Apply2
Tindra Bergstrand
2024-09-19
Goal: To predict the the price of IKEA furniture Click here for the data
Import data
ikea <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-03/ikea.csv')## New names:
## Rows: 3694 Columns: 14
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (7): name, category, old_price, link, other_colors, short_description, d... dbl
## (6): ...1, item_id, price, depth, height, width lgl (1): sellable_online
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`Explore Data
skimr::skim(ikea)| Name | ikea |
| Number of rows | 3694 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| character | 7 |
| logical | 1 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| name | 0 | 1 | 3 | 27 | 0 | 607 | 0 |
| category | 0 | 1 | 4 | 36 | 0 | 17 | 0 |
| old_price | 0 | 1 | 4 | 13 | 0 | 365 | 0 |
| link | 0 | 1 | 52 | 163 | 0 | 2962 | 0 |
| other_colors | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| short_description | 0 | 1 | 3 | 63 | 0 | 1706 | 0 |
| designer | 0 | 1 | 3 | 1261 | 0 | 381 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| sellable_online | 0 | 1 | 0.99 | TRU: 3666, FAL: 28 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1.00 | 1846.50 | 1066.51 | 0 | 923.25 | 1846.5 | 2769.75 | 3693 | ▇▇▇▇▇ |
| item_id | 0 | 1.00 | 48632396.79 | 28887094.10 | 58487 | 20390574.00 | 49288078.0 | 70403572.75 | 99932615 | ▇▇▇▇▇ |
| price | 0 | 1.00 | 1078.21 | 1374.65 | 3 | 180.90 | 544.7 | 1429.50 | 9585 | ▇▁▁▁▁ |
| depth | 1463 | 0.60 | 54.38 | 29.96 | 1 | 38.00 | 47.0 | 60.00 | 257 | ▇▃▁▁▁ |
| height | 988 | 0.73 | 101.68 | 61.10 | 1 | 67.00 | 83.0 | 124.00 | 700 | ▇▂▁▁▁ |
| width | 589 | 0.84 | 104.47 | 71.13 | 1 | 60.00 | 80.0 | 140.00 | 420 | ▇▅▂▁▁ |
data_ikea1 <- ikea %>%
# Treat missing values
select(-old_price, -link, -...1) %>%
na.omit() %>%
# Log transform variables with pos-skewed distributions
mutate(price = log(price))Identify good predictors
# Category
data_ikea1 %>%
ggplot(aes(price, category)) +
geom_boxplot()
# Designer
data_ikea1 %>%
#tokenize title
unnest_tokens(output = word, input = short_description) %>%
# calculate avg rent per word
group_by(word) %>%
summarise(price = mean(price),
n = n()) %>%
ungroup() %>%
filter(n > 10, !str_detect(word, "\\d")) %>%
slice_max(order_by = price, n = 10) %>%
# Plot
ggplot(aes(price, fct_reorder(word, price))) +
geom_point() +
labs(y = "Producsts")
EDA shortcut
# Step 1:Prepare data
#Select
#take out short description, and keep designer
binarized_table <- data_ikea1 %>%
select(price, height, width, depth, category, other_colors, sellable_online) %>%
binarize()
binarized_table %>% glimpse()## Rows: 1,899
## Columns: 35
## $ `price__-Inf_5.68697535633982` <dbl> 1, 1, 0, 1, 1, 1, 0, …
## $ price__5.68697535633982_6.52209279817015 <dbl> 0, 0, 1, 0, 0, 0, 1, …
## $ price__6.52209279817015_7.37085996851068 <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ price__7.37085996851068_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `height__-Inf_71` <dbl> 0, 1, 0, 0, 0, 0, 0, …
## $ height__71_92 <dbl> 0, 0, 1, 0, 0, 0, 0, …
## $ height__92_171 <dbl> 1, 0, 0, 1, 1, 1, 1, …
## $ height__171_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `width__-Inf_60` <dbl> 1, 0, 1, 1, 1, 1, 1, …
## $ width__60_93 <dbl> 0, 1, 0, 0, 0, 0, 0, …
## $ width__93_161.5 <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ width__161.5_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `depth__-Inf_40` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ depth__40_47 <dbl> 0, 0, 1, 1, 1, 1, 1, …
## $ depth__47_60 <dbl> 1, 1, 0, 0, 0, 0, 0, …
## $ depth__60_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ category__Bar_furniture <dbl> 1, 1, 1, 1, 1, 1, 1, …
## $ category__Beds <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Bookcases_&_shelving_units` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Cabinets_&_cupboards` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ category__Chairs <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Chests_of_drawers_&_drawer_units` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Children's_furniture` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ category__Nursery_furniture <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ category__Outdoor_furniture <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Sideboards,_buffets_&_console_tables` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Sofas_&_armchairs` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Tables_&_desks` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__TV_&_media_furniture` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ category__Wardrobes <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__-OTHER` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ other_colors__No <dbl> 0, 1, 1, 1, 1, 1, 1, …
## $ other_colors__Yes <dbl> 1, 0, 0, 0, 0, 0, 0, …
## $ sellable_online__1 <dbl> 1, 1, 1, 1, 1, 1, 1, …
## $ `sellable_online__-OTHER` <dbl> 0, 0, 0, 0, 0, 0, 0, …# Step 2: Correlate
corr_tbl <- binarized_table %>%
correlate(price__7.37085996851068_Inf)
corr_tbl## # A tibble: 35 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 price 7.37085996851068_Inf 1
## 2 width 161.5_Inf 0.579
## 3 depth 60_Inf 0.447
## 4 category Sofas_&_armchairs 0.379
## 5 width -Inf_60 -0.374
## 6 price -Inf_5.68697535633982 -0.336
## 7 price 6.52209279817015_7.37085996851068 -0.333
## 8 price 5.68697535633982_6.52209279817015 -0.331
## 9 category Wardrobes 0.279
## 10 height -Inf_71 -0.277
## # ℹ 25 more rows# Step 3: Plot
corr_tbl %>%
plot_correlation_funnel()## Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Build models
Split data
# Split into train and test data set
set.seed(1234)
ikea_split <- rsample::initial_split(data_ikea1, prop = 0.75)
ikea_train <- training(ikea_split)
ikea_test <- testing(ikea_split)# Specify recipe
ikea_recipe <- recipe(price ~ ., data = ikea_train) %>%
update_role(item_id, new_role = "id variable") %>%
step_tokenize(short_description) %>%
step_tokenfilter(short_description, max_tokens = 100) %>%
step_tfidf(short_description) %>%
step_other(category) %>%
step_dummy(category, one_hot = TRUE) %>%
step_YeoJohnson(width, height) %>%
step_impute_knn(all_predictors())
ikea_recipe %>% prep() %>% juice() %>% glimpse()## Rows: 1,424
## Columns: 118
## $ item_id <dbl> 19282962, 29320911, 49276549…
## $ name <fct> SÖDERHAMN, PAX / VINGROM, NO…
## $ sellable_online <lgl> TRUE, TRUE, TRUE, TRUE, TRUE…
## $ other_colors <fct> No, No, Yes, No, No, Yes, Ye…
## $ designer <fct> "Ola Wihlborg", "IKEA of Swe…
## $ depth <dbl> 151, 60, 47, 52, 47, 47, 44,…
## $ height <dbl> 12.95187, 21.33670, 14.11837…
## $ width <dbl> 9.034855, 7.442056, 5.426853…
## $ price <dbl> 7.595890, 7.833996, 6.429719…
## $ tfidf_short_description_1 <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_120x40x64 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_140x200 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_147x147 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_150x44x236 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_150x60x236 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_150x66x236 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_2 <dbl> 0.8840660, 0.0000000, 0.5304…
## $ tfidf_short_description_200x66x236 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_25x51x70 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_3 <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_4 <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_41x101 <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_41x61 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_5 <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_50x51x70 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_60x50x128 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_61x101 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_74 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_75 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_80x30x202 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_99x44x56 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_and <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_armchair <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_armrest <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_armrests <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_backrest <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_bar <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_baskets <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_bed <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_bench <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_bookcase <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_box <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_cabinet <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_cabinets <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_castors <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_chair <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_chaise <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_changing <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_chest <dbl> 0.0000000, 0.0000000, 0.5867…
## $ `tfidf_short_description_children's` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_clothes <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_cm <dbl> 0.0000000, 0.2905183, 0.1743…
## $ tfidf_short_description_combination <dbl> 0.0000000, 0.6909039, 0.0000…
## $ tfidf_short_description_corner <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_cover <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_desk <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_door <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_doors <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_drawer <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_drawers <dbl> 0.0000000, 0.0000000, 0.5004…
## $ tfidf_short_description_feet <dbl> 0.00000, 0.00000, 0.00000, 0…
## $ tfidf_short_description_foldable <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_for <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_frame <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_glass <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_highchair <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_in <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_inserts <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_junior <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_leg <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_legs <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_lock <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_longue <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_mesh <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_modular <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_module <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_mounted <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_of <dbl> 0.0000000, 0.0000000, 0.5753…
## $ tfidf_short_description_on <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_outdoor <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_panel <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_plinth <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_rail <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_seat <dbl> 0.7549716, 0.0000000, 0.0000…
## $ tfidf_short_description_section <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_sections <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_shelf <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_shelves <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_shelving <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_sliding <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_smart <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_sofa <dbl> 0.7660576, 0.0000000, 0.0000…
## $ tfidf_short_description_step <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_stool <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_storage <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_table <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_top <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_tv <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_underframe <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_unit <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_upright <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_w <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_wall <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_wardrobe <dbl> 0.0000000, 0.7815769, 0.0000…
## $ tfidf_short_description_wire <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_with <dbl> 0.0000000, 0.0000000, 0.0000…
## $ category_Bookcases...shelving.units <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ category_Cabinets...cupboards <dbl> 0, 0, 0, 0, 0, 1, 0, 1, 0, 0…
## $ category_Chairs <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ category_Chests.of.drawers...drawer.units <dbl> 0, 0, 1, 0, 1, 0, 1, 0, 1, 0…
## $ category_Sofas...armchairs <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ category_Tables...desks <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ category_TV...media.furniture <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ category_Wardrobes <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ category_other <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…# Specify model
xgboost_spec <-
boost_tree(trees = tune(), min_n = tune(), mtry = tune(), learn_rate = tune()) %>%
set_mode("regression") %>%
set_engine("xgboost")
# Combine recipe and model using workflow
xgboost_workflow <-
workflow() %>%
add_recipe(ikea_recipe) %>%
add_model(xgboost_spec)
# Create cross-validation folds
set.seed(2345)
ikea_cv <- vfold_cv(ikea_train, v = 5)
# Tune hyperperameters
set.seed(3456)
xgboost_tune <-
tune_grid(xgboost_workflow,
resamples = ikea_cv,
grid = 5)## i Creating pre-processing data to finalize unknown parameter: mtry## → A | error: Some columns are non-numeric. The data cannot be converted to numeric matrix: 'name', 'sellable_online', 'other_colors', 'designer'.##
There were issues with some computations A: x1
There were issues with some computations A: x3
There were issues with some computations A: x5
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There were issues with some computations A: x24
There were issues with some computations A: x25## Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
## information.