House Price Predition
Yonatan Septian Partadarsana
7/3/2021
#Description
This report provides house price prediction using regression algorithm. The data set using in this report for modelling is House Data in Australia. The data set is hosted in Kaggle and can be downloaded here : https://www.kaggle.com/shree1992/housedata
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 and see its structure
# read data
house_df <- read.csv("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 has 4600 observation and 18 variables, the target variable is price and the remaining variables are features
Extract statistical 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 of price is statistically far away from median and third quantile. This could be an outliers.
2. Exploratory Data Analysis
2.1 Univariate 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 Analysis
For plotting purposes, some numerical and char variables are transformed to factor
##casting 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)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))## Warning: Removed 47 rows containing non-finite values (stat_boxplot).
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 building, sport center, etc
2.3 Multivariate 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 variable with high correlation in order are sqft_living, sqft_above, bathrooms, and bedroom
Insight from EDA
- Incorrect values on price (price == 0 )
- Extreme 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, bathrooms.
- In general, the higher number of bedrooms, the higher the price. Exception for bedroom == 0.
- There are locations variables. However, they are non-numeric.