BUSINESS QUESTION: Which are the top 5 products that are going to be more profitable for the company?
What data do we have?
New product attributes and existing product attributes.
Data cleaning
Data exploration
Pre-process: feature selection (correlation matrix) & feature engineering
Modalization: linear regresion, KNN, SVM and Random forest
Error analysis
## Observations: 80 ## Variables: 18 ## $ ProductType <chr> "PC", "PC", "PC", "Laptop", "Laptop", "A... ## $ ProductNum <dbl> 101, 102, 103, 104, 105, 106, 107, 108, ... ## $ Price <dbl> 949.00, 2249.99, 399.00, 409.99, 1079.99... ## $ x5StarReviews <dbl> 3, 2, 3, 49, 58, 83, 11, 33, 16, 10, 21,... ## $ x4StarReviews <dbl> 3, 1, 0, 19, 31, 30, 3, 19, 9, 1, 2, 25,... ## $ x3StarReviews <dbl> 2, 0, 0, 8, 11, 10, 0, 12, 2, 1, 2, 6, 5... ## $ x2StarReviews <dbl> 0, 0, 0, 3, 7, 9, 0, 5, 0, 0, 4, 3, 0, 8... ## $ x1StarReviews <dbl> 0, 0, 0, 9, 36, 40, 1, 9, 2, 0, 15, 3, 1... ## $ PositiveServiceReview <dbl> 2, 1, 1, 7, 7, 12, 3, 5, 2, 2, 2, 9, 2, ... ## $ NegativeServiceReview <dbl> 0, 0, 0, 8, 20, 5, 0, 3, 1, 0, 1, 2, 0, ... ## $ Recommendproduct <dbl> 0.9, 0.9, 0.9, 0.8, 0.7, 0.3, 0.9, 0.7, ... ## $ BestSellersRank <dbl> 1967, 4806, 12076, 109, 268, 64, NA, 2, ... ## $ ShippingWeight <dbl> 25.80, 50.00, 17.40, 5.70, 7.00, 1.60, 7... ## $ ProductDepth <dbl> 23.94, 35.00, 10.50, 15.00, 12.90, 5.80,... ## $ ProductWidth <dbl> 6.62, 31.75, 8.30, 9.90, 0.30, 4.00, 10.... ## $ ProductHeight <dbl> 16.89, 19.00, 10.20, 1.30, 8.90, 1.00, 1... ## $ ProfitMargin <dbl> 0.15, 0.25, 0.08, 0.08, 0.09, 0.05, 0.05... ## $ Volume <dbl> 12, 8, 12, 196, 232, 332, 44, 132, 64, 4...
Transformation to factor:
fact_var <- c("ProductType","ProductNum")
ex_prod[,fact_var] <- apply(ex_prod[,fact_var], 2, as.factor)
Giving names to the rows:
ex_prod <- tibble::column_to_rownames(.data = ex_prod,
var = "ProductNum")
ex_prod$ProductNum <- NULL
## ## Variables sorted by number of missings: ## Variable Count ## BestSellersRank 0.1875 ## ProductType 0.0000 ## Price 0.0000 ## x5StarReviews 0.0000 ## x4StarReviews 0.0000 ## x3StarReviews 0.0000 ## x2StarReviews 0.0000 ## x1StarReviews 0.0000 ## PositiveServiceReview 0.0000 ## NegativeServiceReview 0.0000 ## Recommendproduct 0.0000 ## ShippingWeight 0.0000 ## ProductDepth 0.0000 ## ProductWidth 0.0000 ## ProductHeight 0.0000 ## ProfitMargin 0.0000 ## Volume 0.0000
# train and test train_id <- createDataPartition(y = ex_prod$Volume, p = 0.80, list = F) train <- ex_prod[train_id,] test <- ex_prod[-train_id,] # create linear regression model mod_lm <- lm(formula = Volume ~ ., data = train)
| metric | train | test |
|---|---|---|
| RMSE | 0 | 0 |
| R^2 | 100 % | 100 % |
Main predictors:
| metric | train | test |
|---|---|---|
| RMSE | 0 | 0 |
| R^2 | 100 % | 100 % |
Main predictors:
The model is overfitted again.
| metric | train | test |
|---|---|---|
| RMSE | 307.07 | 276.19 |
| R^2 | 71.77 % | 80.47 % |
My model is not overfitted, but has a very low performance. Let’s check where it is failing!
| product_num | ProductType | Pos_Ser | Neg_Ser | Recomend | Vol |
|---|---|---|---|---|---|
| 132 | ExtendedWarranty | 0 | 3 | 0.4 | 0 |
| 133 | ExtendedWarranty | 0 | 1 | 0.6 | 20 |
| 134 | ExtendedWarranty | 280 | 8 | 0.9 | 1232 |
| 135 | ExtendedWarranty | 280 | 8 | 0.9 | 1232 |
| 136 | ExtendedWarranty | 280 | 8 | 0.9 | 1232 |
| 137 | ExtendedWarranty | 280 | 8 | 0.9 | 1232 |
| 138 | ExtendedWarranty | 280 | 8 | 0.9 | 1232 |
| 139 | ExtendedWarranty | 280 | 8 | 0.9 | 1232 |
| 140 | ExtendedWarranty | 280 | 8 | 0.9 | 1232 |
| 141 | ExtendedWarranty | 280 | 8 | 0.9 | 1232 |
| metric | train | test |
|---|---|---|
| RMSE | 333.82 | 241.01 |
| R^2 | 65.46 % | 69.35 % |
The model has improved a little bit. Let’s see how is performing to the categories we are interested.
# defining variables to create the model
rel_var <- c("x4","x2","Pos_Ser","Neg_Ser","Recomend","Vol","PC","Laptop",
"Netb","Smart_Ph")
# cross validation
ctrl <- caret::trainControl(method = "repeatedcv",
number = 10,
repeats = 3)
# modalization
mod_5knn_caret <- caret::train(Vol ~.,
method = "knn",
data = train[,rel_var],
trControl = ctrl,
preProcess = c("center","scale"))
| metric | train | test |
|---|---|---|
| RMSE | 248.68 | 153.56 |
| R^2 | 84.6 % | 90.4 % |
set.seed(123)
mod_6rf <- caret::train(Vol ~ .,
method = "rf",
data = train[,rel_var],
trControl = ctrl)
| metric | train | test |
|---|---|---|
| RMSE | 102.03 | 84.67 |
| R^2 | 97 % | 98.03 % |
set.seed(123)
mod_7svm <- caret::train(Vol ~ .,
method = "svmLinear",
data = train[,rel_var],
trControl = ctrl)
| metric | train | test |
|---|---|---|
| RMSE | 373.13 | 334.86 |
| R^2 | 61.16 % | 44.33 % |
