Goal: to predict the rental prices in the SF rental market Click here for the data.

Import Data

rent <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-07-05/rent.csv')

skimr::skim(rent)
Data summary
Name rent
Number of rows 200796
Number of columns 17
_______________________
Column type frequency:
character 8
numeric 9
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
post_id 0 1.00 9 14 0 200796 0
nhood 0 1.00 4 43 0 167 0
city 0 1.00 5 19 0 104 0
county 1394 0.99 4 13 0 10 0
address 196888 0.02 1 38 0 2869 0
title 2517 0.99 2 298 0 184961 0
descr 197542 0.02 13 16975 0 3025 0
details 192780 0.04 4 595 0 7667 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
date 0 1.00 20095718.38 44694.07 20000902.00 20050227.00 20110924.00 20120805.0 20180717.00 ▁▇▁▆▃
year 0 1.00 2009.51 4.48 2000.00 2005.00 2011.00 2012.0 2018.00 ▁▇▁▆▃
price 0 1.00 2135.36 1427.75 220.00 1295.00 1800.00 2505.0 40000.00 ▇▁▁▁▁
beds 6608 0.97 1.89 1.08 0.00 1.00 2.00 3.0 12.00 ▇▂▁▁▁
baths 158121 0.21 1.68 0.69 1.00 1.00 2.00 2.0 8.00 ▇▁▁▁▁
sqft 136117 0.32 1201.83 5000.22 80.00 750.00 1000.00 1360.0 900000.00 ▇▁▁▁▁
room_in_apt 0 1.00 0.00 0.04 0.00 0.00 0.00 0.0 1.00 ▇▁▁▁▁
lat 193145 0.04 37.67 0.35 33.57 37.40 37.76 37.8 40.43 ▁▁▅▇▁
lon 196484 0.02 -122.21 0.78 -123.20 -122.42 -122.26 -122.0 -74.20 ▇▁▁▁▁ 
data <- rent %>%
    
    # Treat missing values
    select(-address, -descr, -details, -lat, -lon, -date, -year, -room_in_apt) %>%
    na.omit() %>%
    
    # log transform variables with pos-skewed distributions
    mutate(price = log(price))

Explore Data

Identify good predictors.

sqft

data %>%
    ggplot(aes(price, sqft)) +
    scale_y_log10() +
    geom_point()

beds

data %>%
    ggplot(aes(price, as.factor(beds))) + 
    geom_boxplot()

title

data %>%
    
    # tokenize title
    unnest_tokens(output = word, input = title) %>%
    
    # 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 = 20) %>%
    
    # plot
    ggplot(aes(price, fct_reorder(word, price))) + 
    geom_point() + 
    
    labs(y = "Words in Title")

EDA shortcut

# Step 1: Prepare data
data_binarized_tbl <- data %>%
    select(-post_id, -title) %>%
    binarize()

data_binarized_tbl %>% glimpse()
## Rows: 14,394
## Columns: 85
## $ nhood__campbell                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `nhood__concord_/_pleasant_hill_/_martinez` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__cupertino                            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__daly_city                            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `nhood__danville_/_san_ramon`               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `nhood__dublin_/_pleasanton`                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `nhood__fairfield_/_vacaville`              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__foster_city                          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `nhood__hayward_/_castro_valley`            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__milpitas                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__mountain_view                        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__napa_county                          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__palo_alto                            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__petaluma                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `nhood__pittsburg_/_antioch`                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `nhood__rohnert_pk_/_cotati`                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__san_francisco                        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__san_jose_central                     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__san_jose_east                        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__san_jose_north                       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__san_jose_south                       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__san_jose_west                        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__san_mateo                            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__san_rafael                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__santa_clara                          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__santa_cruz                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__santa_rosa                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `nhood__SOMA_/_south_beach`                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__sunnyvale                            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood__union_city                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `nhood__vallejo_/_benicia`                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `nhood__willow_glen_/_cambrian`             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `nhood__-OTHER`                             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ city__cambrian                              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__campbell                              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__concord                               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__cupertino                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__daly_city                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__dublin                                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__fairfield                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__foster_city                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__hayward                               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__milpitas                              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__mountain_view                         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__napa_county                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__oakland                               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__palo_alto                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__petaluma                              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__pittsburg                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__rohnert_park                          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__san_francisco                         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__san_jose                              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__san_mateo                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__san_rafael                            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__san_ramon                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__santa_clara                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__santa_cruz                            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__santa_rosa                            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__sunnyvale                             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__union_city                            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city__vallejo                               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `city__-OTHER`                              <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ county__alameda                             <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ county__contra_costa                        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ county__marin                               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ county__napa                                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ county__san_francisco                       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ county__san_mateo                           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ county__santa_clara                         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ county__santa_cruz                          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ county__solano                              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ county__sonoma                              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `price__-Inf_7.52294091807237`              <dbl> 0, 1, 0, 1, 0, 1, 1, 0, 0,…
## $ price__7.52294091807237_7.80384330353877    <dbl> 0, 0, 1, 0, 0, 0, 0, 1, 1,…
## $ price__7.80384330353877_8.07868822922987    <dbl> 1, 0, 0, 0, 1, 0, 0, 0, 0,…
## $ price__8.07868822922987_Inf                 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `beds__-Inf_2`                              <dbl> 0, 1, 0, 1, 1, 1, 0, 0, 1,…
## $ beds__2_3                                   <dbl> 0, 0, 1, 0, 0, 0, 1, 1, 0,…
## $ beds__3_Inf                                 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `baths__-Inf_2`                             <dbl> 0, 1, 1, 1, 1, 1, 0, 0, 1,…
## $ baths__2_Inf                                <dbl> 1, 0, 0, 0, 0, 0, 1, 1, 0,…
## $ `sqft__-Inf_887`                            <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1,…
## $ sqft__887_1100                              <dbl> 0, 0, 0, 1, 0, 0, 0, 1, 0,…
## $ sqft__1100_1500                             <dbl> 0, 0, 1, 0, 1, 1, 0, 0, 0,…
## $ sqft__1500_Inf                              <dbl> 1, 0, 0, 0, 0, 0, 1, 0, 0,…
# Step 2: Correlate
data_corr_tbl <- data_binarized_tbl %>%
    correlate(price__8.07868822922987_Inf)

data_corr_tbl
## # A tibble: 85 × 3
##    feature bin                               correlation
##    <fct>   <chr>                                   <dbl>
##  1 price   8.07868822922987_Inf                    1    
##  2 city    san_francisco                           0.389
##  3 county  san_francisco                           0.389
##  4 price   -Inf_7.52294091807237                  -0.342
##  5 price   7.80384330353877_8.07868822922987      -0.330
##  6 price   7.52294091807237_7.80384330353877      -0.328
##  7 sqft    1500_Inf                                0.324
##  8 beds    -Inf_2                                 -0.254
##  9 beds    3_Inf                                   0.241
## 10 sqft    -Inf_887                               -0.240
## # ℹ 75 more rows
# Step 3: Plot
data_corr_tbl %>%
    plot_correlation_funnel()

Build Models

Split data

data <- sample_n(data, 100)

# Split into train and test datasets
set.seed(1234)
data_split <- rsample::initial_split(data)
data_train <- training(data_split)
data_test <- testing(data_split)

# Further split training dataset for cross-validation
set.seed(2345)
data_cv <- rsample::vfold_cv(data_train)
data_cv
## #  10-fold cross-validation 
## # A tibble: 10 × 2
##    splits         id    
##    <list>         <chr> 
##  1 <split [67/8]> Fold01
##  2 <split [67/8]> Fold02
##  3 <split [67/8]> Fold03
##  4 <split [67/8]> Fold04
##  5 <split [67/8]> Fold05
##  6 <split [68/7]> Fold06
##  7 <split [68/7]> Fold07
##  8 <split [68/7]> Fold08
##  9 <split [68/7]> Fold09
## 10 <split [68/7]> Fold10
library(usemodels)
usemodels::use_xgboost(price ~ ., data = data_train)
## xgboost_recipe <- 
##   recipe(formula = price ~ ., data = data_train) %>% 
##   step_zv(all_predictors()) 
## 
## xgboost_spec <- 
##   boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(), 
##     loss_reduction = tune(), sample_size = tune()) %>% 
##   set_mode("classification") %>% 
##   set_engine("xgboost") 
## 
## xgboost_workflow <- 
##   workflow() %>% 
##   add_recipe(xgboost_recipe) %>% 
##   add_model(xgboost_spec) 
## 
## set.seed(6804)
## xgboost_tune <-
##   tune_grid(xgboost_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))
# Specify recipe
xgboost_recipe <- 
  recipe(formula = price ~ ., data = data_train) %>%
    recipes::update_role(post_id, new_role = "id variable") %>%
    step_tokenize(title) %>%
    step_tokenfilter(title, max_tokens = 100) %>%
    step_tfidf(title) %>%
    step_other(nhood, city) %>%
    step_dummy(nhood, city, county, one_hot = TRUE) %>%
    step_YeoJohnson(sqft, beds, baths)

xgboost_recipe %>% prep() %>% juice() %>% glimpse()
## Rows: 75
## Columns: 121
## $ post_id                   <fct> 4258027824, pre2013_39262, pre2013_34423, 52…
## $ beds                      <dbl> 0.9186568, 1.7359906, 3.2101511, 1.7359906, …
## $ baths                     <dbl> 0.5447795, 0.7572607, 0.7572607, 0.5447795, …
## $ sqft                      <dbl> 2.953381, 3.044411, 3.078927, 2.983140, 3.11…
## $ price                     <dbl> 7.435438, 7.492760, 7.423568, 7.598399, 8.13…
## $ tfidf_title_1             <dbl> 0.0000000, 0.0000000, 0.0000000, 0.2737046, …
## $ tfidf_title_1.5           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_11            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_1100ft        <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_12            <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_1220ft        <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_1300ft2       <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_1400          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_15            <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_1700          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_1750          <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_1795          <dbl> 0.0000000, 0.7301316, 0.0000000, 0.0000000, …
## $ tfidf_title_1ba           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_1br           <dbl> 0.1949499, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_2             <dbl> 0.00000000, 0.25541901, 0.00000000, 0.212849…
## $ tfidf_title_2.5ba         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_2050          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_22            <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_24            <dbl> 0.3042215, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_2495          <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_25            <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_2500          <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_2900          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_2ba           <dbl> 0.0000000, 0.0000000, 0.4337816, 0.0000000, …
## $ tfidf_title_2bath         <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_2bd           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_2br           <dbl> 0.00000000, 0.26050490, 0.00000000, 0.000000…
## $ tfidf_title_3             <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_3ba           <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_3br           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_4             <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_4br           <dbl> 0.0000000, 0.0000000, 0.8202697, 0.0000000, …
## $ tfidf_title_850ft         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_a             <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_and           <dbl> 0.1783388, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_apartment     <dbl> 0.0000000, 0.0000000, 0.0000000, 0.4620981, …
## $ tfidf_title_available     <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_ba            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_bath          <dbl> 0.00000000, 0.00000000, 0.00000000, 0.208793…
## $ tfidf_title_baths         <dbl> 0.0000000, 0.5545177, 0.0000000, 0.0000000, …
## $ tfidf_title_beach         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_beautiful     <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_bed           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.3187312, …
## $ tfidf_title_bedroom       <dbl> 0.3187312, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_br            <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_city          <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_closet        <dbl> 0.2715080, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_condo         <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_dryer         <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_family        <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_floor         <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_fremont       <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_garage        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_great         <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_har           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_hide          <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_home          <dbl> 0.0000000, 0.0000000, 0.3301669, 0.3301669, …
## $ tfidf_title_house         <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_in            <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_included      <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_jose          <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_large         <dbl> 0.2050674, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_location      <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_map           <dbl> 0.2310491, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_mar           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_new           <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_newark        <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_north         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_now           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_oca           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_on            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_one           <dbl> 0.608443, 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_open          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_petaluma      <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_pets          <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_pic           <dbl> 0.2485961, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_posting       <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_remodeled     <dbl> 0.0000000, 0.5545177, 0.0000000, 0.0000000, …
## $ tfidf_title_resim         <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_restore       <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_san           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_single        <dbl> 0.0000000, 0.0000000, 0.4971922, 0.0000000, …
## $ tfidf_title_story         <dbl> 0.0000000, 0.0000000, 0.5430161, 0.0000000, …
## $ tfidf_title_stunning      <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.00…
## $ tfidf_title_this          <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_to            <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_top           <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_union         <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_very          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_w             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_washer        <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_welcome       <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_with          <dbl> 0.1861327, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_사진          <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_월            <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_이            <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_지도          <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, …
## $ tfidf_title_图片          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_地图          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ tfidf_title_月            <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nhood_dublin...pleasanton <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,…
## $ nhood_union.city          <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ nhood_other               <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,…
## $ city_dublin               <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,…
## $ city_san.francisco        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
## $ city_san.jose             <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ city_union.city           <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
## $ city_other                <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1,…
## $ county_alameda            <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0,…
## $ county_contra.costa       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ county_marin              <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ county_san.francisco      <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
## $ county_san.mateo          <dbl> 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1,…
## $ county_santa.clara        <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ county_santa.cruz         <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ county_sonoma             <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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(xgboost_recipe) %>% 
  add_model(xgboost_spec) 

# Tune hyperparameters 
set.seed(30220)
xgboost_tune <-
  tune_grid(xgboost_workflow, 
            resamples = data_cv, 
            grid = 5)
## Warning: package 'xgboost' was built under R version 4.3.3

Evaluate Models

Make Predictions