Project: SVM
2025-04-19
With Bedroom, Bathroom and Sq. ft:
library(e1071)
library(caret)## Loading required package: ggplot2## Loading required package: lattice# Loading data:
df <- read.csv("/Users/rupeshswarnakar/Downloads/STAT Learning/cleaned_apartments.csv", stringsAsFactors=FALSE)
# picking only the 3 numeric predictors + price:
df <- df[, c("price","bedrooms","bathrooms","square_feet")]
# coercing & dropping any bad rows:
df[] <- lapply(df, as.numeric)
df <- na.omit(df)
# binary target:
df$high_price <- factor(ifelse(df$price > 1500, "High", "Low"),
levels=c("Low","High"))
# Train/test split:
set.seed(42)
train_idx <- createDataPartition(df$high_price, p=0.8, list=FALSE)
train <- df[train_idx, ]
test <- df[-train_idx, ]
# Scaling predictors:
pred_cols <- c("bedrooms","bathrooms","square_feet")
pp <- preProcess(train[, pred_cols], method=c("center","scale"))
train_scaled <- predict(pp, train[, pred_cols])
test_scaled <- predict(pp, test[, pred_cols])
# reattaching the response:
train_final <- cbind(train_scaled, high_price=train$high_price)
test_final <- cbind(test_scaled, high_price=test$high_price)
num_features <- ncol(train_scaled)
# Fittinf SVM:
svm_base <- svm(
high_price ~ .,
data = train_final,
kernel = "radial",
cost = 1,
gamma = 1 / length(pred_cols) # gamma = 1 / # predictors
)
# Predicting & evaluating:
x_test <- test_final[, colnames(train_scaled), drop=FALSE]
pred_base <- predict(svm_base, newdata = x_test)
cm_base <- confusionMatrix(pred_base, test_final$high_price)
print(cm_base)## Confusion Matrix and Statistics
##
## Reference
## Prediction Low High
## Low 1253 499
## High 65 174
##
## Accuracy : 0.7167
## 95% CI : (0.6964, 0.7364)
## No Information Rate : 0.662
## P-Value [Acc > NIR] : 9.253e-08
##
## Kappa : 0.2484
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.9507
## Specificity : 0.2585
## Pos Pred Value : 0.7152
## Neg Pred Value : 0.7280
## Prevalence : 0.6620
## Detection Rate : 0.6293
## Detection Prevalence : 0.8800
## Balanced Accuracy : 0.6046
##
## 'Positive' Class : Low
## Plot:
library(ggplot2)
library(pROC)## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
##
## cov, smooth, var# Scatter:
ggplot(test_final, aes(x=bedrooms, y=square_feet, color=high_price)) +
geom_point(alpha=0.6,size=2) +
labs(title="Baseline: Bedrooms vs Square Feet",
x="Bedrooms (scaled)", y="Square Feet (scaled)") +
theme_minimal()
# Confusion‐matrix heatmap:
cm_df <- as.data.frame(cm_base$table)
ggplot(cm_df, aes(x=Prediction,y=Reference,fill=Freq)) +
geom_tile(color="white") +
geom_text(aes(label=Freq), color="white", size=5) +
scale_fill_gradient(low="steelblue",high="darkred") +
labs(title="Baseline Confusion Matrix") +
theme_minimal()
# ROC:
svm_prob_base <- svm(
high_price~., data=train_final,
kernel="radial", cost=1,
gamma=1/num_features,
probability=TRUE
)
pred_obj_base <- predict(svm_prob_base,
test_final[,1:num_features],
probability=TRUE)
probs_low_base <- attr(pred_obj_base,"probabilities")[,"Low"]
roc_base <- roc(test_final$high_price, probs_low_base,
levels=c("Low","High"))## Setting direction: controls > casesggroc(roc_base, size=1.2) +
geom_abline(lty="dashed",color="grey50") +
labs(title=sprintf("Baseline ROC (AUC=%.2f)",auc(roc_base)),
x="False Positive Rate", y="True Positive Rate") +
theme_minimal()
With Location:
library(caret)
library(e1071)
# 1. Loading data:
df <- read.csv(
"/Users/rupeshswarnakar/Downloads/STAT Learning/cleaned_apartments.csv",
stringsAsFactors = FALSE
)
keep <- c("price","bedrooms","bathrooms","square_feet",
"cityname","state","latitude","longitude")
df2 <- df[, keep]
df2[] <- lapply(df2, function(col) if(is.character(col)) col else as.numeric(col))
df2 <- na.omit(df2)
# Binary target:
df2$high_price <- factor(
ifelse(df2$price > 1500, "High", "Low"),
levels = c("Low","High")
)
# One‑hot encode cityname + state:
dummy_mod <- dummyVars(~ cityname + state, data = df2)
locs <- predict(dummy_mod, df2)
locs <- as.data.frame(locs)
# Canonicalize those dummy names to valid R names:
loc_cols <- make.names(colnames(locs))
# Build modeling frame:
model_df <- data.frame(
bedrooms = df2$bedrooms,
bathrooms = df2$bathrooms,
square_feet = df2$square_feet,
latitude = df2$latitude,
longitude = df2$longitude,
locs, # these columns now have names = loc_cols
high_price = df2$high_price
)
# Train/Test splitting:
set.seed(42)
train_i <- createDataPartition(model_df$high_price, p = 0.8, list = FALSE)
train2 <- model_df[train_i, ]
test2 <- model_df[-train_i, ]
# Scaling only the true numerics:
num_vars <- c("bedrooms","bathrooms","square_feet","latitude","longitude")
pp <- preProcess(train2[, num_vars], method = c("center","scale"))
train2_num <- predict(pp, train2[, num_vars])
test2_num <- predict(pp, test2[, num_vars])
# Re‑assembling train_final & test_final using exactly loc_cols:
common_locs <- intersect(loc_cols, names(train2))
train_final <- data.frame(
train2_num,
train2[, common_locs, drop = FALSE],
high_price = train2$high_price
)
test_final <- data.frame(
test2_num,
test2[, common_locs, drop = FALSE],
high_price = test2$high_price
)
# Fitting baseline RBF‐SVM:
svm_fit <- svm(
high_price ~ .,
data = train_final,
kernel = "radial",
cost = 1,
gamma = 1 / length(num_vars)
)## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'citynameAcworth' and 'citynameAllendale' and 'citynameAmelia' and
## 'citynameArvilla' and 'citynameAtlantic.Highlands' and 'citynameAvon' and
## 'citynameBarrington' and 'citynameBear' and 'citynameBellwood' and
## 'citynameBerlin' and 'citynameBlanco' and 'citynameBloomfield' and
## 'citynameBountiful' and 'citynameBrockport' and 'citynameBrookline' and
## 'citynameButner' and 'citynameCarbon.Cliff' and 'citynameCastro.Valley' and
## 'citynameCedar.City' and 'citynameChanhassen' and 'citynameCharlottesville' and
## 'citynameChaska' and 'citynameCherryville' and 'citynameChester' and
## 'citynameCliffside.Park' and 'citynameCoralville' and 'citynameCorunna' and
## 'citynameCrossville' and 'citynameCrozet' and 'citynameCrystal' and
## 'citynameDanville' and 'citynameDeltona' and 'citynameDestin' and
## 'citynameDickinson' and 'citynameDunwoody' and 'citynameEl.Segundo' and
## 'citynameElk.River' and 'citynameElwood' and 'citynameEnterprise' and
## 'citynameEssexville' and 'citynameFort.Wayne' and 'citynameFountain.Valley' and
## 'citynameFredericksburg' and 'citynameFreeland' and 'citynameGeneva' and
## 'citynameGiddings' and 'citynameGlenshaw' and 'citynameGoffstown' and
## 'citynameGrand.Prairie' and 'citynameGreer' and 'citynameHayward' and
## 'citynameHermosa.Beach' and 'citynameHighland.Park' and 'citynameHollywood' and
## 'citynameHurricane' and 'citynameIndian.Trail' and 'citynameKaukauna' and
## 'citynameKaysville' and 'citynameKennesaw' and 'citynameKeyser' and
## 'citynameKingston' and 'citynameLake.Forest' and 'citynameLauderhill' and
## 'citynameLeavenworth' and 'citynameLeland' and 'citynameLeon.Valley' and
## 'citynameLincoln.Park' and 'citynameLiverpool' and 'citynameLumberton' and
## 'citynameMahtomedi' and 'citynameManahawkin' and 'citynameMankato' and
## 'citynameManson' and 'citynameMaplewood' and 'citynameMarquette' and
## 'citynameMassillon' and 'citynameMc.Farland' and 'citynameMckinney' and
## 'citynameMcminnville' and 'citynameMedia' and 'citynameMeeker' and
## 'citynameMicro' and 'citynameMonticello' and 'citynameMoorpark' and
## 'citynameMorgan.Hill' and 'citynameMoses.Lake' and 'citynameMound' and
## 'citynameMount.Clemens' and 'citynameNaperville' and 'citynameNevada' and
## 'citynameNew.Canton' and 'citynameNew.Carlisle' and 'citynameNewberg' and
## 'citynameNewburyport' and 'citynameNewport' and 'citynameNorth.Versailles' and
## 'citynameNorthglenn' and 'citynameOakland.Park' and 'citynameOlympia' and
## 'citynameOoltewah' and 'citynameOrtonville' and 'citynameOviedo' and
## 'citynamePaoli' and 'citynamePenns.Grove' and 'citynamePewaukee' and
## 'citynamePlant.City' and 'citynamePonte.Vedra' and 'citynameRadnor' and
## 'citynameRandolph' and 'citynameRed.Bluff' and 'citynameReynoldsburg' and
## 'citynameRogersville' and 'citynameRosenberg' and 'citynameSaint.Marys' and
## 'citynameSanford' and 'citynameSanta.Fe' and 'citynameSavoy' and
## 'citynameScappoose' and 'citynameScarborough' and 'citynameSelma' and
## 'citynameSevern' and 'citynameSheffield.Lake' and 'citynameSilverton' and
## 'citynameSlidell' and 'citynameSoldotna' and 'citynameSouth.Dartmouth' and
## 'citynameSouth.Easton' and 'citynameSouth.Saint.Paul' and
## 'citynameSouth.Yarmouth' and 'citynameSouthfield' and 'citynameSpanaway' and
## 'citynameStanton' and 'citynameStreetsboro' and 'citynameTarpon.Springs' and
## 'citynameTerrell' and 'citynameTiverton' and 'citynameTolleson' and
## 'citynameTulare' and 'citynameVandalia' and 'citynameWaltham' and
## 'citynameWatertown' and 'citynameWest.Deptford' and 'citynameWhite.Plains' and
## 'citynameWilton' and 'citynameWinchester' and 'citynameWooster' and
## 'citynameWorcester' and 'citynameYork' and 'citynameYoungstown' constant.
## Cannot scale data.# Predicting & evaluating:
preds <- predict(svm_fit, test_final[, -ncol(test_final)])
cm <- confusionMatrix(preds, test_final$high_price)
cat("\nConfusion matrix including location dummies:\n")##
## Confusion matrix including location dummies:print(cm)## Confusion Matrix and Statistics
##
## Reference
## Prediction Low High
## Low 1238 181
## High 79 491
##
## Accuracy : 0.8693
## 95% CI : (0.8537, 0.8838)
## No Information Rate : 0.6621
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6966
##
## Mcnemar's Test P-Value : 3.758e-10
##
## Sensitivity : 0.9400
## Specificity : 0.7307
## Pos Pred Value : 0.8724
## Neg Pred Value : 0.8614
## Prevalence : 0.6621
## Detection Rate : 0.6224
## Detection Prevalence : 0.7134
## Balanced Accuracy : 0.8353
##
## 'Positive' Class : Low
## model_df## bedrooms bathrooms square_feet latitude longitude cityname citynameAberdeen
## 3 0 1 107 38.8910 -77.0816 0 0
## 4 0 1 116 47.6160 -122.3275 0 0
## 6 0 1 130 40.7629 -73.9885 0 0
## 9 0 1 138 37.7599 -122.4379 0 0
## 15 0 1 190 37.7599 -122.4379 0 0
## 16 1 1 200 35.0847 -77.0609 0 0
## 17 1 1 200 35.0960 -77.0272 0 0
## 18 1 1 200 30.0871 -95.4685 0 0
## 19 1 1 200 38.1172 -122.2313 0 0
## 20 1 1 200 33.9649 -84.5107 0 0
## 21 1 1 200 35.2016 -80.8124 0 0
## 22 1 1 200 30.4601 -84.2714 0 0
## 24 1 1 200 33.5178 -112.0848 0 0
## 25 1 1 215 27.8492 -82.3155 0 0
## 26 1 1 219 32.8956 -85.4351 0 0
## 27 1 1 219 37.7599 -122.4379 0 0
## 28 1 1 219 39.1634 -76.6165 0 0
## 29 1 1 219 39.5816 -105.0387 0 0
## 30 1 1 219 38.6210 -76.9225 0 0
## 31 1 1 219 27.8492 -82.3155 0 0
## 33 1 1 219 39.6828 -104.8078 0 0
## 34 1 1 219 38.7418 -77.4684 0 0
## 35 1 1 219 33.7416 -84.2614 0 0
## 36 1 1 219 28.0076 -82.4792 0 0
## 37 1 1 219 35.1038 -106.6110 0 0
## 39 0 1 223 47.6160 -122.3275 0 0
## 40 0 1 225 47.6160 -122.3275 0 0
## 41 1 1 225 41.8625 -87.6825 0 0
## 42 0 1 228 47.6160 -122.3275 0 0
## 43 1 1 230 35.2240 -81.1809 0 0
## 44 1 1 230 27.7011 -82.3178 0 0
## 45 1 1 230 35.1211 -89.9517 0 0
## 46 0 1 231 47.6160 -122.3275 0 0
## 47 0 1 232 37.7599 -122.4379 0 0
## 49 1 1 240 48.7871 -122.4437 0 0
## 50 0 1 243 47.6160 -122.3275 0 0
## 51 1 1 245 61.1723 -149.8414 0 0
## 52 1 1 250 37.6019 -122.4863 0 0
## 53 1 1 250 33.9855 -118.4532 0 0
## 54 1 1 250 37.7599 -122.4379 0 0
## 55 1 1 250 34.1450 -118.3898 0 0
## 56 1 1 250 34.2805 -118.8861 0 0
## 57 1 1 250 33.8363 -118.3330 0 0
## 58 1 1 250 38.5587 -121.4320 0 0
## 59 1 1 250 34.2017 -118.4920 0 0
## 60 1 1 250 47.6160 -122.3275 0 0
## 61 1 1 250 34.1881 -118.3905 0 0
## 62 1 1 250 39.7360 -104.9819 0 0
## 63 1 1 250 39.6828 -104.8078 0 0
## 64 1 1 250 37.9353 -121.6935 0 0
## 65 1 1 250 37.9917 -122.5247 0 0
## 66 1 1 250 39.1088 -77.2384 0 0
## 67 1 1 250 42.6484 -71.1615 0 0
## 68 1 1 250 37.7904 -122.2151 0 0
## 69 1 1 250 37.9724 -122.3304 0 0
## 70 0 1 250 41.8625 -87.6825 0 0
## 71 1 1 250 38.7999 -77.1072 0 0
## 72 1 1 250 41.8625 -87.6825 0 0
## 73 1 1 250 34.2009 -118.8636 0 0
## 74 1 1 250 39.7360 -104.9819 0 0
## 75 1 1 250 38.7860 -77.2858 0 0
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## [ reached 'max' / getOption("max.print") -- omitted 9889 rows ]Plot:
library(ggplot2)
library(pROC)
library(e1071)
# Scatter plot of scaled bedrooms vs sq ft, colored by true class:
ggplot(test_final, aes(x = bedrooms, y = square_feet, color = high_price)) +
geom_point(alpha = 0.6, size = 2) +
labs(
title = "Bedrooms vs. Square Feet (scaled)\nTrue Price Class",
x = "Bedrooms (scaled)",
y = "Square Feet (scaled)",
color = "High Price?"
) +
theme_minimal()
# Confusion‐matrix heatmap:
cm_df <- as.data.frame(cm$table)
ggplot(cm_df, aes(x = Prediction, y = Reference, fill = Freq)) +
geom_tile(color = "white") +
geom_text(aes(label = Freq), color = "white", size = 5) +
scale_fill_gradient(low = "steelblue", high = "darkred") +
labs(
title = "Confusion Matrix Heatmap",
x = "Predicted Class",
y = "Actual Class"
) +
theme_minimal()
# 3) ROC Curve:
svm_prob_model <- svm(
high_price ~ .,
data = train_final,
kernel = "radial",
cost = 1,
gamma = 1 / length(num_vars),
probability = TRUE
)## Warning in svm.default(x, y, scale = scale, ..., na.action = na.action):
## Variable(s) 'citynameAcworth' and 'citynameAllendale' and 'citynameAmelia' and
## 'citynameArvilla' and 'citynameAtlantic.Highlands' and 'citynameAvon' and
## 'citynameBarrington' and 'citynameBear' and 'citynameBellwood' and
## 'citynameBerlin' and 'citynameBlanco' and 'citynameBloomfield' and
## 'citynameBountiful' and 'citynameBrockport' and 'citynameBrookline' and
## 'citynameButner' and 'citynameCarbon.Cliff' and 'citynameCastro.Valley' and
## 'citynameCedar.City' and 'citynameChanhassen' and 'citynameCharlottesville' and
## 'citynameChaska' and 'citynameCherryville' and 'citynameChester' and
## 'citynameCliffside.Park' and 'citynameCoralville' and 'citynameCorunna' and
## 'citynameCrossville' and 'citynameCrozet' and 'citynameCrystal' and
## 'citynameDanville' and 'citynameDeltona' and 'citynameDestin' and
## 'citynameDickinson' and 'citynameDunwoody' and 'citynameEl.Segundo' and
## 'citynameElk.River' and 'citynameElwood' and 'citynameEnterprise' and
## 'citynameEssexville' and 'citynameFort.Wayne' and 'citynameFountain.Valley' and
## 'citynameFredericksburg' and 'citynameFreeland' and 'citynameGeneva' and
## 'citynameGiddings' and 'citynameGlenshaw' and 'citynameGoffstown' and
## 'citynameGrand.Prairie' and 'citynameGreer' and 'citynameHayward' and
## 'citynameHermosa.Beach' and 'citynameHighland.Park' and 'citynameHollywood' and
## 'citynameHurricane' and 'citynameIndian.Trail' and 'citynameKaukauna' and
## 'citynameKaysville' and 'citynameKennesaw' and 'citynameKeyser' and
## 'citynameKingston' and 'citynameLake.Forest' and 'citynameLauderhill' and
## 'citynameLeavenworth' and 'citynameLeland' and 'citynameLeon.Valley' and
## 'citynameLincoln.Park' and 'citynameLiverpool' and 'citynameLumberton' and
## 'citynameMahtomedi' and 'citynameManahawkin' and 'citynameMankato' and
## 'citynameManson' and 'citynameMaplewood' and 'citynameMarquette' and
## 'citynameMassillon' and 'citynameMc.Farland' and 'citynameMckinney' and
## 'citynameMcminnville' and 'citynameMedia' and 'citynameMeeker' and
## 'citynameMicro' and 'citynameMonticello' and 'citynameMoorpark' and
## 'citynameMorgan.Hill' and 'citynameMoses.Lake' and 'citynameMound' and
## 'citynameMount.Clemens' and 'citynameNaperville' and 'citynameNevada' and
## 'citynameNew.Canton' and 'citynameNew.Carlisle' and 'citynameNewberg' and
## 'citynameNewburyport' and 'citynameNewport' and 'citynameNorth.Versailles' and
## 'citynameNorthglenn' and 'citynameOakland.Park' and 'citynameOlympia' and
## 'citynameOoltewah' and 'citynameOrtonville' and 'citynameOviedo' and
## 'citynamePaoli' and 'citynamePenns.Grove' and 'citynamePewaukee' and
## 'citynamePlant.City' and 'citynamePonte.Vedra' and 'citynameRadnor' and
## 'citynameRandolph' and 'citynameRed.Bluff' and 'citynameReynoldsburg' and
## 'citynameRogersville' and 'citynameRosenberg' and 'citynameSaint.Marys' and
## 'citynameSanford' and 'citynameSanta.Fe' and 'citynameSavoy' and
## 'citynameScappoose' and 'citynameScarborough' and 'citynameSelma' and
## 'citynameSevern' and 'citynameSheffield.Lake' and 'citynameSilverton' and
## 'citynameSlidell' and 'citynameSoldotna' and 'citynameSouth.Dartmouth' and
## 'citynameSouth.Easton' and 'citynameSouth.Saint.Paul' and
## 'citynameSouth.Yarmouth' and 'citynameSouthfield' and 'citynameSpanaway' and
## 'citynameStanton' and 'citynameStreetsboro' and 'citynameTarpon.Springs' and
## 'citynameTerrell' and 'citynameTiverton' and 'citynameTolleson' and
## 'citynameTulare' and 'citynameVandalia' and 'citynameWaltham' and
## 'citynameWatertown' and 'citynameWest.Deptford' and 'citynameWhite.Plains' and
## 'citynameWilton' and 'citynameWinchester' and 'citynameWooster' and
## 'citynameWorcester' and 'citynameYork' and 'citynameYoungstown' constant.
## Cannot scale data.# predict class + probabilities on test set:
pred_obj <- predict(
svm_prob_model,
test_final[, -ncol(test_final)],
probability = TRUE
)
probs_low <- attr(pred_obj, "probabilities")[, "Low"]
# computing & plotting ROC:
roc_obj <- roc(
response = test_final$high_price,
predictor = probs_low,
levels = c("Low", "High")
)## Setting direction: controls > casesggroc(roc_obj, size = 1.2) +
geom_abline(linetype = "dashed", color = "grey50") +
labs(
title = sprintf("ROC Curve (AUC = %.2f)", auc(roc_obj)),
x = "False Positive Rate",
y = "True Positive Rate"
) +
theme_minimal()
What Improved?
| Metric | Baseline (no location) | With location |
|---|---|---|
| Accuracy | 0.7167 | 0.8693 |
| Sensitivity (Low price) | 0.9507 | 0.94 |
| Specificity (High price) | 0.2585 | 0.7307 |
| Balanced accuracy | 0.6046 | 0.8353 |
| Kappa | 0.2484 | 0.6966 |
| AUC | 0.67 | 0.92 |