'count', bins = 2)
# check for NAs
sum(is.na(complete))## [1] 0
# change data type and values
complete$brand <- as.factor(complete$brand)
str(complete)## 'data.frame': 9898 obs. of 7 variables:
## $ salary : num 119807 106880 78021 63690 50874 ...
## $ age : int 45 63 23 51 20 56 24 62 29 41 ...
## $ elevel : int 0 1 0 3 3 3 4 3 4 1 ...
## $ car : int 14 11 15 6 14 14 8 3 17 5 ...
## $ zipcode: int 4 6 2 5 4 3 5 0 0 4 ...
## $ credit : num 442038 45007 48795 40889 352951 ...
## $ brand : Factor w/ 2 levels "0","1": 1 2 1 2 1 2 2 2 1 2 ...
set.seed(123)
# createDataPartition() 75% and 25%
index1 <- createDataPartition(complete$brand, p=0.75, list = FALSE)
train1 <- complete[ index1,]
test1 <- complete[-index1,]
# Check structure of trainSet
str(train1)## 'data.frame': 7424 obs. of 7 variables:
## $ salary : num 119807 106880 78021 63690 130813 ...
## $ age : int 45 63 23 51 56 24 62 29 48 52 ...
## $ elevel : int 0 1 0 3 3 4 3 4 4 1 ...
## $ car : int 14 11 15 6 14 8 3 17 16 6 ...
## $ zipcode: int 4 6 2 5 3 5 0 0 5 0 ...
## $ credit : num 442038 45007 48795 40889 135943 ...
## $ brand : Factor w/ 2 levels "0","1": 1 2 1 2 2 2 2 1 2 1 ...
# set cross validation
control <- trainControl(method = 'repeatedcv',
number=10,
repeats = 1)set.seed(123)
# train algorithm
rf1 <- train(brand~.,
data = train1,
method = 'rf',
trControl = control,
tuneLength = 1)
rf1## Random Forest
##
## 7424 samples
## 6 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 1 times)
## Summary of sample sizes: 6681, 6681, 6681, 6682, 6681, 6683, ...
## Resampling results:
##
## Accuracy Kappa
## 0.9172925 0.8246223
##
## Tuning parameter 'mtry' was held constant at a value of 2
set.seed(123)
# train
rf2 <- train(brand~.,
data = train1,
method = 'rf',
trControl=control,
tuneLength = 5)
rf2## Random Forest
##
## 7424 samples
## 6 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 1 times)
## Summary of sample sizes: 6681, 6681, 6681, 6682, 6681, 6683, ...
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 2 0.9176981 0.8252764
## 3 0.9176985 0.8252930
## 4 0.9171598 0.8240410
## 5 0.9155429 0.8203682
## 6 0.9132536 0.8155551
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 3.
- Both Random forest models are about the same. We will use Random Forest 1 with an mtry of 2 and 91.8% accuracy, as it is simpler and faster model and meets client goal of at least 80% accuracy.
- Kappa is 82.5%, which is a more useful determination of accuracy if predicted class is imbalanced, as it helps normalize an imbalance in the classes.
# variable importance using ggplot
ggplot(varImp(rf1, scale=FALSE)) +
geom_bar(stat = 'identity') +
ggtitle('Variable Importance of Top Random Forest Model')
rfPreds <- predict(rf1, newdata = test1)# predict using type = 'prob' helps see prediction for each observation
rfProbs <- predict(rf1, newdata = test1, type = 'prob')
head(rfProbs, 10)## 0 1
## 5 0.600 0.400
## 10 0.092 0.908
## 14 0.918 0.082
## 30 0.040 0.960
## 31 0.004 0.996
## 38 0.172 0.828
## 39 0.496 0.504
## 45 0.646 0.354
## 55 0.194 0.806
## 56 0.926 0.074
confusionMatrix(data = rfPreds, test1$brand)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 861 108
## 1 75 1430
##
## Accuracy : 0.926
## 95% CI : (0.915, 0.936)
## No Information Rate : 0.6217
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.8438
##
## Mcnemar's Test P-Value : 0.01801
##
## Sensitivity : 0.9199
## Specificity : 0.9298
## Pos Pred Value : 0.8885
## Neg Pred Value : 0.9502
## Prevalence : 0.3783
## Detection Rate : 0.3480
## Detection Prevalence : 0.3917
## Balanced Accuracy : 0.9248
##
## 'Positive' Class : 0
##
- Confusion matrix shows 92.6% accuracy, 92% sensitivity (% of positives we are catching), and 93% specificity (% of negatives we are catching). This is a good model.
# postResample reveals if it will do well in real world or if it is overfitting
postResample(rfPreds, test1$brand)## Accuracy Kappa
## 0.9260307 0.8438283
# compare predictions to actual within same data frame
compare_rf <- data.frame(test1,rfPreds)
head(compare_rf, 100)## salary age elevel car zipcode credit brand rfPreds
## 5 50873.62 20 3 14 4 352951.498 0 0
## 10 37803.33 41 1 5 4 493219.269 1 1
## 14 82474.58 33 4 13 3 424657.497 0 0
## 30 107710.32 75 0 16 2 209002.407 1 1
## 31 148495.44 62 2 9 1 377495.295 1 1
## 38 49692.10 42 4 8 2 462826.149 1 1
## 39 101454.63 26 0 18 2 22960.800 0 1
## 45 87634.05 59 2 5 6 206711.940 1 0
## 55 81015.26 63 0 7 7 250036.000 1 1
## 56 88405.44 36 4 8 7 195042.678 0 0
## 63 83185.07 62 4 2 8 163108.438 1 0
## 64 31430.24 47 4 10 2 177297.720 1 1
## 70 131957.81 65 4 11 1 238253.007 1 1
## 74 131927.71 63 1 8 1 424565.963 1 1
## 75 110539.06 64 4 11 6 257476.590 1 1
## 76 133162.37 69 0 7 8 413996.477 1 1
## 81 137368.94 57 4 12 8 415156.282 1 1
## 82 43377.96 30 0 6 2 190336.160 1 1
## 83 85919.99 75 3 7 1 43412.866 1 1
## 89 47399.24 21 4 2 3 362188.027 1 1
## 97 34759.48 58 4 16 1 396818.591 1 1
## 98 141628.04 75 2 18 3 326686.025 1 1
## 109 27102.74 46 0 16 2 293842.154 1 1
## 116 96839.45 53 1 18 4 393853.363 0 0
## 118 100820.01 70 4 4 6 463076.230 1 1
## 119 129274.99 29 2 12 0 98820.083 1 1
## 124 43727.81 43 3 19 1 199837.255 1 1
## 130 23964.83 57 1 17 4 165441.501 1 1
## 132 134663.82 51 1 2 1 476990.362 1 1
## 133 27705.56 41 4 16 5 213908.798 1 1
## 134 68420.38 26 4 14 2 485034.815 0 0
## 136 45257.89 40 2 11 4 374850.874 1 1
## 140 109830.17 24 1 9 8 144156.978 1 1
## 143 58089.91 45 2 9 5 460439.314 1 1
## 144 138483.51 34 4 17 7 158932.977 1 1
## 147 137794.61 24 0 13 2 216481.904 1 1
## 151 111872.72 41 0 8 1 13674.562 0 0
## 153 133279.78 41 2 15 6 260987.137 1 1
## 158 106841.00 78 3 2 5 386222.361 1 1
## 161 92422.59 52 2 8 2 308886.506 0 0
## 163 66938.52 47 1 11 8 15198.151 1 1
## 169 119888.63 41 0 3 6 328779.040 0 0
## 170 128751.15 66 0 2 0 394010.991 1 1
## 175 132530.65 25 1 5 4 145812.348 1 1
## 176 23520.41 72 2 8 7 313318.661 0 1
## 177 29819.80 57 4 4 4 186745.141 1 1
## 179 68054.64 61 1 20 3 277300.510 0 0
## 182 130455.16 56 4 20 1 92299.373 1 1
## 190 101445.01 23 2 15 2 243688.702 1 1
## 192 54661.20 32 0 18 7 168218.382 0 0
## 196 103158.63 44 1 5 0 244790.504 0 0
## 197 58555.51 62 2 19 3 389687.140 0 0
## 204 48777.81 28 0 15 7 500000.000 0 1
## 206 127367.22 78 0 11 2 252011.588 1 1
## 207 121775.83 30 1 11 8 0.000 1 1
## 208 138178.88 53 4 20 3 102848.193 1 1
## 216 68366.52 43 4 14 2 355413.199 1 1
## 219 130119.76 46 0 1 1 277130.032 1 1
## 229 96160.32 61 2 6 3 328170.531 1 0
## 230 61926.03 62 2 2 2 353903.981 0 0
## 231 89248.67 42 4 15 0 453443.387 0 0
## 233 73906.88 32 4 17 8 499164.343 0 0
## 234 30284.09 22 4 13 7 280717.295 1 1
## 238 75378.27 49 0 19 6 92529.983 1 0
## 244 51667.39 70 2 1 8 347967.023 0 0
## 245 127191.59 39 0 18 1 166839.029 1 1
## 250 31327.02 37 3 14 5 244495.552 1 1
## 253 59217.18 58 0 10 2 173339.583 0 1
## 259 104663.80 77 1 19 2 309124.886 1 1
## 261 140924.81 80 2 3 0 299265.761 1 1
## 264 106795.67 58 2 4 8 137897.615 1 0
## 270 115429.48 57 0 1 4 145421.506 0 0
## 274 52866.81 73 1 6 5 61389.592 0 0
## 276 45116.39 28 4 16 6 281700.230 1 1
## 278 127504.25 47 4 6 5 422383.878 1 1
## 279 35821.32 58 2 11 3 327520.885 1 1
## 281 150000.00 80 1 19 3 249547.659 1 1
## 287 65666.11 77 0 8 4 11105.343 0 0
## 292 86242.63 22 4 10 4 206786.427 0 0
## 296 100577.60 35 2 8 7 288933.139 1 1
## 305 79986.35 49 3 2 6 500000.000 0 0
## 306 44697.96 70 3 3 2 195563.429 0 0
## 307 32394.89 77 4 7 5 383085.772 0 0
## 308 143742.35 80 4 11 8 129507.011 1 1
## 310 138335.93 50 1 6 6 0.000 1 1
## 313 111228.29 75 2 9 7 390871.986 1 1
## 315 43943.42 78 3 10 2 140100.814 0 0
## 316 103863.52 44 4 17 5 120606.688 0 0
## 320 35888.78 59 2 14 0 16701.637 0 0
## 327 43465.95 79 3 11 4 305635.722 0 0
## 334 126044.39 26 2 2 1 113170.066 1 1
## 336 116184.32 57 4 20 5 472757.667 0 0
## 339 142443.02 80 3 14 2 217056.567 1 1
## 342 141371.40 57 2 20 3 6758.351 1 1
## 345 41864.34 60 3 15 5 58115.384 1 0
## 353 135945.74 50 3 13 1 238705.912 1 1
## 354 96689.22 54 2 10 3 487481.529 0 0
## 355 68762.77 23 3 5 2 114843.394 0 0
## 356 122551.72 49 4 6 7 302519.882 0 0
## 357 75542.85 43 2 20 4 282457.597 0 0
# summarize and plot
summary(rfPreds)## 0 1
## 969 1505
ggplot(compare_rf) +
geom_histogram(aes(rfPreds), stat = 'count', bins = 2) +
xlab('Brand Preference Predictions') +
ggtitle('Distribution of Brand Preference Predictions')
# check structure of new dataset
str(incomplete)## 'data.frame': 5000 obs. of 7 variables:
## $ salary : num 150000 82524 115647 141443 149211 ...
## $ age : int 76 51 34 22 56 26 64 50 26 46 ...
## $ elevel : int 1 1 0 3 0 4 3 3 2 3 ...
## $ car : int 3 8 10 18 5 12 1 9 3 18 ...
## $ zipcode: int 3 3 2 2 3 1 2 0 4 6 ...
## $ credit : num 377980 141658 360980 282736 215667 ...
## $ brand : int 1 0 1 1 1 1 1 1 1 0 ...
# check for NAs
sum(is.na(incomplete))## [1] 0
# change data type
incomplete$brand <- as.factor(incomplete$brand)
# check structure and summary
str(incomplete)## 'data.frame': 5000 obs. of 7 variables:
## $ salary : num 150000 82524 115647 141443 149211 ...
## $ age : int 76 51 34 22 56 26 64 50 26 46 ...
## $ elevel : int 1 1 0 3 0 4 3 3 2 3 ...
## $ car : int 3 8 10 18 5 12 1 9 3 18 ...
## $ zipcode: int 3 3 2 2 3 1 2 0 4 6 ...
## $ credit : num 377980 141658 360980 282736 215667 ...
## $ brand : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 2 2 2 1 ...
summary(incomplete)## salary age elevel car
## Min. : 20000 Min. :20.00 Min. :0.000 Min. : 1.0
## 1st Qu.: 52590 1st Qu.:35.00 1st Qu.:1.000 1st Qu.: 6.0
## Median : 86221 Median :50.00 Median :2.000 Median :11.0
## Mean : 85794 Mean :49.94 Mean :2.009 Mean :10.6
## 3rd Qu.:118535 3rd Qu.:65.00 3rd Qu.:3.000 3rd Qu.:16.0
## Max. :150000 Max. :80.00 Max. :4.000 Max. :20.0
## zipcode credit brand
## Min. :0.000 Min. : 0 0:4937
## 1st Qu.:2.000 1st Qu.:122311 1: 63
## Median :4.000 Median :250974
## Mean :4.038 Mean :249546
## 3rd Qu.:6.000 3rd Qu.:375653
## Max. :8.000 Max. :500000
- Summary reveals some brands are answered as 1
- Deeper dive of dataset unveils that first 102 rows of brand have been filled in
- The rest (103:5000) are unanswered and need to be predicted
# make brand predictions on new customer data 'incomplete'
incompletePreds <- data.frame(predict(rf1, newdata = incomplete))
names(incompletePreds)[1] <- 'Predictions'
str(incompletePreds)## 'data.frame': 5000 obs. of 1 variable:
## $ Predictions: Factor w/ 2 levels "0","1": 2 1 2 2 2 2 2 2 2 1 ...
# postResample on first 102 observations to determine how well model did on test df
subset_incomplete <- incomplete %>% slice(1:102)
subset_PR <- incompletePreds %>% slice(1:102)
postResample(subset_PR, subset_incomplete$brand)## Accuracy Kappa
## 0.8529412 0.6808511
- Accuracy is high at 85.3%, kappa is lower, perhaps because of small sample size
# compare first 102 actual observations to predictions
bind_cols(subset_incomplete$brand, subset_PR)## New names:
## * NA -> ...1
## ...1 Predictions
## 1 1 1
## 2 0 0
## 3 1 1
## 4 1 1
## 5 1 1
## 6 1 1
## 7 1 1
## 8 1 1
## 9 1 1
## 10 0 0
## 11 0 0
## 12 0 0
## 13 1 1
## 14 0 1
## 15 1 1
## 16 1 1
## 17 0 0
## 18 1 1
## 19 1 1
## 20 0 0
## 21 0 0
## 22 1 1
## 23 0 1
## 24 0 1
## 25 1 1
## 26 1 1
## 27 1 0
## 28 1 1
## 29 1 1
## 30 1 1
## 31 1 1
## 32 0 0
## 33 1 1
## 34 1 1
## 35 1 1
## 36 1 1
## 37 0 1
## 38 1 1
## 39 1 1
## 40 1 1
## 41 1 1
## 42 0 1
## 43 1 1
## 44 1 1
## 45 1 1
## 46 1 1
## 47 1 1
## 48 0 0
## 49 0 0
## 50 0 0
## 51 0 0
## 52 1 1
## 53 0 1
## 54 1 1
## 55 1 1
## 56 1 1
## 57 0 0
## 58 1 1
## 59 0 0
## 60 0 1
## 61 1 1
## 62 1 1
## 63 1 1
## 64 1 0
## 65 1 1
## 66 0 0
## 67 1 1
## 68 0 1
## 69 0 0
## 70 0 0
## 71 1 1
## 72 1 0
## 73 1 1
## 74 0 0
## 75 0 0
## 76 1 1
## 77 1 1
## 78 1 1
## 79 1 0
## 80 0 0
## 81 0 0
## 82 1 1
## 83 0 0
## 84 1 1
## 85 1 0
## 86 0 1
## 87 1 1
## 88 0 1
## 89 1 1
## 90 0 0
## 91 0 0
## 92 0 0
## 93 0 0
## 94 0 0
## 95 0 0
## 96 1 1
## 97 1 1
## 98 0 0
## 99 1 1
## 100 1 1
## 101 1 1
## 102 1 1
# summarize and plot predictions
summary(incompletePreds)## Predictions
## 0:1893
## 1:3107
ggplot(incompletePreds) +
geom_histogram(aes(Predictions), stat = 'count', bins = 2)
- Ratio of distribution of predictions is very similar to training and testing: 38% predicted to prefer Acer and 62% predicted to prefer Sony
# predictions in new data
compare_incomplete <- data.frame(incomplete,incompletePreds)
head(compare_incomplete, 150)## salary age elevel car zipcode credit brand Predictions
## 1 150000.00 76 1 3 3 377980.10 1 1
## 2 82523.84 51 1 8 3 141657.61 0 0
## 3 115646.64 34 0 10 2 360980.36 1 1
## 4 141443.39 22 3 18 2 282736.32 1 1
## 5 149211.27 56 0 5 3 215667.29 1 1
## 6 46202.25 26 4 12 1 150419.40 1 1
## 7 125821.24 64 3 1 2 173429.39 1 1
## 8 20141.14 50 3 9 0 447716.53 1 1
## 9 135261.85 26 2 3 4 223821.22 1 1
## 10 83273.93 46 3 18 6 213961.37 0 0
## 11 51475.90 66 0 15 6 82059.50 0 0
## 12 60787.32 76 0 18 4 249276.46 0 0
## 13 61494.52 44 0 14 4 205624.59 1 1
## 14 125904.77 42 3 7 4 459119.17 0 1
## 15 87232.20 80 2 3 4 199156.53 1 1
## 16 28966.72 22 3 16 4 147625.70 1 1
## 17 82393.18 30 3 18 2 418293.00 0 0
## 18 27724.52 37 0 9 1 431552.11 1 1
## 19 94998.07 73 1 20 8 352723.33 1 1
## 20 56005.12 35 0 14 3 205351.36 0 0
## 21 70241.27 62 0 12 8 330532.14 0 0
## 22 91852.88 69 4 9 5 193700.98 1 1
## 23 47920.93 40 4 17 2 258034.24 0 1
## 24 125879.93 50 0 10 2 123780.89 0 1
## 25 105885.11 62 2 10 4 37183.54 1 1
## 26 140824.94 72 4 20 0 411726.02 1 1
## 27 28483.93 70 3 14 4 340911.01 1 0
## 28 58353.19 46 1 19 1 125476.82 1 1
## 29 116269.45 67 1 6 0 273318.21 1 1
## 30 36971.40 56 4 10 7 16307.77 1 1
## 31 29183.09 33 4 3 3 20704.64 1 1
## 32 40807.21 63 4 11 8 382144.47 0 0
## 33 89954.32 67 2 14 1 350530.05 1 1
## 34 23478.99 57 4 5 2 200986.73 1 1
## 35 118374.98 29 4 10 6 105954.54 1 1
## 36 138330.49 20 0 4 1 446821.54 1 1
## 37 21345.30 58 2 17 1 229348.26 0 1
## 38 149459.65 67 0 17 0 396511.17 1 1
## 39 103910.39 70 2 20 7 229000.75 1 1
## 40 24663.77 61 4 15 6 407435.55 1 1
## 41 23043.77 55 0 2 6 471847.02 1 1
## 42 104313.06 39 2 14 6 449116.26 0 1
## 43 86609.84 72 0 7 1 381696.70 1 1
## 44 136232.75 77 4 16 8 133090.01 1 1
## 45 116754.00 61 1 13 2 430749.29 1 1
## 46 129412.07 54 2 6 8 305531.03 1 1
## 47 125083.79 68 1 17 6 258844.54 1 1
## 48 78782.08 45 4 1 5 6240.85 0 0
## 49 60337.24 75 1 19 3 326574.84 0 0
## 50 80167.41 50 3 17 4 157123.72 0 0
## 51 86721.10 56 4 17 5 43340.83 0 0
## 52 56468.84 48 2 8 2 243646.34 1 1
## 53 69616.74 58 2 9 5 419945.18 0 1
## 54 147166.38 61 4 2 0 397447.39 1 1
## 55 130543.49 37 3 8 3 239709.04 1 1
## 56 83138.88 66 3 12 4 489417.94 1 1
## 57 35455.15 74 3 16 5 134287.44 0 0
## 58 121516.56 22 0 20 7 150662.23 1 1
## 59 88058.88 30 1 1 6 397398.48 0 0
## 60 73174.23 44 1 1 3 225058.20 0 1
## 61 99548.13 24 4 20 0 258620.92 1 1
## 62 57385.88 48 1 17 1 97202.55 1 1
## 63 130638.11 43 1 19 4 472569.38 1 1
## 64 103270.80 38 0 6 0 82446.66 1 0
## 65 53190.17 51 2 5 7 0.00 1 1
## 66 85553.38 34 0 7 0 146998.36 0 0
## 67 110068.36 66 3 4 1 17685.91 1 1
## 68 124031.25 52 3 5 6 0.00 0 1
## 69 95841.68 54 1 19 8 469014.49 0 0
## 70 66201.72 38 2 18 6 423150.07 0 0
## 71 111837.75 73 4 16 0 327286.27 1 1
## 72 94928.86 20 1 19 8 337952.90 1 0
## 73 145613.88 74 0 11 1 386122.71 1 1
## 74 54744.78 72 2 18 2 238827.56 0 0
## 75 116250.86 41 2 20 8 63761.49 0 0
## 76 128098.86 76 4 16 2 84307.29 1 1
## 77 131505.30 51 2 6 3 424148.52 1 1
## 78 103473.38 75 0 11 0 62123.99 1 1
## 79 118932.14 41 3 16 6 465578.02 1 0
## 80 46620.67 72 4 19 3 252582.28 0 0
## 81 80746.76 33 0 13 4 420476.92 0 0
## 82 111334.90 32 4 3 7 384233.39 1 1
## 83 71933.45 63 0 1 1 433606.15 0 0
## 84 112042.06 68 3 1 8 80403.31 1 1
## 85 74222.61 52 1 11 1 348048.09 1 0
## 86 21070.43 67 2 16 2 22896.27 0 1
## 87 115647.13 64 2 17 3 411999.49 1 1
## 88 124255.99 43 3 11 4 235693.47 0 1
## 89 24228.85 72 3 5 0 347027.23 1 1
## 90 42176.58 63 3 20 2 185901.91 0 0
## 91 47377.00 67 2 2 3 455074.87 0 0
## 92 51763.11 75 3 8 1 56667.39 0 0
## 93 103896.05 46 0 9 3 425032.80 0 0
## 94 79496.43 34 0 13 0 363107.54 0 0
## 95 83138.10 22 4 13 4 159897.04 0 0
## 96 143929.62 22 0 12 4 259574.36 1 1
## 97 23235.85 75 4 17 6 375881.49 1 1
## 98 92428.27 37 2 5 3 255023.91 0 0
## 99 92539.75 80 4 3 7 45027.70 1 1
## 100 84296.09 72 2 8 7 131900.47 1 1
## 101 129891.41 32 1 2 7 213871.10 1 1
## 102 112032.61 77 1 13 4 0.00 1 1
## 103 64788.45 79 4 5 1 77918.10 0 0
## 104 135639.88 27 4 19 6 19186.18 0 1
## 105 105017.64 78 0 8 0 60901.15 0 1
## 106 30651.28 70 3 4 6 274973.34 0 0
## 107 124914.89 20 3 14 0 422490.72 0 1
## 108 51554.60 43 1 6 8 403873.21 0 1
## 109 41756.59 30 0 9 8 261237.01 0 1
## 110 40777.18 24 3 18 2 389602.49 0 1
## 111 22852.18 20 3 19 3 471134.00 0 1
## 112 46972.72 58 4 1 3 202696.61 0 1
## 113 81360.09 57 2 1 5 464026.01 0 0
## 114 118399.67 52 3 2 2 349837.50 0 0
## 115 97798.00 51 1 17 0 322701.69 0 0
## 116 34013.23 37 4 2 7 35509.87 0 1
## 117 39825.38 67 0 8 4 449697.25 0 0
## 118 42887.81 68 0 8 5 311664.92 0 0
## 119 136958.06 62 3 1 2 471262.04 0 1
## 120 73966.94 28 1 4 6 427243.06 0 0
## 121 58996.46 43 2 9 4 327786.82 0 1
## 122 147256.23 54 0 7 1 494074.74 0 1
## 123 137604.47 80 2 17 1 129093.43 0 1
## 124 67246.30 70 1 7 4 241223.87 0 0
## 125 111248.54 53 1 18 4 464879.92 0 0
## 126 138762.62 27 3 18 7 270868.15 0 1
## 127 130104.22 57 0 3 0 0.00 0 1
## 128 66419.40 70 1 1 6 430196.55 0 0
## 129 105905.17 78 1 14 4 221805.03 0 1
## 130 66718.69 22 3 3 0 359392.59 0 0
## 131 105371.38 28 0 17 3 118819.02 0 1
## 132 150000.00 20 3 8 1 52783.80 0 1
## 133 78824.05 70 1 13 6 36615.61 0 1
## 134 104203.67 20 0 5 4 472282.73 0 1
## 135 81031.09 20 3 7 8 281226.60 0 0
## 136 35394.28 26 1 18 4 461495.78 0 1
## 137 93010.35 44 4 14 8 500000.00 0 0
## 138 89977.25 29 2 13 7 119006.38 0 0
## 139 71357.39 62 0 18 3 413141.14 0 0
## 140 86564.45 33 2 9 5 477016.95 0 0
## 141 141029.82 22 2 10 8 166924.91 0 1
## 142 104962.64 45 4 10 1 344391.50 0 0
## 143 91708.12 50 3 11 8 446718.26 0 0
## 144 79017.63 24 1 11 1 182439.18 0 0
## 145 126987.44 77 3 10 8 276517.61 0 1
## 146 50959.12 69 2 11 4 158345.53 0 0
## 147 97361.54 52 2 2 1 472890.20 0 0
## 148 57871.07 27 4 18 5 40516.26 0 0
## 149 114719.99 35 2 1 6 371147.62 0 1
## 150 127173.60 33 2 5 7 278513.53 0 1
kable(compare_incomplete[1:200,], format = 'html', caption = 'Brand Preference Predictions on New Customer Dataset', digits=3) %>% kable_styling(bootstrap_options = 'striped', full_width = FALSE)| salary | age | elevel | car | zipcode | credit | brand | Predictions |
|---|---|---|---|---|---|---|---|
| 150000.00 | 76 | 1 | 3 | 3 | 377980.10 | 1 | 1 |
| 82523.84 | 51 | 1 | 8 | 3 | 141657.61 | 0 | 0 |
| 115646.64 | 34 | 0 | 10 | 2 | 360980.36 | 1 | 1 |
| 141443.39 | 22 | 3 | 18 | 2 | 282736.32 | 1 | 1 |
| 149211.27 | 56 | 0 | 5 | 3 | 215667.29 | 1 | 1 |
| 46202.25 | 26 | 4 | 12 | 1 | 150419.40 | 1 | 1 |
| 125821.24 | 64 | 3 | 1 | 2 | 173429.39 | 1 | 1 |
| 20141.14 | 50 | 3 | 9 | 0 | 447716.53 | 1 | 1 |
| 135261.85 | 26 | 2 | 3 | 4 | 223821.22 | 1 | 1 |
| 83273.93 | 46 | 3 | 18 | 6 | 213961.37 | 0 | 0 |
| 51475.90 | 66 | 0 | 15 | 6 | 82059.50 | 0 | 0 |
| 60787.32 | 76 | 0 | 18 | 4 | 249276.46 | 0 | 0 |
| 61494.52 | 44 | 0 | 14 | 4 | 205624.59 | 1 | 1 |
| 125904.77 | 42 | 3 | 7 | 4 | 459119.17 | 0 | 1 |
| 87232.20 | 80 | 2 | 3 | 4 | 199156.53 | 1 | 1 |
| 28966.72 | 22 | 3 | 16 | 4 | 147625.70 | 1 | 1 |
| 82393.18 | 30 | 3 | 18 | 2 | 418293.00 | 0 | 0 |
| 27724.52 | 37 | 0 | 9 | 1 | 431552.11 | 1 | 1 |
| 94998.07 | 73 | 1 | 20 | 8 | 352723.33 | 1 | 1 |
| 56005.12 | 35 | 0 | 14 | 3 | 205351.36 | 0 | 0 |
| 70241.27 | 62 | 0 | 12 | 8 | 330532.14 | 0 | 0 |
| 91852.88 | 69 | 4 | 9 | 5 | 193700.98 | 1 | 1 |
| 47920.93 | 40 | 4 | 17 | 2 | 258034.24 | 0 | 1 |
| 125879.93 | 50 | 0 | 10 | 2 | 123780.89 | 0 | 1 |
| 105885.11 | 62 | 2 | 10 | 4 | 37183.54 | 1 | 1 |
| 140824.94 | 72 | 4 | 20 | 0 | 411726.02 | 1 | 1 |
| 28483.93 | 70 | 3 | 14 | 4 | 340911.01 | 1 | 0 |
| 58353.19 | 46 | 1 | 19 | 1 | 125476.82 | 1 | 1 |
| 116269.45 | 67 | 1 | 6 | 0 | 273318.21 | 1 | 1 |
| 36971.40 | 56 | 4 | 10 | 7 | 16307.77 | 1 | 1 |
| 29183.09 | 33 | 4 | 3 | 3 | 20704.64 | 1 | 1 |
| 40807.21 | 63 | 4 | 11 | 8 | 382144.47 | 0 | 0 |
| 89954.32 | 67 | 2 | 14 | 1 | 350530.05 | 1 | 1 |
| 23478.99 | 57 | 4 | 5 | 2 | 200986.73 | 1 | 1 |
| 118374.98 | 29 | 4 | 10 | 6 | 105954.54 | 1 | 1 |
| 138330.49 | 20 | 0 | 4 | 1 | 446821.54 | 1 | 1 |
| 21345.30 | 58 | 2 | 17 | 1 | 229348.26 | 0 | 1 |
| 149459.65 | 67 | 0 | 17 | 0 | 396511.17 | 1 | 1 |
| 103910.39 | 70 | 2 | 20 | 7 | 229000.75 | 1 | 1 |
| 24663.77 | 61 | 4 | 15 | 6 | 407435.55 | 1 | 1 |
| 23043.77 | 55 | 0 | 2 | 6 | 471847.02 | 1 | 1 |
| 104313.06 | 39 | 2 | 14 | 6 | 449116.26 | 0 | 1 |
| 86609.84 | 72 | 0 | 7 | 1 | 381696.70 | 1 | 1 |
| 136232.75 | 77 | 4 | 16 | 8 | 133090.01 | 1 | 1 |
| 116754.00 | 61 | 1 | 13 | 2 | 430749.29 | 1 | 1 |
| 129412.07 | 54 | 2 | 6 | 8 | 305531.03 | 1 | 1 |
| 125083.79 | 68 | 1 | 17 | 6 | 258844.54 | 1 | 1 |
| 78782.08 | 45 | 4 | 1 | 5 | 6240.85 | 0 | 0 |
| 60337.24 | 75 | 1 | 19 | 3 | 326574.84 | 0 | 0 |
| 80167.41 | 50 | 3 | 17 | 4 | 157123.72 | 0 | 0 |
| 86721.10 | 56 | 4 | 17 | 5 | 43340.83 | 0 | 0 |
| 56468.84 | 48 | 2 | 8 | 2 | 243646.34 | 1 | 1 |
| 69616.74 | 58 | 2 | 9 | 5 | 419945.18 | 0 | 1 |
| 147166.38 | 61 | 4 | 2 | 0 | 397447.39 | 1 | 1 |
| 130543.49 | 37 | 3 | 8 | 3 | 239709.04 | 1 | 1 |
| 83138.88 | 66 | 3 | 12 | 4 | 489417.94 | 1 | 1 |
| 35455.15 | 74 | 3 | 16 | 5 | 134287.44 | 0 | 0 |
| 121516.56 | 22 | 0 | 20 | 7 | 150662.23 | 1 | 1 |
| 88058.88 | 30 | 1 | 1 | 6 | 397398.48 | 0 | 0 |
| 73174.23 | 44 | 1 | 1 | 3 | 225058.20 | 0 | 1 |
| 99548.13 | 24 | 4 | 20 | 0 | 258620.92 | 1 | 1 |
| 57385.88 | 48 | 1 | 17 | 1 | 97202.55 | 1 | 1 |
| 130638.11 | 43 | 1 | 19 | 4 | 472569.38 | 1 | 1 |
| 103270.80 | 38 | 0 | 6 | 0 | 82446.66 | 1 | 0 |
| 53190.17 | 51 | 2 | 5 | 7 | 0.00 | 1 | 1 |
| 85553.38 | 34 | 0 | 7 | 0 | 146998.36 | 0 | 0 |
| 110068.36 | 66 | 3 | 4 | 1 | 17685.91 | 1 | 1 |
| 124031.25 | 52 | 3 | 5 | 6 | 0.00 | 0 | 1 |
| 95841.68 | 54 | 1 | 19 | 8 | 469014.49 | 0 | 0 |
| 66201.72 | 38 | 2 | 18 | 6 | 423150.07 | 0 | 0 |
| 111837.75 | 73 | 4 | 16 | 0 | 327286.27 | 1 | 1 |
| 94928.86 | 20 | 1 | 19 | 8 | 337952.90 | 1 | 0 |
| 145613.88 | 74 | 0 | 11 | 1 | 386122.71 | 1 | 1 |
| 54744.78 | 72 | 2 | 18 | 2 | 238827.56 | 0 | 0 |
| 116250.86 | 41 | 2 | 20 | 8 | 63761.49 | 0 | 0 |
| 128098.86 | 76 | 4 | 16 | 2 | 84307.29 | 1 | 1 |
| 131505.30 | 51 | 2 | 6 | 3 | 424148.52 | 1 | 1 |
| 103473.38 | 75 | 0 | 11 | 0 | 62123.99 | 1 | 1 |
| 118932.14 | 41 | 3 | 16 | 6 | 465578.02 | 1 | 0 |
| 46620.67 | 72 | 4 | 19 | 3 | 252582.28 | 0 | 0 |
| 80746.76 | 33 | 0 | 13 | 4 | 420476.92 | 0 | 0 |
| 111334.90 | 32 | 4 | 3 | 7 | 384233.39 | 1 | 1 |
| 71933.45 | 63 | 0 | 1 | 1 | 433606.15 | 0 | 0 |
| 112042.06 | 68 | 3 | 1 | 8 | 80403.31 | 1 | 1 |
| 74222.61 | 52 | 1 | 11 | 1 | 348048.09 | 1 | 0 |
| 21070.43 | 67 | 2 | 16 | 2 | 22896.27 | 0 | 1 |
| 115647.13 | 64 | 2 | 17 | 3 | 411999.49 | 1 | 1 |
| 124255.99 | 43 | 3 | 11 | 4 | 235693.47 | 0 | 1 |
| 24228.85 | 72 | 3 | 5 | 0 | 347027.23 | 1 | 1 |
| 42176.58 | 63 | 3 | 20 | 2 | 185901.91 | 0 | 0 |
| 47377.00 | 67 | 2 | 2 | 3 | 455074.87 | 0 | 0 |
| 51763.11 | 75 | 3 | 8 | 1 | 56667.39 | 0 | 0 |
| 103896.05 | 46 | 0 | 9 | 3 | 425032.79 | 0 | 0 |
| 79496.43 | 34 | 0 | 13 | 0 | 363107.54 | 0 | 0 |
| 83138.10 | 22 | 4 | 13 | 4 | 159897.04 | 0 | 0 |
| 143929.62 | 22 | 0 | 12 | 4 | 259574.36 | 1 | 1 |
| 23235.85 | 75 | 4 | 17 | 6 | 375881.49 | 1 | 1 |
| 92428.27 | 37 | 2 | 5 | 3 | 255023.91 | 0 | 0 |
| 92539.75 | 80 | 4 | 3 | 7 | 45027.70 | 1 | 1 |
| 84296.09 | 72 | 2 | 8 | 7 | 131900.47 | 1 | 1 |
| 129891.41 | 32 | 1 | 2 | 7 | 213871.10 | 1 | 1 |
| 112032.61 | 77 | 1 | 13 | 4 | 0.00 | 1 | 1 |
| 64788.45 | 79 | 4 | 5 | 1 | 77918.10 | 0 | 0 |
| 135639.88 | 27 | 4 | 19 | 6 | 19186.18 | 0 | 1 |
| 105017.64 | 78 | 0 | 8 | 0 | 60901.15 | 0 | 1 |
| 30651.28 | 70 | 3 | 4 | 6 | 274973.34 | 0 | 0 |
| 124914.89 | 20 | 3 | 14 | 0 | 422490.72 | 0 | 1 |
| 51554.60 | 43 | 1 | 6 | 8 | 403873.21 | 0 | 1 |
| 41756.59 | 30 | 0 | 9 | 8 | 261237.01 | 0 | 1 |
| 40777.18 | 24 | 3 | 18 | 2 | 389602.49 | 0 | 1 |
| 22852.18 | 20 | 3 | 19 | 3 | 471134.00 | 0 | 1 |
| 46972.72 | 58 | 4 | 1 | 3 | 202696.61 | 0 | 1 |
| 81360.10 | 57 | 2 | 1 | 5 | 464026.01 | 0 | 0 |
| 118399.67 | 52 | 3 | 2 | 2 | 349837.50 | 0 | 0 |
| 97798.00 | 51 | 1 | 17 | 0 | 322701.69 | 0 | 0 |
| 34013.23 | 37 | 4 | 2 | 7 | 35509.87 | 0 | 1 |
| 39825.38 | 67 | 0 | 8 | 4 | 449697.25 | 0 | 0 |
| 42887.81 | 68 | 0 | 8 | 5 | 311664.92 | 0 | 0 |
| 136958.05 | 62 | 3 | 1 | 2 | 471262.04 | 0 | 1 |
| 73966.94 | 28 | 1 | 4 | 6 | 427243.06 | 0 | 0 |
| 58996.46 | 43 | 2 | 9 | 4 | 327786.82 | 0 | 1 |
| 147256.23 | 54 | 0 | 7 | 1 | 494074.74 | 0 | 1 |
| 137604.47 | 80 | 2 | 17 | 1 | 129093.43 | 0 | 1 |
| 67246.30 | 70 | 1 | 7 | 4 | 241223.87 | 0 | 0 |
| 111248.54 | 53 | 1 | 18 | 4 | 464879.92 | 0 | 0 |
| 138762.62 | 27 | 3 | 18 | 7 | 270868.15 | 0 | 1 |
| 130104.22 | 57 | 0 | 3 | 0 | 0.00 | 0 | 1 |
| 66419.40 | 70 | 1 | 1 | 6 | 430196.54 | 0 | 0 |
| 105905.17 | 78 | 1 | 14 | 4 | 221805.03 | 0 | 1 |
| 66718.69 | 22 | 3 | 3 | 0 | 359392.59 | 0 | 0 |
| 105371.38 | 28 | 0 | 17 | 3 | 118819.02 | 0 | 1 |
| 150000.00 | 20 | 3 | 8 | 1 | 52783.80 | 0 | 1 |
| 78824.05 | 70 | 1 | 13 | 6 | 36615.61 | 0 | 1 |
| 104203.66 | 20 | 0 | 5 | 4 | 472282.73 | 0 | 1 |
| 81031.09 | 20 | 3 | 7 | 8 | 281226.60 | 0 | 0 |
| 35394.28 | 26 | 1 | 18 | 4 | 461495.78 | 0 | 1 |
| 93010.35 | 44 | 4 | 14 | 8 | 500000.00 | 0 | 0 |
| 89977.25 | 29 | 2 | 13 | 7 | 119006.38 | 0 | 0 |
| 71357.39 | 62 | 0 | 18 | 3 | 413141.14 | 0 | 0 |
| 86564.45 | 33 | 2 | 9 | 5 | 477016.95 | 0 | 0 |
| 141029.82 | 22 | 2 | 10 | 8 | 166924.91 | 0 | 1 |
| 104962.64 | 45 | 4 | 10 | 1 | 344391.50 | 0 | 0 |
| 91708.12 | 50 | 3 | 11 | 8 | 446718.26 | 0 | 0 |
| 79017.63 | 24 | 1 | 11 | 1 | 182439.18 | 0 | 0 |
| 126987.44 | 77 | 3 | 10 | 8 | 276517.61 | 0 | 1 |
| 50959.12 | 69 | 2 | 11 | 4 | 158345.54 | 0 | 0 |
| 97361.54 | 52 | 2 | 2 | 1 | 472890.20 | 0 | 0 |
| 57871.07 | 27 | 4 | 18 | 5 | 40516.26 | 0 | 0 |
| 114719.99 | 35 | 2 | 1 | 6 | 371147.62 | 0 | 1 |
| 127173.60 | 33 | 2 | 5 | 7 | 278513.53 | 0 | 1 |
| 22550.37 | 78 | 3 | 20 | 2 | 358223.86 | 0 | 1 |
| 41046.87 | 46 | 2 | 13 | 3 | 261612.27 | 0 | 1 |
| 124898.08 | 41 | 4 | 10 | 8 | 425863.76 | 0 | 1 |
| 126582.41 | 54 | 1 | 8 | 8 | 230828.11 | 0 | 1 |
| 102972.17 | 51 | 0 | 7 | 2 | 157549.97 | 0 | 0 |
| 28104.27 | 79 | 0 | 8 | 3 | 212529.63 | 0 | 0 |
| 91758.29 | 55 | 0 | 5 | 4 | 95280.30 | 0 | 0 |
| 64470.54 | 72 | 0 | 4 | 5 | 72924.57 | 0 | 0 |
| 70008.34 | 44 | 4 | 17 | 2 | 369048.20 | 0 | 1 |
| 43081.32 | 41 | 1 | 6 | 5 | 191982.40 | 0 | 1 |
| 81168.48 | 34 | 4 | 10 | 3 | 35099.86 | 0 | 0 |
| 37764.30 | 25 | 1 | 8 | 2 | 284129.43 | 0 | 1 |
| 150000.00 | 23 | 4 | 18 | 3 | 500000.00 | 0 | 1 |
| 30760.75 | 78 | 3 | 4 | 1 | 224080.10 | 0 | 0 |
| 115765.86 | 71 | 4 | 20 | 6 | 394825.63 | 0 | 1 |
| 50168.51 | 34 | 2 | 18 | 4 | 187646.35 | 0 | 0 |
| 108117.31 | 44 | 2 | 9 | 5 | 304615.65 | 0 | 0 |
| 117407.26 | 23 | 1 | 5 | 0 | 302195.48 | 0 | 1 |
| 47941.26 | 24 | 0 | 3 | 7 | 83615.72 | 0 | 0 |
| 98186.31 | 76 | 3 | 8 | 1 | 199271.62 | 0 | 1 |
| 123561.03 | 48 | 2 | 19 | 8 | 76055.51 | 0 | 0 |
| 85061.25 | 32 | 1 | 12 | 6 | 474664.16 | 0 | 0 |
| 42224.98 | 60 | 1 | 5 | 1 | 166459.62 | 0 | 0 |
| 118559.78 | 72 | 3 | 3 | 2 | 287256.55 | 0 | 1 |
| 120060.69 | 21 | 1 | 17 | 2 | 241135.20 | 0 | 1 |
| 62275.60 | 49 | 2 | 17 | 8 | 428596.18 | 0 | 1 |
| 48687.53 | 41 | 2 | 2 | 8 | 201617.39 | 0 | 1 |
| 124449.27 | 76 | 1 | 1 | 5 | 124080.94 | 0 | 1 |
| 102587.43 | 20 | 1 | 20 | 6 | 291874.29 | 0 | 1 |
| 37922.36 | 44 | 2 | 10 | 0 | 411478.22 | 0 | 1 |
| 20000.00 | 69 | 2 | 14 | 2 | 203408.69 | 0 | 1 |
| 20863.04 | 53 | 4 | 3 | 0 | 91408.71 | 0 | 1 |
| 97753.98 | 48 | 1 | 18 | 1 | 228509.30 | 0 | 0 |
| 76230.21 | 70 | 2 | 18 | 0 | 239003.97 | 0 | 1 |
| 61490.09 | 69 | 1 | 19 | 7 | 122582.23 | 0 | 0 |
| 71638.61 | 26 | 1 | 7 | 6 | 173247.38 | 0 | 0 |
| 119640.78 | 33 | 2 | 20 | 3 | 331233.65 | 0 | 1 |
| 85002.33 | 62 | 4 | 7 | 8 | 79004.52 | 0 | 1 |
| 126499.07 | 78 | 4 | 10 | 4 | 477178.64 | 0 | 1 |
| 78407.74 | 23 | 2 | 15 | 3 | 198359.20 | 0 | 0 |
| 78825.21 | 41 | 2 | 7 | 4 | 276219.76 | 0 | 0 |
| 84333.91 | 69 | 3 | 17 | 1 | 227553.51 | 0 | 1 |
| 141174.42 | 71 | 3 | 3 | 5 | 248924.01 | 0 | 1 |
| 36326.11 | 57 | 2 | 5 | 4 | 416774.03 | 0 | 1 |
| 57481.25 | 80 | 2 | 20 | 7 | 87090.64 | 0 | 0 |
| 76974.38 | 70 | 0 | 17 | 5 | 235786.55 | 0 | 1 |
| 122464.62 | 35 | 4 | 4 | 7 | 474694.34 | 0 | 1 |
| 115800.11 | 30 | 4 | 13 | 0 | 462889.38 | 0 | 1 |
| 43973.42 | 49 | 4 | 9 | 3 | 436988.91 | 0 | 1 |
| 138893.08 | 51 | 4 | 4 | 8 | 247379.14 | 0 | 1 |
- Continue product inventory ratio of Sony (62%) to Acer (38%) for new customer clientele
- Target new customer base with direct-to-consumer email and mailing promotional efforts based on predicted brand preference
- Deploy algorithm in online platform for upselling and recommender strategies during all client online shopping experiences