House Price Prediction
Willy
3/7/2021
Description
This report privides house price prediction regression algorithm. The dataset using in this report for modeling is House Data in Australia.The dataset is hosted in Kaggel and can be downloaded here.
The report is structured as follows:
1. Data Extraction
2. Exploratory Data Analysis
3. Data Preparation
4. Modeling
5. Evaluation
6. Recommendation
1. Data Extraction
Import necessary libraries.
rm(list = ls())
library(ggplot2)Read house dataset dan see its structure.
# Read data
house_df <- read.csv("C:/Users/lenovo/Documents/MyProjects/data/house.csv")
# Structure of dataframe
str(house_df)## 'data.frame': 4600 obs. of 18 variables:
## $ date : chr "2014-05-02 00:00:00" "2014-05-02 00:00:00" "2014-05-02 00:00:00" "2014-05-02 00:00:00" ...
## $ price : num 313000 2384000 342000 420000 550000 ...
## $ bedrooms : num 3 5 3 3 4 2 2 4 3 4 ...
## $ bathrooms : num 1.5 2.5 2 2.25 2.5 1 2 2.5 2.5 2 ...
## $ sqft_living : int 1340 3650 1930 2000 1940 880 1350 2710 2430 1520 ...
## $ sqft_lot : int 7912 9050 11947 8030 10500 6380 2560 35868 88426 6200 ...
## $ floors : num 1.5 2 1 1 1 1 1 2 1 1.5 ...
## $ waterfront : int 0 0 0 0 0 0 0 0 0 0 ...
## $ view : int 0 4 0 0 0 0 0 0 0 0 ...
## $ condition : int 3 5 4 4 4 3 3 3 4 3 ...
## $ sqft_above : int 1340 3370 1930 1000 1140 880 1350 2710 1570 1520 ...
## $ sqft_basement: int 0 280 0 1000 800 0 0 0 860 0 ...
## $ yr_built : int 1955 1921 1966 1963 1976 1938 1976 1989 1985 1945 ...
## $ yr_renovated : int 2005 0 0 0 1992 1994 0 0 0 2010 ...
## $ street : chr "18810 Densmore Ave N" "709 W Blaine St" "26206-26214 143rd Ave SE" "857 170th Pl NE" ...
## $ city : chr "Shoreline" "Seattle" "Kent" "Bellevue" ...
## $ statezip : chr "WA 98133" "WA 98119" "WA 98042" "WA 98008" ...
## $ country : chr "USA" "USA" "USA" "USA" ...The dataset 4600 observations and 18 variables. The target variable is price and the remaining variables are features.
Extract statictical summary of each variables.
# Data dimension
d <- dim(house_df)
m <- d[1] # m: number of rows
n <- d[2] # n: number of columns
# Statistical summary
summary(house_df)## date price bedrooms bathrooms
## Length:4600 Min. : 0 Min. :0.000 Min. :0.000
## Class :character 1st Qu.: 322875 1st Qu.:3.000 1st Qu.:1.750
## Mode :character Median : 460943 Median :3.000 Median :2.250
## Mean : 551963 Mean :3.401 Mean :2.161
## 3rd Qu.: 654962 3rd Qu.:4.000 3rd Qu.:2.500
## Max. :26590000 Max. :9.000 Max. :8.000
## sqft_living sqft_lot floors waterfront
## Min. : 370 Min. : 638 Min. :1.000 Min. :0.000000
## 1st Qu.: 1460 1st Qu.: 5001 1st Qu.:1.000 1st Qu.:0.000000
## Median : 1980 Median : 7683 Median :1.500 Median :0.000000
## Mean : 2139 Mean : 14852 Mean :1.512 Mean :0.007174
## 3rd Qu.: 2620 3rd Qu.: 11001 3rd Qu.:2.000 3rd Qu.:0.000000
## Max. :13540 Max. :1074218 Max. :3.500 Max. :1.000000
## view condition sqft_above sqft_basement
## Min. :0.0000 Min. :1.000 Min. : 370 Min. : 0.0
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:1190 1st Qu.: 0.0
## Median :0.0000 Median :3.000 Median :1590 Median : 0.0
## Mean :0.2407 Mean :3.452 Mean :1827 Mean : 312.1
## 3rd Qu.:0.0000 3rd Qu.:4.000 3rd Qu.:2300 3rd Qu.: 610.0
## Max. :4.0000 Max. :5.000 Max. :9410 Max. :4820.0
## yr_built yr_renovated street city
## Min. :1900 Min. : 0.0 Length:4600 Length:4600
## 1st Qu.:1951 1st Qu.: 0.0 Class :character Class :character
## Median :1976 Median : 0.0 Mode :character Mode :character
## Mean :1971 Mean : 808.6
## 3rd Qu.:1997 3rd Qu.:1999.0
## Max. :2014 Max. :2014.0
## statezip country
## Length:4600 Length:4600
## Class :character Class :character
## Mode :character Mode :character
##
##
## We can see minimun, median, mean and maximum values of each numeric variables. It is interesting that the minimum values of price is zero. This could be an incorrect data.
We can also notice that the maximum values statistically far away from median third quantile. This could be an outliers.
2. Exploratory Data Analysis
2.1. Univariate Data Analysis
Plot distribution of price using boxplot.
## Boxplot
ggplot(data = house_df, aes(y = price)) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 2000000))
Based on boxplot above, we can see that there are outliers in price.
2.2. Bivariate Data Analysis
For plotting purposes, some numerical and char variables are transformed to factor.
## Casting bedrooms from numeric to factor
house_df$bedrooms2 <- factor(house_df$bedrooms)
house_df$city2 <- factor(house_df$city)
house_df$statezip2 <- factor(house_df$statezip)
house_df$street2 <- factor(house_df$street)
house_df$country2 <- factor(house_df$country)
ggplot(data = house_df, aes(x = bedrooms2,
y = price)) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 2000000))
Plot house price based on number of bedrooms.
ggplot(data = house_df, aes(x = bedrooms2,
y = price)) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 2000000))
Based on price by number of bedrooms, we can see the following: 1. In general, the higher number of bedrooms the higher the price. 2. It is interesting that house with number of bedrooms == 0, the house price is significantly high. It could be a special house such as meeting hall, religious builing, sport center, etc.
2.3. Multivariate Data Analysis
Compute correlation coefficient (R) among all numerical variables. Visualize Correlation Coefficient in a diagram.
## Compute Correlation Coefficient
house_df_num <- house_df[ , 2:12]
r <- cor(house_df_num)
library(corrgram)
corrgram(house_df_num, order = TRUE,
upper.panel = panel.pie)
Several variables are highly correlated. For example sqft_living and sqft_above.
For target variable (price), the variables with high correlation in order are sqft_living, sqft_above, bathroom and bedroom.
Insight from EDA
- Incorrect values on price (price == 0).
- Extremen outliers on price variables.
- There are co-linear variables.
- Based on Pearson’s correlation coefficient (r), the variables with highest correlation with target (price) are sqft_living, sqft_above, bathroom and bedroom.
- In general, the highter number of bedrooms, the higher the price. Exception for bedroom == 0.
- There are locations variables. However, they are non-numeric.
3. Data Preparation
3.1. Data Cleaning
Remove rows with incorrect values in price.
# Get indices
idx <- which(house_df_num$price %in% c(0))
# Remove those rows
house_df_num <- house_df_num[-idx, ]
summary(house_df_num$price)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7800 326264 465000 557906 657500 26590000The minimum value on price is now not zero but 7800.
Remove rows with outliers in price.
# Get outliers values
out_price <- boxplot.stats(house_df_num$price)$out
# Get indices
out_idx <- which(house_df_num$price %in% c(out_price))
# Remove those rows
house_df_num <- house_df_num[ -out_idx, ]
summary(house_df_num$price)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7800 320000 450000 487457 615000 1150000The maximum value on price is now 1150000.
nrow(house_df_num)## [1] 4311Number of observation is now 4311. It means the data cleaning process removed 298 rows.
3.2. Feature Extraction
One Hot Encoding for Location Features (using statezip column)
#### Remove rows as in house_df_num
house_df <- house_df[ rownames(house_df_num), ]
#### 1. Create dataframe for statezip
statezip <- house_df$statezip
statezip_df <- data.frame(statezip)
colnames(statezip_df) <- c("loc.")
#### 2. OHE on statezip dataframe
library(caret)
df1 <- dummyVars("~.", data = statezip_df)
df2 <- data.frame(predict(df1, newdata = statezip_df))
#### 3. Combine to house_df_num dataframe
#### 11 + 77 = 88 columns
house_df_num <- cbind(house_df_num, df2)
dim(house_df_num)## [1] 4311 88Number of features (columns) is now 88. It means added 77 new features for location information using OHE in statezip.
3.3. Training and Testing Division
Ramdomly divided the dataset into training dan testing with ration = 70:30. For reproducible result, it is necessary to set seed.
# Data dimension
d <- dim(house_df_num)
m <- d[1] # m: number of rows
n <- d[2] # n: number of columns
## Ratio train:test = 70:30
## Randomly divided
set.seed(2021)
train_idx <- sample(m, 0.7 * m)
train_idx[1:3]## [1] 2214 442 743train_data <- house_df_num[ train_idx , ]
test_data <- house_df_num[ -train_idx , ]
dim(train_data)## [1] 3017 88dim(test_data)## [1] 1294 88The train data has 3017 rows and test data has 1294 rows.
4. Modeling
Create regression model.
mymodel <- lm(formula = price ~ . +
I(sqft_living^2) +
sqft_living:bedrooms,
data = train_data)
summary(mymodel)##
## Call:
## lm(formula = price ~ . + I(sqft_living^2) + sqft_living:bedrooms,
## data = train_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -872020 -52263 -1836 48176 520918
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.000e+05 1.037e+05 -0.964 0.335018
## bedrooms 1.127e+04 7.435e+03 1.516 0.129518
## bathrooms 2.191e+04 4.317e+03 5.075 4.11e-07 ***
## sqft_living 1.702e+02 1.137e+01 14.960 < 2e-16 ***
## sqft_lot 2.743e-01 5.575e-02 4.920 9.13e-07 ***
## floors -3.338e+04 5.251e+03 -6.356 2.39e-10 ***
## waterfront 1.354e+05 3.682e+04 3.678 0.000239 ***
## view 3.672e+04 3.262e+03 11.257 < 2e-16 ***
## condition 2.308e+04 3.043e+03 7.585 4.44e-14 ***
## sqft_above 8.334e+01 6.353e+00 13.117 < 2e-16 ***
## sqft_basement NA NA NA NA
## loc.WA.98001 -1.046e+05 1.026e+05 -1.020 0.307959
## loc.WA.98002 -1.127e+05 1.037e+05 -1.087 0.277266
## loc.WA.98003 -9.356e+04 1.030e+05 -0.908 0.363934
## loc.WA.98004 4.455e+05 1.036e+05 4.299 1.77e-05 ***
## loc.WA.98005 2.412e+05 1.042e+05 2.315 0.020657 *
## loc.WA.98006 1.915e+05 1.023e+05 1.872 0.061294 .
## loc.WA.98007 2.161e+05 1.036e+05 2.086 0.037080 *
## loc.WA.98008 1.237e+05 1.030e+05 1.201 0.229827
## loc.WA.98010 5.043e+03 1.112e+05 0.045 0.963840
## loc.WA.98011 4.492e+04 1.037e+05 0.433 0.664739
## loc.WA.98014 5.046e+03 1.052e+05 0.048 0.961734
## loc.WA.98019 -8.018e+03 1.033e+05 -0.078 0.938134
## loc.WA.98022 -1.214e+05 1.040e+05 -1.167 0.243275
## loc.WA.98023 -1.179e+05 1.022e+05 -1.153 0.248916
## loc.WA.98024 6.126e+04 1.097e+05 0.559 0.576464
## loc.WA.98027 9.721e+04 1.025e+05 0.949 0.342795
## loc.WA.98028 5.189e+04 1.026e+05 0.506 0.612964
## loc.WA.98029 1.634e+05 1.024e+05 1.595 0.110740
## loc.WA.98030 -9.482e+04 1.034e+05 -0.917 0.359028
## loc.WA.98031 -9.087e+04 1.027e+05 -0.885 0.376375
## loc.WA.98032 -1.182e+05 1.051e+05 -1.125 0.260848
## loc.WA.98033 2.388e+05 1.024e+05 2.331 0.019809 *
## loc.WA.98034 1.069e+05 1.024e+05 1.044 0.296346
## loc.WA.98038 -7.578e+04 1.023e+05 -0.741 0.458861
## loc.WA.98039 -3.825e+05 1.436e+05 -2.663 0.007786 **
## loc.WA.98040 3.101e+05 1.030e+05 3.009 0.002641 **
## loc.WA.98042 -9.962e+04 1.022e+05 -0.975 0.329789
## loc.WA.98045 -6.168e+03 1.029e+05 -0.060 0.952219
## loc.WA.98047 -9.415e+04 1.112e+05 -0.847 0.397284
## loc.WA.98050 8.550e+03 1.441e+05 0.059 0.952686
## loc.WA.98051 -2.062e+04 1.116e+05 -0.185 0.853467
## loc.WA.98052 1.918e+05 1.020e+05 1.879 0.060306 .
## loc.WA.98053 1.514e+05 1.023e+05 1.480 0.138858
## loc.WA.98055 -5.428e+04 1.043e+05 -0.520 0.602784
## loc.WA.98056 2.489e+04 1.024e+05 0.243 0.808066
## loc.WA.98057 -7.989e+04 1.060e+05 -0.753 0.451264
## loc.WA.98058 -5.394e+04 1.023e+05 -0.527 0.597992
## loc.WA.98059 5.430e+03 1.022e+05 0.053 0.957646
## loc.WA.98065 4.819e+04 1.027e+05 0.469 0.639102
## loc.WA.98068 1.495e+05 1.437e+05 1.041 0.298156
## loc.WA.98070 7.905e+03 1.044e+05 0.076 0.939640
## loc.WA.98072 1.012e+05 1.027e+05 0.985 0.324596
## loc.WA.98074 1.267e+05 1.023e+05 1.239 0.215417
## loc.WA.98075 1.705e+05 1.025e+05 1.663 0.096330 .
## loc.WA.98077 1.219e+05 1.032e+05 1.181 0.237887
## loc.WA.98092 -1.075e+05 1.024e+05 -1.050 0.293706
## loc.WA.98102 3.308e+05 1.050e+05 3.150 0.001648 **
## loc.WA.98103 2.502e+05 1.021e+05 2.450 0.014339 *
## loc.WA.98105 3.583e+05 1.039e+05 3.447 0.000575 ***
## loc.WA.98106 3.489e+04 1.027e+05 0.340 0.734118
## loc.WA.98107 2.486e+05 1.027e+05 2.421 0.015551 *
## loc.WA.98108 2.727e+04 1.032e+05 0.264 0.791644
## loc.WA.98109 4.323e+05 1.066e+05 4.055 5.13e-05 ***
## loc.WA.98112 3.731e+05 1.031e+05 3.619 0.000301 ***
## loc.WA.98115 2.409e+05 1.021e+05 2.360 0.018358 *
## loc.WA.98116 2.219e+05 1.027e+05 2.160 0.030848 *
## loc.WA.98117 2.241e+05 1.021e+05 2.195 0.028250 *
## loc.WA.98118 8.617e+04 1.025e+05 0.841 0.400645
## loc.WA.98119 3.310e+05 1.034e+05 3.200 0.001389 **
## loc.WA.98122 2.650e+05 1.028e+05 2.578 0.009984 **
## loc.WA.98125 8.670e+04 1.024e+05 0.847 0.397056
## loc.WA.98126 1.142e+05 1.024e+05 1.115 0.264930
## loc.WA.98133 6.011e+04 1.023e+05 0.588 0.556720
## loc.WA.98136 1.694e+05 1.029e+05 1.646 0.099898 .
## loc.WA.98144 1.748e+05 1.025e+05 1.706 0.088124 .
## loc.WA.98146 4.353e+04 1.029e+05 0.423 0.672173
## loc.WA.98148 -3.307e+04 1.085e+05 -0.305 0.760587
## loc.WA.98155 3.714e+04 1.024e+05 0.363 0.716785
## loc.WA.98166 9.244e+02 1.027e+05 0.009 0.992821
## loc.WA.98168 -5.496e+04 1.026e+05 -0.535 0.592420
## loc.WA.98177 1.633e+05 1.029e+05 1.587 0.112601
## loc.WA.98178 -5.612e+04 1.032e+05 -0.544 0.586724
## loc.WA.98188 -9.531e+04 1.049e+05 -0.909 0.363523
## loc.WA.98198 -9.769e+04 1.028e+05 -0.950 0.341938
## loc.WA.98199 2.732e+05 1.030e+05 2.653 0.008022 **
## loc.WA.98288 4.083e+04 1.438e+05 0.284 0.776479
## loc.WA.98354 NA NA NA NA
## I(sqft_living^2) -8.753e-03 2.508e-03 -3.490 0.000490 ***
## bedrooms:sqft_living -1.086e+01 3.057e+00 -3.553 0.000387 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 101500 on 2929 degrees of freedom
## Multiple R-squared: 0.7877, Adjusted R-squared: 0.7814
## F-statistic: 124.9 on 87 and 2929 DF, p-value: < 2.2e-16We can see that some statezip (locations) has significant influence on price. This means location is important to predict house price.
5. Evaluation
Predict Price
## Actual price
actual <- test_data$price
## Predicted price
pred.mymodel <- predict(mymodel, test_data)Plot Actual vs. Predicted Price
price_df <- data.frame(actual, pred.mymodel)
ggplot(data = price_df, aes(x = actual, y = pred.mymodel)) +
geom_point() +
scale_x_continuous(limits = c(0,1500000)) +
scale_y_continuous(limits = c(0,1500000))
Mostly the points are located in the diagonal. It means the predicted valeus are closed to the actual values. However, there are still some points that are relatively far for diagonal.
Measure RMSE and R
performance <- function(prediction, actual, method){
e <- prediction - actual # error
se <- e^2 # squared error
sse <- sum(se) # sum of squared error
mse <- mean(se) # mean squared error
rmse <- sqrt(mse) # root mean squared error
r <- cor(prediction, actual) # correlation coefficient
result <- paste("*** Method: ", method,
"\nRMSE = ", round(rmse,3),
"\nR = ", round(r,3),
"\n")
cat(result)
}
performance(pred.mymodel, actual, "My Model")## *** Method: My Model
## RMSE = 97428.002
## R = 0.889The correlation coefficient is very high. The error in terms of RMSE is significantly reduced to lower 100000. This means we made huge improvement in modeling.
6. Recommendation
The prediction model is good enough to predict the price of mainstream houses.
It is ready for deployment. However, there are still room of improvement in the future. For example, house with bedroom == 0 and house with significantly expensive price may be designed a dedicated model.
Location is an important features generated from one of the column in the dataset (statezip). There could be other important feature that can be generated and make significant influence.