Kaggle Playground - Regression Problem
This is a simplified version of the program used for the Kaggle competition “House Prices”
Data science problem
Data descriptions
Ask a home buyer to describe their dream house, and they probably won’t begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition’s dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
The potential for creative feature engineering provides a rich opportunity for fun and learning. This dataset lends itself to advanced regression techniques like random forests and gradient boosting with the popular XGBoost library. We encourage Kagglers to create benchmark code and tutorials on Kernels for community learning. Top kernels will be awarded swag prizes at the competition close.
Acknowledgments
The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It’s an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset.
File descriptions
train.csv - the training set
test.csv - the test set
data_description.txt - full description of each column, originally prepared by Dean De Cock but lightly edited to match the column names used here
sample_submission.csv - a benchmark submission from a linear regression on year and month of sale, lot square footage, and number of bedrooms
Variables
SalePrice - the property’s sale price in dollars. This is the target variable that you’re trying to predict.
MSSubClass: The building class
MSZoning: The general zoning classification
LotFrontage: Linear feet of street connected to property
LotArea: Lot size in square feet
Street: Type of road access
Alley: Type of alley access
LotShape: General shape of property
LandContour: Flatness of the property Utilities: Type of utilities available
LotConfig: Lot configuration
LandSlope: Slope of property
Neighborhood: Physical locations within Ames city limits
Condition1: Proximity to main road or railroad
Condition2: Proximity to main road or railroad (if a second is present)
BldgType: Type of dwelling
HouseStyle: Style of dwelling
OverallQual: Overall material and finish quality
OverallCond: Overall condition rating
YearBuilt: Original construction date
YearRemodAdd: Remodel date
RoofStyle: Type of roof
RoofMatl: Roof material
Exterior1st: Exterior covering on house
Exterior2nd: Exterior covering on house (if more than one material)
MasVnrType: Masonry veneer type
MasVnrArea: Masonry veneer area in square feet
ExterQual: Exterior material quality
ExterCond: Present condition of the material on the exterior
Foundation: Type of foundation
BsmtQual: Height of the basement
BsmtCond: General condition of the basement
BsmtExposure: Walkout or garden level basement walls
BsmtFinType1: Quality of basement finished area
BsmtFinSF1: Type 1 finished square feet
BsmtFinType2: Quality of second finishedarea (if present)
BsmtFinSF2: Type 2 finished square feet
BsmtUnfSF: Unfinished square feet of basement area
TotalBsmtSF: Total square feet of basement area
Heating: Type of heating
HeatingQC: Heating quality and condition
CentralAir: Central air conditioning
Electrical: Electrical system
1stFlrSF: First Floor square feet
2ndFlrSF: Second floor square feet
LowQualFinSF: Low quality finished square feet (all floors)
GrLivArea: Above grade (ground) living area square feet
BsmtFullBath: Basement full bathrooms
BsmtHalfBath: Basement half bathrooms
FullBath: Full bathrooms above grade
HalfBath: Half baths above grade
Bedroom: Number of bedrooms above basement level
Kitchen: Number of kitchens
KitchenQual: Kitchen quality
TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)
Functional: Home functionality rating
Fireplaces: Number of fireplaces
FireplaceQu: Fireplace quality
GarageType: Garage location
GarageYrBlt: Year garage was built
GarageFinish: Interior finish of the garage
GarageCars: Size of garage in car capacity
GarageArea: Size of garage in square feet
GarageQual: Garage quality
GarageCond: Garage condition
PavedDrive: Paved driveway
WoodDeckSF: Wood deck area in square feet
OpenPorchSF: Open porch area in square feet
EnclosedPorch: Enclosed porch area in square feet
3SsnPorch: Three season porch area in square feet
ScreenPorch: Screen porch area in square feet
PoolArea: Pool area in square feet
PoolQC: Pool quality
Fence: Fence quality
MiscFeature: Miscellaneous feature not covered in other categories
MiscVal: $Value of miscellaneous feature
MoSold: Month Sold
YrSold: Year Sold
SaleType: Type of sale
SaleCondition: Condition of sale
About Kaggle
In 2010, Kaggle was founded as a platform for predictive modelling and analytics competitions on which companies and researchers post their data and statisticians and data miners from all over the world compete to produce the best models.
This crowdsourcing approach relies on the fact that there are countless strategies that can be applied to any predictive modelling task and it is impossible to know at the outset which technique or analyst will be most effective. Kaggle also hosts recruiting competitions in which data scientists compete for a chance to interview at leading data science companies like Facebook, Winton Capital, and Walmart.
Libraries
# For data manipulation and tidying
library(MASS)
library(tidyr)
library(plyr)
library(dplyr)
library(broom)
library(data.table)
library(testthat)
library(gridExtra)
# For data visualizations
library(ggplot2)
library(plotly)
library(DT)
library(corrplot)
library(GGally)
library(Boruta)
library(pROC)
library(VIM)
library(mice)
# For modeling and predictions
library(mlbench)
library(caret)
library(glmnet)
library(ranger)
library(clValid)
library(e1071)
library(xgboost)Data Exploration
Data
train <- read.csv("train.csv", header = TRUE, sep = ",", stringsAsFactors = FALSE)
data.test <- read.csv("test.csv", header = TRUE, sep = ",", stringsAsFactors = FALSE)
datatable(head(train, n=20),options = list(scrollX = TRUE))Summary
summary(train)## Id MSSubClass MSZoning LotFrontage
## Min. : 1.0 Min. : 20.0 Length:1460 Min. : 21.00
## 1st Qu.: 365.8 1st Qu.: 20.0 Class :character 1st Qu.: 59.00
## Median : 730.5 Median : 50.0 Mode :character Median : 69.00
## Mean : 730.5 Mean : 56.9 Mean : 70.05
## 3rd Qu.:1095.2 3rd Qu.: 70.0 3rd Qu.: 80.00
## Max. :1460.0 Max. :190.0 Max. :313.00
## NA's :259
## LotArea Street Alley LotShape
## Min. : 1300 Length:1460 Length:1460 Length:1460
## 1st Qu.: 7554 Class :character Class :character Class :character
## Median : 9478 Mode :character Mode :character Mode :character
## Mean : 10517
## 3rd Qu.: 11602
## Max. :215245
##
## LandContour Utilities LotConfig
## Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## LandSlope Neighborhood Condition1
## Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Condition2 BldgType HouseStyle OverallQual
## Length:1460 Length:1460 Length:1460 Min. : 1.000
## Class :character Class :character Class :character 1st Qu.: 5.000
## Mode :character Mode :character Mode :character Median : 6.000
## Mean : 6.099
## 3rd Qu.: 7.000
## Max. :10.000
##
## OverallCond YearBuilt YearRemodAdd RoofStyle
## Min. :1.000 Min. :1872 Min. :1950 Length:1460
## 1st Qu.:5.000 1st Qu.:1954 1st Qu.:1967 Class :character
## Median :5.000 Median :1973 Median :1994 Mode :character
## Mean :5.575 Mean :1971 Mean :1985
## 3rd Qu.:6.000 3rd Qu.:2000 3rd Qu.:2004
## Max. :9.000 Max. :2010 Max. :2010
##
## RoofMatl Exterior1st Exterior2nd
## Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## MasVnrType MasVnrArea ExterQual ExterCond
## Length:1460 Min. : 0.0 Length:1460 Length:1460
## Class :character 1st Qu.: 0.0 Class :character Class :character
## Mode :character Median : 0.0 Mode :character Mode :character
## Mean : 103.7
## 3rd Qu.: 166.0
## Max. :1600.0
## NA's :8
## Foundation BsmtQual BsmtCond
## Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2
## Length:1460 Length:1460 Min. : 0.0 Length:1460
## Class :character Class :character 1st Qu.: 0.0 Class :character
## Mode :character Mode :character Median : 383.5 Mode :character
## Mean : 443.6
## 3rd Qu.: 712.2
## Max. :5644.0
##
## BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating
## Min. : 0.00 Min. : 0.0 Min. : 0.0 Length:1460
## 1st Qu.: 0.00 1st Qu.: 223.0 1st Qu.: 795.8 Class :character
## Median : 0.00 Median : 477.5 Median : 991.5 Mode :character
## Mean : 46.55 Mean : 567.2 Mean :1057.4
## 3rd Qu.: 0.00 3rd Qu.: 808.0 3rd Qu.:1298.2
## Max. :1474.00 Max. :2336.0 Max. :6110.0
##
## HeatingQC CentralAir Electrical X1stFlrSF
## Length:1460 Length:1460 Length:1460 Min. : 334
## Class :character Class :character Class :character 1st Qu.: 882
## Mode :character Mode :character Mode :character Median :1087
## Mean :1163
## 3rd Qu.:1391
## Max. :4692
##
## X2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath
## Min. : 0 Min. : 0.000 Min. : 334 Min. :0.0000
## 1st Qu.: 0 1st Qu.: 0.000 1st Qu.:1130 1st Qu.:0.0000
## Median : 0 Median : 0.000 Median :1464 Median :0.0000
## Mean : 347 Mean : 5.845 Mean :1515 Mean :0.4253
## 3rd Qu.: 728 3rd Qu.: 0.000 3rd Qu.:1777 3rd Qu.:1.0000
## Max. :2065 Max. :572.000 Max. :5642 Max. :3.0000
##
## BsmtHalfBath FullBath HalfBath BedroomAbvGr
## Min. :0.00000 Min. :0.000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.00000 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:2.000
## Median :0.00000 Median :2.000 Median :0.0000 Median :3.000
## Mean :0.05753 Mean :1.565 Mean :0.3829 Mean :2.866
## 3rd Qu.:0.00000 3rd Qu.:2.000 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :2.00000 Max. :3.000 Max. :2.0000 Max. :8.000
##
## KitchenAbvGr KitchenQual TotRmsAbvGrd Functional
## Min. :0.000 Length:1460 Min. : 2.000 Length:1460
## 1st Qu.:1.000 Class :character 1st Qu.: 5.000 Class :character
## Median :1.000 Mode :character Median : 6.000 Mode :character
## Mean :1.047 Mean : 6.518
## 3rd Qu.:1.000 3rd Qu.: 7.000
## Max. :3.000 Max. :14.000
##
## Fireplaces FireplaceQu GarageType GarageYrBlt
## Min. :0.000 Length:1460 Length:1460 Min. :1900
## 1st Qu.:0.000 Class :character Class :character 1st Qu.:1961
## Median :1.000 Mode :character Mode :character Median :1980
## Mean :0.613 Mean :1979
## 3rd Qu.:1.000 3rd Qu.:2002
## Max. :3.000 Max. :2010
## NA's :81
## GarageFinish GarageCars GarageArea GarageQual
## Length:1460 Min. :0.000 Min. : 0.0 Length:1460
## Class :character 1st Qu.:1.000 1st Qu.: 334.5 Class :character
## Mode :character Median :2.000 Median : 480.0 Mode :character
## Mean :1.767 Mean : 473.0
## 3rd Qu.:2.000 3rd Qu.: 576.0
## Max. :4.000 Max. :1418.0
##
## GarageCond PavedDrive WoodDeckSF OpenPorchSF
## Length:1460 Length:1460 Min. : 0.00 Min. : 0.00
## Class :character Class :character 1st Qu.: 0.00 1st Qu.: 0.00
## Mode :character Mode :character Median : 0.00 Median : 25.00
## Mean : 94.24 Mean : 46.66
## 3rd Qu.:168.00 3rd Qu.: 68.00
## Max. :857.00 Max. :547.00
##
## EnclosedPorch X3SsnPorch ScreenPorch PoolArea
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.000
## Mean : 21.95 Mean : 3.41 Mean : 15.06 Mean : 2.759
## 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.000
## Max. :552.00 Max. :508.00 Max. :480.00 Max. :738.000
##
## PoolQC Fence MiscFeature
## Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## MiscVal MoSold YrSold SaleType
## Min. : 0.00 Min. : 1.000 Min. :2006 Length:1460
## 1st Qu.: 0.00 1st Qu.: 5.000 1st Qu.:2007 Class :character
## Median : 0.00 Median : 6.000 Median :2008 Mode :character
## Mean : 43.49 Mean : 6.322 Mean :2008
## 3rd Qu.: 0.00 3rd Qu.: 8.000 3rd Qu.:2009
## Max. :15500.00 Max. :12.000 Max. :2010
##
## SaleCondition SalePrice
## Length:1460 Min. : 34900
## Class :character 1st Qu.:129975
## Mode :character Median :163000
## Mean :180921
## 3rd Qu.:214000
## Max. :755000
## Visualisation
Thanks to laurae2 for this code for plotting all data using tabplots. The objective is to find out some of the good features visually. As Laurae2 say: you can think of it as the vertical as the “sort by SalePrice”:
invisible(library(tabplot))
invisible(library(data.table))
columns <- c("numeric",
rep("character", 2),
rep("numeric", 2),
rep("character", 12),
rep("numeric", 4),
rep("character", 5),
"numeric",
rep("character", 7),
"numeric",
"character",
rep("numeric", 3),
rep("character", 4),
rep("numeric", 10),
"character",
"numeric",
"character",
"numeric",
rep("character", 2),
"numeric",
"character",
rep("numeric", 2),
rep("character", 3),
rep("numeric", 6),
rep("character", 3),
rep("numeric", 3),
rep("character", 2),
rep("numeric"))
train$SalePrice <- log(train$SalePrice) # To respect lrmse
train_visu <- as.data.frame(train)
for (i in 1:80) {
if (typeof(train_visu[, i]) == "character") {
train_visu[is.na(train_visu[, i]), i] <- ""
train_visu[, i] <- as.factor(train_visu[, i])
}
}
for (i in 1:16) {
plot(tableplot(train_visu, select = c(((i - 1) * 5 + 1):(i * 5), 81), sortCol = 6, nBins = 73, plot = FALSE), fontsize = 12, title = paste("log(SalePrice) vs ", paste(colnames(train_visu)[((i - 1) * 5 + 1):(i * 5)], collapse = "+"), sep = ""), showTitle = TRUE, fontsize.title = 12)
}
Boruta
Thanks to Jim Thompson (JMT5802) for this Boruta Feature Importance Analysis. This report determines what features may be relevant to predicting house sale price. This analysis is based on the Boruta package. The code can be found here.
ID.VAR <- "Id"
TARGET.VAR <- "SalePrice"
# Data Preparation for Bourta Analysis
# retrive data for analysis
sample.df <- read.csv(file.path(ROOT.DIR,"input/train.csv"),stringsAsFactors = FALSE)
# extract only candidate feture names
candidate.features <- setdiff(names(sample.df),c(ID.VAR,TARGET.VAR))
data.type <- sapply(candidate.features,function(x){class(sample.df[[x]])})
# deterimine data types
explanatory.attributes <- setdiff(names(sample.df),c(ID.VAR,TARGET.VAR))
data.classes <- sapply(explanatory.attributes,function(x){class(sample.df[[x]])})
# categorize data types in the data set?
unique.classes <- unique(data.classes)
attr.data.types <- lapply(unique.classes,function(x){names(data.classes[data.classes==x])})
names(attr.data.types) <- unique.classes
#Prepare data set for Boruta analysis. For this analysis, missing values are
#handled as follows:
#* missing numeric data is set to -1
#* missing character data is set to __*MISSING*__
# pull out the response variable
response <- sample.df$SalePrice
# remove identifier and response variables
sample.df <- sample.df[candidate.features]
# for numeric set missing values to -1 for purposes of the random forest run
for (x in attr.data.types$integer){
sample.df[[x]][is.na(sample.df[[x]])] <- -1
}
for (x in attr.data.types$character){
sample.df[[x]][is.na(sample.df[[x]])] <- "*MISSING*"
}
# Run Boruta Analysis
set.seed(13)
bor.results <- Boruta(sample.df,response,
maxRuns=101,
doTrace=0)
cat("\nSummary of Boruta run:\n")
print(bor.results)
cat("\n\nRelevant Attributes:\n")
getSelectedAttributes(bor.results)
plot(bor.results)
#Detailed results for each candidate explanatory attributes.
cat("\n\nAttribute Importance Details:\n")
options(width=125)
arrange(cbind(attr=rownames(attStats(bor.results)), attStats(bor.results)),desc(medianImp))
Missing values
aggr(train, prop = F, numbers = T)
apply(is.na(train),2,sum)## Id MSSubClass MSZoning LotFrontage LotArea
## 0 0 0 259 0
## Street Alley LotShape LandContour Utilities
## 0 1369 0 0 0
## LotConfig LandSlope Neighborhood Condition1 Condition2
## 0 0 0 0 0
## BldgType HouseStyle OverallQual OverallCond YearBuilt
## 0 0 0 0 0
## YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd
## 0 0 0 0 0
## MasVnrType MasVnrArea ExterQual ExterCond Foundation
## 8 8 0 0 0
## BsmtQual BsmtCond BsmtExposure BsmtFinType1 BsmtFinSF1
## 37 37 38 37 0
## BsmtFinType2 BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating
## 38 0 0 0 0
## HeatingQC CentralAir Electrical X1stFlrSF X2ndFlrSF
## 0 0 1 0 0
## LowQualFinSF GrLivArea BsmtFullBath BsmtHalfBath FullBath
## 0 0 0 0 0
## HalfBath BedroomAbvGr KitchenAbvGr KitchenQual TotRmsAbvGrd
## 0 0 0 0 0
## Functional Fireplaces FireplaceQu GarageType GarageYrBlt
## 0 0 690 81 81
## GarageFinish GarageCars GarageArea GarageQual GarageCond
## 81 0 0 81 81
## PavedDrive WoodDeckSF OpenPorchSF EnclosedPorch X3SsnPorch
## 0 0 0 0 0
## ScreenPorch PoolArea PoolQC Fence MiscFeature
## 0 0 1453 1179 1406
## MiscVal MoSold YrSold SaleType SaleCondition
## 0 0 0 0 0
## SalePrice
## 0Data Preparation
The goal here is to select the most relevant features, reshape them, handle missing values and outliers and get data ready to be processed by different machine learning models.
Feature selection
# 1. Incorporate results of Boruta analysis
Boruta_analysis <- c("MSSubClass","MSZoning","LotArea","LotShape","LandContour","Neighborhood",
"BldgType","HouseStyle","OverallQual","OverallCond","YearBuilt",
"YearRemodAdd","Exterior1st","Exterior2nd","MasVnrArea","ExterQual",
"Foundation","BsmtQual","BsmtCond","BsmtFinType1","BsmtFinSF1",
"BsmtFinType2","BsmtUnfSF","TotalBsmtSF","HeatingQC","CentralAir",
"X1stFlrSF","X2ndFlrSF","GrLivArea","BsmtFullBath","FullBath","HalfBath",
"BedroomAbvGr","KitchenAbvGr","KitchenQual","TotRmsAbvGrd","Functional",
"Fireplaces","FireplaceQu","GarageType","GarageYrBlt","GarageFinish",
"GarageCars","GarageArea","GarageQual","GarageCond","PavedDrive","WoodDeckSF",
"OpenPorchSF","Fence", "SalePrice")
train_selected_boruta <- train[Boruta_analysis]
# Identify near zero variance predictors: remove_cols
remove_cols <- nearZeroVar(train_selected_boruta, names = TRUE,
freqCut = 2, uniqueCut = 20)
# Remove predictors with low variance
all_cols <- names(train_selected_boruta)
train_selected <- train_selected_boruta[ , setdiff(all_cols, remove_cols)]Types
# transform all the charaters variable into factor
train_selected[sapply(train_selected, is.character)] <- lapply(train_selected[sapply(train_selected, is.character)], as.factor)Outliers
train_selected_outliers <- train_selected
# remove outliers
train_selected <- subset(train_selected,!(train_selected$SalePrice > quantile(train_selected$SalePrice, probs=c(.01, .99))[2] | train_selected$SalePrice < quantile(train_selected$SalePrice, probs=c(.01, .9))[1]) )
par(mfrow=c(1,2))
boxplot(train_selected_outliers$SalePrice, main="Before")
boxplot(train_selected$SalePrice, main="After")
Missing values
Heuristics or rules of thumb
- Numeric variables: PMM (Predictive Mean Matching)
- For Binary Variables( with 2 levels): logreg(Logistic Regression)
- For Factor Variables (>= 2 levels): polyreg(Bayesian polytomous regression)
As we keep this model simple we just use ppm.
# missingvaluenumeric <- MasVnrArea, GarageYrBlt
# missingvaluefactor <- c('BsmtQual', 'BsmtFinType1', 'FireplaceQu', 'GarageFinish')
## FireplaceQu miss almost 50% of value!!
tempData <- mice(train_selected,m=5,maxit=50,meth='pmm',seed=500)##
## iter imp variable
## 1 1 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 1 2 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 1 3 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 1 4 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 1 5 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 2 1 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 2 2 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 2 3 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 2 4 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 2 5 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 3 1 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 3 2 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 3 3 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 3 4 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 3 5 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 4 1 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 4 2 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 4 3 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 4 4 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 4 5 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 5 1 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 5 2 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 5 3 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 5 4 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 5 5 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 6 1 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 6 2 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 6 3 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 6 4 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 6 5 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 7 1 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 7 2 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 7 3 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 7 4 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 7 5 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 8 1 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 8 2 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 8 3 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 8 4 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 8 5 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 9 1 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 9 2 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 9 3 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
## 9 4 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinish
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## 50 5 MasVnrArea BsmtQual BsmtFinType1 FireplaceQu GarageYrBlt GarageFinishtrain_selected_reform <- complete(tempData,1)
apply(is.na(train_selected_reform),2,sum)## MSSubClass LotArea LotShape Neighborhood HouseStyle
## 0 0 0 0 0
## OverallQual YearBuilt YearRemodAdd MasVnrArea ExterQual
## 0 0 0 0 0
## Foundation BsmtQual BsmtFinType1 BsmtFinSF1 BsmtUnfSF
## 0 0 0 0 0
## TotalBsmtSF HeatingQC X1stFlrSF X2ndFlrSF GrLivArea
## 0 0 0 0 0
## BsmtFullBath FullBath HalfBath KitchenQual TotRmsAbvGrd
## 0 0 0 0 0
## Fireplaces FireplaceQu GarageYrBlt GarageFinish GarageArea
## 0 0 0 0 0
## SalePrice
## 0Modeling
We now build and evaluate two simple regression models that will need to be futher tuned.
Generalized Boosted Regression Models (Gbm)
### 1
# Train on cross-validation
train_control<- trainControl(method="cv", number=8, repeats=5)
# Build the Generalized Boosted Regression Models
gbm <- train(SalePrice~., data=train_selected_reform, trControl=train_control, method="gbm")## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1219 nan 0.1000 0.0121
## 2 0.1123 nan 0.1000 0.0097
## 3 0.1041 nan 0.1000 0.0080
## 4 0.0969 nan 0.1000 0.0071
## 5 0.0909 nan 0.1000 0.0058
## 6 0.0851 nan 0.1000 0.0052
## 7 0.0800 nan 0.1000 0.0049
## 8 0.0756 nan 0.1000 0.0038
## 9 0.0715 nan 0.1000 0.0040
## 10 0.0682 nan 0.1000 0.0031
## 20 0.0438 nan 0.1000 0.0013
## 40 0.0271 nan 0.1000 0.0004
## 60 0.0212 nan 0.1000 0.0001
## 80 0.0182 nan 0.1000 0.0000
## 100 0.0168 nan 0.1000 -0.0000
## 120 0.0159 nan 0.1000 0.0000
## 140 0.0153 nan 0.1000 -0.0000
## 150 0.0150 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1206 nan 0.1000 0.0136
## 2 0.1092 nan 0.1000 0.0113
## 3 0.0995 nan 0.1000 0.0098
## 4 0.0901 nan 0.1000 0.0091
## 5 0.0825 nan 0.1000 0.0070
## 6 0.0756 nan 0.1000 0.0066
## 7 0.0703 nan 0.1000 0.0054
## 8 0.0654 nan 0.1000 0.0047
## 9 0.0606 nan 0.1000 0.0045
## 10 0.0566 nan 0.1000 0.0038
## 20 0.0330 nan 0.1000 0.0013
## 40 0.0196 nan 0.1000 0.0002
## 60 0.0158 nan 0.1000 0.0000
## 80 0.0144 nan 0.1000 0.0000
## 100 0.0135 nan 0.1000 0.0000
## 120 0.0130 nan 0.1000 -0.0000
## 140 0.0123 nan 0.1000 -0.0000
## 150 0.0121 nan 0.1000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1185 nan 0.1000 0.0151
## 2 0.1053 nan 0.1000 0.0134
## 3 0.0945 nan 0.1000 0.0110
## 4 0.0846 nan 0.1000 0.0098
## 5 0.0771 nan 0.1000 0.0069
## 6 0.0702 nan 0.1000 0.0062
## 7 0.0640 nan 0.1000 0.0062
## 8 0.0590 nan 0.1000 0.0047
## 9 0.0541 nan 0.1000 0.0043
## 10 0.0499 nan 0.1000 0.0038
## 20 0.0276 nan 0.1000 0.0009
## 40 0.0165 nan 0.1000 0.0001
## 60 0.0139 nan 0.1000 0.0001
## 80 0.0126 nan 0.1000 -0.0000
## 100 0.0119 nan 0.1000 0.0000
## 120 0.0112 nan 0.1000 -0.0000
## 140 0.0106 nan 0.1000 -0.0000
## 150 0.0103 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1209 nan 0.1000 0.0123
## 2 0.1114 nan 0.1000 0.0098
## 3 0.1029 nan 0.1000 0.0079
## 4 0.0959 nan 0.1000 0.0070
## 5 0.0899 nan 0.1000 0.0060
## 6 0.0846 nan 0.1000 0.0052
## 7 0.0799 nan 0.1000 0.0043
## 8 0.0754 nan 0.1000 0.0041
## 9 0.0715 nan 0.1000 0.0039
## 10 0.0678 nan 0.1000 0.0037
## 20 0.0444 nan 0.1000 0.0014
## 40 0.0277 nan 0.1000 0.0003
## 60 0.0218 nan 0.1000 0.0001
## 80 0.0188 nan 0.1000 0.0001
## 100 0.0173 nan 0.1000 0.0000
## 120 0.0164 nan 0.1000 -0.0000
## 140 0.0158 nan 0.1000 -0.0000
## 150 0.0155 nan 0.1000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.1191 nan 0.1000 0.0143
## 2 0.1079 nan 0.1000 0.0113
## 3 0.0973 nan 0.1000 0.0107
## 4 0.0886 nan 0.1000 0.0082
## 5 0.0814 nan 0.1000 0.0072
## 6 0.0751 nan 0.1000 0.0068
## 7 0.0696 nan 0.1000 0.0055
## 8 0.0646 nan 0.1000 0.0044
## 9 0.0599 nan 0.1000 0.0046
## 10 0.0565 nan 0.1000 0.0034
## 20 0.0336 nan 0.1000 0.0014
## 40 0.0201 nan 0.1000 0.0002
## 60 0.0163 nan 0.1000 0.0000
## 80 0.0149 nan 0.1000 -0.0000
## 100 0.0140 nan 0.1000 -0.0000
## 120 0.0132 nan 0.1000 -0.0000
## 140 0.0126 nan 0.1000 -0.0000
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## 150 0.0105 nan 0.1000 -0.0000# make prediction on the train data
prediction <- predict(gbm, train_selected_reform)
binded <- cbind(train_selected_reform, prediction)Calculate the rmse
res <- binded$SalePrice - prediction
rmse <- sqrt(mean(res ^ 2))
print(rmse)## [1] 0.1023828Example: this code isn’t ran (it’s just an ideas on how this algorithm could be futher tuned)
library(hydroGOF)
library(Metrics)
caretGrid <- expand.grid(interaction.depth=c(1, 3, 5), n.trees = (0:50)*50,
shrinkage=c(0.01, 0.001),
n.minobsinnode=10)
metric <- "RMSE"
set.seed(99)
gbm.caret <- train(SalePrice ~ ., data=train_selected_reform, method="gbm",
trControl=train_control, verbose=FALSE,
tuneGrid=caretGrid, metric=metric, bag.fraction=0.75)
print(gbm.caret)Extreme Gradient Boosting (Xgboost)
xgbTree <- train(SalePrice~., data=train_selected_reform, trControl=train_control, method="xgbTree")
# make prediction on the train data
prediction <- predict(xgbTree, train_selected_reform)
binded <- cbind(train_selected_reform, prediction)Calculate the rmse
res <- binded$SalePrice - prediction
rmse <- sqrt(mean(res ^ 2))
print(rmse)## [1] 0.09031233*learn more about how to tune a Xgboost here https://github.com/topepo/caret/blob/master/RegressionTests/Code/xgbTree.R
---
title: <center> Kaggle Playground - Regression Problem</center>
output:
  
  html_document:
    code_download: yes
    code_folding: show
    highlight: tango
    theme: sandstone
    toc: yes
    toc_float: yes
   # number_sections: false
    df_print: paged
---

<br/>

<center> <h5> This is a **simplified version** of the program used for the Kaggle competition ["House Prices"](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) </h5></center>
<center> 12/2016 </center>

<br/>

![](house.jpg)

<br/>

# Data science problem {.tabset}

## Data descriptions

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

The potential for creative feature engineering provides a rich opportunity for fun and learning. This dataset lends itself to advanced regression techniques like random forests and gradient boosting with the popular XGBoost library. We encourage Kagglers to create benchmark code and tutorials on Kernels for community learning. Top kernels will be awarded swag prizes at the competition close. 

Acknowledgments

The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset. 

<br/>
<hr/>
<br/>

<center> Find the data and the code on [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/) or on [Github](https://github.com/ludovicbenistant/Kaggle) </center>

<br/>
<hr/>
<br/>

## File descriptions

train.csv - the training set  
test.csv - the test set  
data_description.txt - full description of each column, originally prepared by Dean De Cock but lightly edited to match the column names used here  
sample_submission.csv - a benchmark submission from a linear regression on year and month of sale, lot square footage, and number of bedrooms  

<br/>
<hr/>
<br/>

<center> Find the data and the code on [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/) or on [Github](https://github.com/ludovicbenistant/Kaggle) </center>

<br/>
<hr/>
<br/>

## Variables

SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict.  
MSSubClass: The building class  
MSZoning: The general zoning classification  
LotFrontage: Linear feet of street connected to property  
LotArea: Lot size in square feet  
Street: Type of road access  
Alley: Type of alley access  
LotShape: General shape of property  
LandContour: Flatness of the property
Utilities: Type of utilities available  
LotConfig: Lot configuration  
LandSlope: Slope of property  
Neighborhood: Physical locations within Ames city limits  
Condition1: Proximity to main road or railroad  
Condition2: Proximity to main road or railroad (if a second is present)  
BldgType: Type of dwelling  
HouseStyle: Style of dwelling  
OverallQual: Overall material and finish quality  
OverallCond: Overall condition rating  
YearBuilt: Original construction date  
YearRemodAdd: Remodel date  
RoofStyle: Type of roof  
RoofMatl: Roof material  
Exterior1st: Exterior covering on house  
Exterior2nd: Exterior covering on house (if more than one material)  
MasVnrType: Masonry veneer type  
MasVnrArea: Masonry veneer area in square feet  
ExterQual: Exterior material quality  
ExterCond: Present condition of the material on the exterior  
Foundation: Type of foundation  
BsmtQual: Height of the basement  
BsmtCond: General condition of the basement  
BsmtExposure: Walkout or garden level basement walls  
BsmtFinType1: Quality of basement finished area  
BsmtFinSF1: Type 1 finished square feet  
BsmtFinType2: Quality of second finishedarea (if present)  
BsmtFinSF2: Type 2 finished square feet  
BsmtUnfSF: Unfinished square feet of basement area  
TotalBsmtSF: Total square feet of basement area  
Heating: Type of heating  
HeatingQC: Heating quality and condition  
CentralAir: Central air conditioning  
Electrical: Electrical system  
1stFlrSF: First Floor square feet  
2ndFlrSF: Second floor square feet   
LowQualFinSF: Low quality finished square feet (all floors)  
GrLivArea: Above grade (ground) living area square feet  
BsmtFullBath: Basement full bathrooms  
BsmtHalfBath: Basement half bathrooms  
FullBath: Full bathrooms above grade  
HalfBath: Half baths above grade  
Bedroom: Number of bedrooms above basement level  
Kitchen: Number of kitchens  
KitchenQual: Kitchen quality  
TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)  
Functional: Home functionality rating  
Fireplaces: Number of fireplaces  
FireplaceQu: Fireplace quality  
GarageType: Garage location  
GarageYrBlt: Year garage was built  
GarageFinish: Interior finish of the garage  
GarageCars: Size of garage in car capacity  
GarageArea: Size of garage in square feet  
GarageQual: Garage quality  
GarageCond: Garage condition  
PavedDrive: Paved driveway  
WoodDeckSF: Wood deck area in square feet  
OpenPorchSF: Open porch area in square feet  
EnclosedPorch: Enclosed porch area in square feet  
3SsnPorch: Three season porch area in square feet  
ScreenPorch: Screen porch area in square feet  
PoolArea: Pool area in square feet  
PoolQC: Pool quality  
Fence: Fence quality  
MiscFeature: Miscellaneous feature not covered in other categories  
MiscVal: $Value of miscellaneous feature  
MoSold: Month Sold  
YrSold: Year Sold  
SaleType: Type of sale  
SaleCondition: Condition of sale  

<br/>
<hr/>
<br/>

<center> Find the data and the code on [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/) or on [Github](https://github.com/ludovicbenistant/Kaggle) </center>

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<br/>

## About Kaggle

In 2010, Kaggle was founded as a platform for predictive modelling and analytics competitions on which companies and researchers post their data and statisticians and data miners from all over the world compete to produce the best models.  
  
This crowdsourcing approach relies on the fact that there are countless strategies that can be applied to any predictive modelling task and it is impossible to know at the outset which technique or analyst will be most effective. Kaggle also hosts recruiting competitions in which data scientists compete for a chance to interview at leading data science companies like Facebook, Winton Capital, and Walmart.

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<br/>

<center> Find the data and the code on [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/) or on [Github](https://github.com/ludovicbenistant/Kaggle) </center>

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<br/>

# Libraries

<br/>

```{r, warning=F, message=F, echo=TRUE}
# For data manipulation and tidying
library(MASS)
library(tidyr)
library(plyr)
library(dplyr)
library(broom)
library(data.table)
library(testthat)
library(gridExtra)

# For data visualizations
library(ggplot2)
library(plotly)
library(DT)
library(corrplot)
library(GGally)
library(Boruta)
library(pROC)
library(VIM)
library(mice)

# For modeling and predictions
library(mlbench)
library(caret)
library(glmnet)
library(ranger)
library(clValid)
library(e1071)
library(xgboost)

```

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<hr/>
<br/>

# Data Exploration {.tabset}

<br/>

## Data

```{r, message=F, warning=F}
train <- read.csv("train.csv", header = TRUE, sep = ",", stringsAsFactors = FALSE)
data.test <- read.csv("test.csv", header = TRUE, sep = ",", stringsAsFactors = FALSE)
datatable(head(train, n=20),options = list(scrollX = TRUE))
```

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<hr/>
<br/>

## Summary

```{r}
summary(train)
```

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<hr/>
<br/>

## Visualisation

Thanks to [laurae2](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/) for this code for plotting all data using tabplots. The objective is to find out some of the good features visually. As Laurae2 say: you can think of it as the vertical as the "sort by SalePrice":

```{r, fig.width = 11, fig.height = 5.5, message = FALSE, warning = FALSE}
invisible(library(tabplot))
invisible(library(data.table))

columns <- c("numeric",
             rep("character", 2),
             rep("numeric", 2),
             rep("character", 12),
             rep("numeric", 4),
             rep("character", 5),
             "numeric",
             rep("character", 7),
             "numeric",
             "character",
             rep("numeric", 3),
             rep("character", 4),
             rep("numeric", 10),
             "character",
             "numeric",
             "character",
             "numeric",
             rep("character", 2),
             "numeric",
             "character",
             rep("numeric", 2),
             rep("character", 3),
             rep("numeric", 6),
             rep("character", 3),
             rep("numeric", 3),
             rep("character", 2),
             rep("numeric"))

train$SalePrice <- log(train$SalePrice) # To respect lrmse
train_visu <- as.data.frame(train)

for (i in 1:80) {
  if (typeof(train_visu[, i]) == "character") {
    train_visu[is.na(train_visu[, i]), i] <- ""
    train_visu[, i] <- as.factor(train_visu[, i])
  }
}

for (i in 1:16) {
  plot(tableplot(train_visu, select = c(((i - 1) * 5 + 1):(i * 5), 81), sortCol = 6, nBins = 73, plot = FALSE), fontsize = 12, title = paste("log(SalePrice) vs ", paste(colnames(train_visu)[((i - 1) * 5 + 1):(i * 5)], collapse = "+"), sep = ""), showTitle = TRUE, fontsize.title = 12)
}

```

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<hr/>
<br/>

## Boruta

Thanks to [Jim Thompson (JMT5802)](https://www.kaggle.com/jimthompson/house-prices-advanced-regression-techniques/boruta-feature-importance-analysis/code) for this **Boruta Feature Importance Analysis**. This report determines what features may be relevant to predicting house sale price. This analysis is based on the [Boruta](https://cran.r-project.org/web/packages/Boruta/index.html) package. The code can be found [here](https://www.kaggle.com/jimthompson/house-prices-advanced-regression-techniques/boruta-feature-importance-analysis/code).

```{r, eval=FALSE}
ID.VAR <- "Id"
TARGET.VAR <- "SalePrice"

# Data Preparation for Bourta Analysis
# retrive data for analysis
sample.df <- read.csv(file.path(ROOT.DIR,"input/train.csv"),stringsAsFactors = FALSE)
# extract only candidate feture names
candidate.features <- setdiff(names(sample.df),c(ID.VAR,TARGET.VAR))
data.type <- sapply(candidate.features,function(x){class(sample.df[[x]])})
# deterimine data types
explanatory.attributes <- setdiff(names(sample.df),c(ID.VAR,TARGET.VAR))
data.classes <- sapply(explanatory.attributes,function(x){class(sample.df[[x]])})
# categorize data types in the data set?
unique.classes <- unique(data.classes)
attr.data.types <- lapply(unique.classes,function(x){names(data.classes[data.classes==x])})
names(attr.data.types) <- unique.classes

#Prepare data set for Boruta analysis.  For this analysis, missing values are
#handled as follows:
#* missing numeric data is set to -1
#* missing character data is set to __*MISSING*__

# pull out the response variable
response <- sample.df$SalePrice

# remove identifier and response variables
sample.df <- sample.df[candidate.features]

# for numeric set missing values to -1 for purposes of the random forest run
for (x in attr.data.types$integer){
  sample.df[[x]][is.na(sample.df[[x]])] <- -1
}

for (x in attr.data.types$character){
  sample.df[[x]][is.na(sample.df[[x]])] <- "*MISSING*"
}

# Run Boruta Analysis
set.seed(13)
bor.results <- Boruta(sample.df,response,
                   maxRuns=101,
                   doTrace=0)
cat("\nSummary of Boruta run:\n")
print(bor.results)

cat("\n\nRelevant Attributes:\n")
getSelectedAttributes(bor.results)
plot(bor.results)

#Detailed results for each candidate explanatory attributes.

cat("\n\nAttribute Importance Details:\n")
options(width=125)
arrange(cbind(attr=rownames(attStats(bor.results)), attStats(bor.results)),desc(medianImp))
```


![](Boruta.jpg)
![](Borutadt.jpg)

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## Missing values

```{r, message=F, warning=F, fig.width=10}
aggr(train, prop = F, numbers = T)
apply(is.na(train),2,sum)
```

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<br/>

# Data Preparation {.tabset}

<br/>

The goal here is to **select the most relevant features**, **reshape them**, **handle missing values and outliers** and get data ready to be processed by different machine learning models.

<br/>

## Feature selection

```{r}
# 1. Incorporate results of Boruta analysis
Boruta_analysis <- c("MSSubClass","MSZoning","LotArea","LotShape","LandContour","Neighborhood",
                    "BldgType","HouseStyle","OverallQual","OverallCond","YearBuilt",
                    "YearRemodAdd","Exterior1st","Exterior2nd","MasVnrArea","ExterQual",
                    "Foundation","BsmtQual","BsmtCond","BsmtFinType1","BsmtFinSF1",
                    "BsmtFinType2","BsmtUnfSF","TotalBsmtSF","HeatingQC","CentralAir",
                    "X1stFlrSF","X2ndFlrSF","GrLivArea","BsmtFullBath","FullBath","HalfBath",
                    "BedroomAbvGr","KitchenAbvGr","KitchenQual","TotRmsAbvGrd","Functional",
                    "Fireplaces","FireplaceQu","GarageType","GarageYrBlt","GarageFinish",
                    "GarageCars","GarageArea","GarageQual","GarageCond","PavedDrive","WoodDeckSF",
                    "OpenPorchSF","Fence", "SalePrice")

train_selected_boruta <- train[Boruta_analysis]

# Identify near zero variance predictors: remove_cols
remove_cols <- nearZeroVar(train_selected_boruta, names = TRUE, 
                           freqCut = 2, uniqueCut = 20)

# Remove predictors with low variance 
all_cols <- names(train_selected_boruta)
train_selected <- train_selected_boruta[ , setdiff(all_cols, remove_cols)]
```

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<br/>

## Types

```{r}
# transform all the charaters variable into factor
train_selected[sapply(train_selected, is.character)] <- lapply(train_selected[sapply(train_selected, is.character)], as.factor)
```

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<br/>

## Outliers

```{r, fig.width=10}
train_selected_outliers <- train_selected
# remove outliers
train_selected <- subset(train_selected,!(train_selected$SalePrice > quantile(train_selected$SalePrice, probs=c(.01, .99))[2] | train_selected$SalePrice < quantile(train_selected$SalePrice, probs=c(.01, .9))[1]) ) 

par(mfrow=c(1,2))
boxplot(train_selected_outliers$SalePrice, main="Before")
boxplot(train_selected$SalePrice, main="After")
```

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<br/>

## Missing values

**Heuristics or rules of thumb**

- Numeric variables: PMM (Predictive Mean Matching)  
- For Binary Variables( with 2 levels): logreg(Logistic Regression)  
- For Factor Variables (>= 2 levels): polyreg(Bayesian polytomous regression)    

As we **keep this model simple we just use ppm**.

```{r}
# missingvaluenumeric <- MasVnrArea, GarageYrBlt
# missingvaluefactor <- c('BsmtQual', 'BsmtFinType1', 'FireplaceQu', 'GarageFinish') 
## FireplaceQu miss almost 50% of value!! 
tempData <- mice(train_selected,m=5,maxit=50,meth='pmm',seed=500)
train_selected_reform <- complete(tempData,1)
apply(is.na(train_selected_reform),2,sum)

```

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<hr/>
<br/>

# Modeling {.tabset}

<br/> 

We now build and evaluate two **simple regression models** that will need to be futher tuned. 

## Generalized Boosted Regression Models (Gbm)


```{r, warning=F, message=F}
### 1
# Train on cross-validation
train_control<- trainControl(method="cv", number=8, repeats=5)

# Build the Generalized Boosted Regression Models
gbm <- train(SalePrice~., data=train_selected_reform, trControl=train_control, method="gbm")

# make prediction on the train data
prediction <- predict(gbm, train_selected_reform)
binded <- cbind(train_selected_reform, prediction)
```

Calculate the **rmse**

```{r}
res <- binded$SalePrice - prediction
rmse <- sqrt(mean(res ^ 2))
print(rmse)
```

Example: this code isn't ran (it's just an ideas on how this algorithm could be futher tuned)

```{r, eval=FALSE}
library(hydroGOF)
library(Metrics)

caretGrid <- expand.grid(interaction.depth=c(1, 3, 5), n.trees = (0:50)*50,
                   shrinkage=c(0.01, 0.001),
                   n.minobsinnode=10)
metric <- "RMSE"

set.seed(99)
gbm.caret <- train(SalePrice ~ ., data=train_selected_reform, method="gbm",
              trControl=train_control, verbose=FALSE, 
              tuneGrid=caretGrid, metric=metric, bag.fraction=0.75)

print(gbm.caret)

```


<center> Find the data and the code on [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/) or on [Github](https://github.com/ludovicbenistant/Kaggle) </center>

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<br/>


## Extreme Gradient Boosting (Xgboost)


```{r, warning=F, message=F}
xgbTree <- train(SalePrice~., data=train_selected_reform, trControl=train_control, method="xgbTree")
# make prediction on the train data
prediction <- predict(xgbTree, train_selected_reform)
binded <- cbind(train_selected_reform, prediction)
```

Calculate the **rmse**

```{r}
res <- binded$SalePrice - prediction
rmse <- sqrt(mean(res ^ 2))
print(rmse)
```

*learn more about how to tune a Xgboost here https://github.com/topepo/caret/blob/master/RegressionTests/Code/xgbTree.R

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<hr/>
<br/>

<center> Find the data and the code on [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/) or on [Github](https://github.com/ludovicbenistant/Kaggle) </center>

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