#setting the working directory to be home directory prior to beginning
setwd("~/Desktop/Projects/Rossman/")
#loading important libraries for data analysis ahead
library(readr)
library(dplyr)
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
#reading the training data
training_data <- read.csv("train.csv")
#looking at structure and first few entries of training data
str(training_data)
## 'data.frame': 1017209 obs. of 9 variables:
## $ Store : int 1 2 3 4 5 6 7 8 9 10 ...
## $ DayOfWeek : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Date : Factor w/ 942 levels "2013-01-01","2013-01-02",..: 942 942 942 942 942 942 942 942 942 942 ...
## $ Sales : int 5263 6064 8314 13995 4822 5651 15344 8492 8565 7185 ...
## $ Customers : int 555 625 821 1498 559 589 1414 833 687 681 ...
## $ Open : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Promo : int 1 1 1 1 1 1 1 1 1 1 ...
## $ StateHoliday : Factor w/ 4 levels "0","a","b","c": 1 1 1 1 1 1 1 1 1 1 ...
## $ SchoolHoliday: int 1 1 1 1 1 1 1 1 1 1 ...
dim(training_data)
## [1] 1017209 9
head(training_data,2)
## Store DayOfWeek Date Sales Customers Open Promo StateHoliday
## 1 1 5 2015-07-31 5263 555 1 1 0
## 2 2 5 2015-07-31 6064 625 1 1 0
## SchoolHoliday
## 1 1
## 2 1
#getting descriptive statistics for train dataa
summary(training_data)
## Store DayOfWeek Date Sales
## Min. : 1.0 Min. :1.000 2013-01-02: 1115 Min. : 0
## 1st Qu.: 280.0 1st Qu.:2.000 2013-01-03: 1115 1st Qu.: 3727
## Median : 558.0 Median :4.000 2013-01-04: 1115 Median : 5744
## Mean : 558.4 Mean :3.998 2013-01-05: 1115 Mean : 5774
## 3rd Qu.: 838.0 3rd Qu.:6.000 2013-01-06: 1115 3rd Qu.: 7856
## Max. :1115.0 Max. :7.000 2013-01-07: 1115 Max. :41551
## (Other) :1010519
## Customers Open Promo StateHoliday
## Min. : 0.0 Min. :0.0000 Min. :0.0000 0:986159
## 1st Qu.: 405.0 1st Qu.:1.0000 1st Qu.:0.0000 a: 20260
## Median : 609.0 Median :1.0000 Median :0.0000 b: 6690
## Mean : 633.1 Mean :0.8301 Mean :0.3815 c: 4100
## 3rd Qu.: 837.0 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :7388.0 Max. :1.0000 Max. :1.0000
##
## SchoolHoliday
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.1786
## 3rd Qu.:0.0000
## Max. :1.0000
##
#reading the testing data
testing_data <- read.csv("test.csv")
#looking at structure and first few entries of testing data
str(testing_data)
## 'data.frame': 41088 obs. of 8 variables:
## $ Id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Store : int 1 3 7 8 9 10 11 12 13 14 ...
## $ DayOfWeek : int 4 4 4 4 4 4 4 4 4 4 ...
## $ Date : Factor w/ 48 levels "2015-08-01","2015-08-02",..: 48 48 48 48 48 48 48 48 48 48 ...
## $ Open : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Promo : int 1 1 1 1 1 1 1 1 1 1 ...
## $ StateHoliday : Factor w/ 2 levels "0","a": 1 1 1 1 1 1 1 1 1 1 ...
## $ SchoolHoliday: int 0 0 0 0 0 0 0 0 0 0 ...
dim(testing_data)
## [1] 41088 8
head(testing_data,2)
## Id Store DayOfWeek Date Open Promo StateHoliday SchoolHoliday
## 1 1 1 4 2015-09-17 1 1 0 0
## 2 2 3 4 2015-09-17 1 1 0 0
#getting descriptive statistics for test data
summary(testing_data)
## Id Store DayOfWeek Date
## Min. : 1 Min. : 1.0 Min. :1.000 2015-08-01: 856
## 1st Qu.:10273 1st Qu.: 279.8 1st Qu.:2.000 2015-08-02: 856
## Median :20544 Median : 553.5 Median :4.000 2015-08-03: 856
## Mean :20544 Mean : 555.9 Mean :3.979 2015-08-04: 856
## 3rd Qu.:30816 3rd Qu.: 832.2 3rd Qu.:6.000 2015-08-05: 856
## Max. :41088 Max. :1115.0 Max. :7.000 2015-08-06: 856
## (Other) :35952
## Open Promo StateHoliday SchoolHoliday
## Min. :0.0000 Min. :0.0000 0:40908 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.0000 a: 180 1st Qu.:0.0000
## Median :1.0000 Median :0.0000 Median :0.0000
## Mean :0.8543 Mean :0.3958 Mean :0.4435
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :11
#looking store data information
store_data <- read.csv("store.csv")
str(store_data)
## 'data.frame': 1115 obs. of 10 variables:
## $ Store : int 1 2 3 4 5 6 7 8 9 10 ...
## $ StoreType : Factor w/ 4 levels "a","b","c","d": 3 1 1 3 1 1 1 1 1 1 ...
## $ Assortment : Factor w/ 3 levels "a","b","c": 1 1 1 3 1 1 3 1 3 1 ...
## $ CompetitionDistance : int 1270 570 14130 620 29910 310 24000 7520 2030 3160 ...
## $ CompetitionOpenSinceMonth: int 9 11 12 9 4 12 4 10 8 9 ...
## $ CompetitionOpenSinceYear : int 2008 2007 2006 2009 2015 2013 2013 2014 2000 2009 ...
## $ Promo2 : int 0 1 1 0 0 0 0 0 0 0 ...
## $ Promo2SinceWeek : int NA 13 14 NA NA NA NA NA NA NA ...
## $ Promo2SinceYear : int NA 2010 2011 NA NA NA NA NA NA NA ...
## $ PromoInterval : Factor w/ 4 levels "","Feb,May,Aug,Nov",..: 1 3 3 1 1 1 1 1 1 1 ...
dim(store_data)
## [1] 1115 10
head(store_data,2)
## Store StoreType Assortment CompetitionDistance CompetitionOpenSinceMonth
## 1 1 c a 1270 9
## 2 2 a a 570 11
## CompetitionOpenSinceYear Promo2 Promo2SinceWeek Promo2SinceYear
## 1 2008 0 NA NA
## 2 2007 1 13 2010
## PromoInterval
## 1
## 2 Jan,Apr,Jul,Oct
#getting descriptive statistics for store data
summary(store_data)
## Store StoreType Assortment CompetitionDistance
## Min. : 1.0 a:602 a:593 Min. : 20.0
## 1st Qu.: 279.5 b: 17 b: 9 1st Qu.: 717.5
## Median : 558.0 c:148 c:513 Median : 2325.0
## Mean : 558.0 d:348 Mean : 5404.9
## 3rd Qu.: 836.5 3rd Qu.: 6882.5
## Max. :1115.0 Max. :75860.0
## NA's :3
## CompetitionOpenSinceMonth CompetitionOpenSinceYear Promo2
## Min. : 1.000 Min. :1900 Min. :0.0000
## 1st Qu.: 4.000 1st Qu.:2006 1st Qu.:0.0000
## Median : 8.000 Median :2010 Median :1.0000
## Mean : 7.225 Mean :2009 Mean :0.5121
## 3rd Qu.:10.000 3rd Qu.:2013 3rd Qu.:1.0000
## Max. :12.000 Max. :2015 Max. :1.0000
## NA's :354 NA's :354
## Promo2SinceWeek Promo2SinceYear PromoInterval
## Min. : 1.0 Min. :2009 :544
## 1st Qu.:13.0 1st Qu.:2011 Feb,May,Aug,Nov :130
## Median :22.0 Median :2012 Jan,Apr,Jul,Oct :335
## Mean :23.6 Mean :2012 Mar,Jun,Sept,Dec:106
## 3rd Qu.:37.0 3rd Qu.:2013
## Max. :50.0 Max. :2015
## NA's :544 NA's :544
#looking at sample_submission
sample_submission <- read.csv("sample_submission.csv")
str(sample_submission)
## 'data.frame': 41088 obs. of 2 variables:
## $ Id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Sales: int 0 0 0 0 0 0 0 0 0 0 ...
dim(sample_submission)
## [1] 41088 2
head(sample_submission,2)
## Id Sales
## 1 1 0
## 2 2 0
#Preprocessing with training data
#attaching the data sets to be used to directly refer to their column names
attach(training_data)
attach(testing_data)
## The following objects are masked from training_data:
##
## Date, DayOfWeek, Open, Promo, SchoolHoliday, StateHoliday,
## Store
attach(store_data)
## The following object is masked from testing_data:
##
## Store
##
## The following object is masked from training_data:
##
## Store
#converting Store, DayOfWeek, Open, Promo, SchoolHoliday variables into factor
#variables as it does not make sense to treat them as integers in further analysis.
#factorization with training data
training_data$Store <- as.factor(training_data$Store)
training_data$DayOfWeek <- as.factor(training_data$DayOfWeek)
training_data$Open <- as.factor(training_data$Open)
training_data$Promo <- as.factor(training_data$Promo)
training_data$SchoolHoliday <- as.factor(training_data$SchoolHoliday)
#factorization with testing data set
testing_data$Id <- as.factor(testing_data$Id)
testing_data$Store <- as.factor(testing_data$Store)
testing_data$DayOfWeek <- as.factor(testing_data$DayOfWeek)
testing_data$Open <- as.factor(testing_data$Open)
testing_data$Promo <- as.factor(testing_data$Promo)
testing_data$SchoolHoliday <- as.factor(testing_data$SchoolHoliday)
#factorization with store data
store_data$Store <- as.factor(store_data$Store)
store_data$CompetitionDistance <- as.numeric(store_data$CompetitionDistance)
store_data$Promo2 <- as.factor(store_data$Promo2)
store_data$PromoInterval <- as.factor(store_data$PromoInterval)
#Factorization ends for all datasets, just checking if indeed has been done,
#let's check the structure of all three datasets
str(training_data)
## 'data.frame': 1017209 obs. of 9 variables:
## $ Store : Factor w/ 1115 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ DayOfWeek : Factor w/ 7 levels "1","2","3","4",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ Date : Factor w/ 942 levels "2013-01-01","2013-01-02",..: 942 942 942 942 942 942 942 942 942 942 ...
## $ Sales : int 5263 6064 8314 13995 4822 5651 15344 8492 8565 7185 ...
## $ Customers : int 555 625 821 1498 559 589 1414 833 687 681 ...
## $ Open : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ Promo : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ StateHoliday : Factor w/ 4 levels "0","a","b","c": 1 1 1 1 1 1 1 1 1 1 ...
## $ SchoolHoliday: Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
str(testing_data)
## 'data.frame': 41088 obs. of 8 variables:
## $ Id : Factor w/ 41088 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Store : Factor w/ 856 levels "1","3","7","8",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ DayOfWeek : Factor w/ 7 levels "1","2","3","4",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ Date : Factor w/ 48 levels "2015-08-01","2015-08-02",..: 48 48 48 48 48 48 48 48 48 48 ...
## $ Open : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ Promo : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ StateHoliday : Factor w/ 2 levels "0","a": 1 1 1 1 1 1 1 1 1 1 ...
## $ SchoolHoliday: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
str(store_data)
## 'data.frame': 1115 obs. of 10 variables:
## $ Store : Factor w/ 1115 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ StoreType : Factor w/ 4 levels "a","b","c","d": 3 1 1 3 1 1 1 1 1 1 ...
## $ Assortment : Factor w/ 3 levels "a","b","c": 1 1 1 3 1 1 3 1 3 1 ...
## $ CompetitionDistance : num 1270 570 14130 620 29910 ...
## $ CompetitionOpenSinceMonth: int 9 11 12 9 4 12 4 10 8 9 ...
## $ CompetitionOpenSinceYear : int 2008 2007 2006 2009 2015 2013 2013 2014 2000 2009 ...
## $ Promo2 : Factor w/ 2 levels "0","1": 1 2 2 1 1 1 1 1 1 1 ...
## $ Promo2SinceWeek : int NA 13 14 NA NA NA NA NA NA NA ...
## $ Promo2SinceYear : int NA 2010 2011 NA NA NA NA NA NA NA ...
## $ PromoInterval : Factor w/ 4 levels "","Feb,May,Aug,Nov",..: 1 3 3 1 1 1 1 1 1 1 ...
#Note : We can see that there are no customers in testing dataset
#Now we split date column of both testing and training datasets into three separate columns
#for day, month and year respectively, for increasing our predictors for better learning from
#data
training_data$Date <- as.Date(training_data$Date)
training_data <- within(training_data, {
year <- as.integer(format(training_data$Date, "%Y"))
month <- as.integer(format(training_data$Date, "%m"))
day <- as.integer(format(training_data$Date, "%d"))
})
testing_data$Date <- as.Date(testing_data$Date)
testing_data <- within(testing_data, {
year <- as.integer(format(testing_data$Date, "%Y"))
month <- as.integer(format(testing_data$Date, "%m"))
day <- as.integer(format(testing_data$Date, "%d"))
})
#Refactorizing training and test datasets again
training_data$month <- as.factor(training_data$month)
training_data$day <- as.factor(training_data$day)
training_data$year <- as.factor(training_data$year)
testing_data$month <- as.factor(testing_data$month)
testing_data$day <- as.factor(testing_data$day)
testing_data$year <- as.factor(testing_data$year)
#we now, drop down date variable as have already captured it's information separately in day, month and year respectively
testing_data <- within(testing_data,rm(Date))
#Now, we also drop Date column from training_data for similar reasons as was the case with testing data
training_data <- within(training_data,rm(Date))
#getting descriptive stats for both training and testing datasets
summary(training_data)
## Store DayOfWeek Sales Customers Open
## 1 : 942 1:144730 Min. : 0 Min. : 0.0 0:172817
## 2 : 942 2:145664 1st Qu.: 3727 1st Qu.: 405.0 1:844392
## 3 : 942 3:145665 Median : 5744 Median : 609.0
## 4 : 942 4:145845 Mean : 5774 Mean : 633.1
## 5 : 942 5:145845 3rd Qu.: 7856 3rd Qu.: 837.0
## 6 : 942 6:144730 Max. :41551 Max. :7388.0
## (Other):1011557 7:144730
## Promo StateHoliday SchoolHoliday day month
## 0:629129 0:986159 0:835488 2 : 33485 3 :103695
## 1:388080 a: 20260 1:181721 3 : 33485 5 :103695
## b: 6690 4 : 33485 1 :103694
## c: 4100 5 : 33485 4 :100350
## 6 : 33485 6 :100350
## 7 : 33485 7 : 98115
## (Other):816299 (Other):407310
## year
## 2013:406974
## 2014:373855
## 2015:236380
##
##
##
##
summary(testing_data)
## Id Store DayOfWeek Open Promo
## 1 : 1 1 : 48 1:5992 0 : 5984 0:24824
## 2 : 1 3 : 48 2:5992 1 :35093 1:16264
## 3 : 1 7 : 48 3:5992 NA's: 11
## 4 : 1 8 : 48 4:5992
## 5 : 1 9 : 48 5:5136
## 6 : 1 10 : 48 6:5992
## (Other):41082 (Other):40800 7:5992
## StateHoliday SchoolHoliday day month year
## 0:40908 0:22866 1 : 1712 8:26536 2015:41088
## a: 180 1:18222 2 : 1712 9:14552
## 3 : 1712
## 4 : 1712
## 5 : 1712
## 6 : 1712
## (Other):30816
#Important Observations
#Observation:1 We see here that testing data does not have representation of b and c factor levels in
#the StateHoliday variable.
#Observation 2: Test data has 11 NA's under the Open variable where as training data has none
#Now, let us check out the stores where these NA's are occuring
testing_data[is.na(Open),]
## Id Store DayOfWeek Open Promo StateHoliday SchoolHoliday day
## 480 480 622 4 <NA> 1 0 0 17
## 1336 1336 622 3 <NA> 1 0 0 16
## 2192 2192 622 2 <NA> 1 0 0 15
## 3048 3048 622 1 <NA> 1 0 0 14
## 4760 4760 622 6 <NA> 0 0 0 12
## 5616 5616 622 5 <NA> 0 0 0 11
## 6472 6472 622 4 <NA> 0 0 0 10
## 7328 7328 622 3 <NA> 0 0 0 9
## 8184 8184 622 2 <NA> 0 0 0 8
## 9040 9040 622 1 <NA> 0 0 0 7
## 10752 10752 622 6 <NA> 0 0 0 5
## month year
## 480 9 2015
## 1336 9 2015
## 2192 9 2015
## 3048 9 2015
## 4760 9 2015
## 5616 9 2015
## 6472 9 2015
## 7328 9 2015
## 8184 9 2015
## 9040 9 2015
## 10752 9 2015
#We see that missing data for Open is only at Store number 622.
#Since we have missing data here, we want to account for this missing data and fill these
#NA values with some meaningful replacement. So, let us checkout on what all days do the Store 622
#open
subset(testing_data,(testing_data$Store == 622 & testing_data$Open == 1))
## Id Store DayOfWeek Open Promo StateHoliday SchoolHoliday day
## 11608 11608 622 5 1 1 0 0 4
## 12464 12464 622 4 1 1 0 0 3
## 13320 13320 622 3 1 1 0 0 2
## 14176 14176 622 2 1 1 0 0 1
## 15032 15032 622 1 1 1 0 0 31
## 16744 16744 622 6 1 0 0 0 29
## 17600 17600 622 5 1 0 0 1 28
## 18456 18456 622 4 1 0 0 1 27
## 19312 19312 622 3 1 0 0 1 26
## 20168 20168 622 2 1 0 0 1 25
## 21024 21024 622 1 1 0 0 1 24
## 22736 22736 622 6 1 0 0 0 22
## 23592 23592 622 5 1 1 0 1 21
## 24448 24448 622 4 1 1 0 1 20
## 25304 25304 622 3 1 1 0 1 19
## 26160 26160 622 2 1 1 0 1 18
## 27016 27016 622 1 1 1 0 1 17
## 28728 28728 622 6 1 0 0 0 15
## 29584 29584 622 5 1 0 0 1 14
## 30440 30440 622 4 1 0 0 1 13
## 31296 31296 622 3 1 0 0 1 12
## 32152 32152 622 2 1 0 0 1 11
## 33008 33008 622 1 1 0 0 1 10
## 34720 34720 622 6 1 0 0 0 8
## 35576 35576 622 5 1 1 0 1 7
## 36432 36432 622 4 1 1 0 1 6
## 37288 37288 622 3 1 1 0 1 5
## 38144 38144 622 2 1 1 0 1 4
## 39000 39000 622 1 1 1 0 1 3
## 40712 40712 622 6 1 0 0 0 1
## month year
## 11608 9 2015
## 12464 9 2015
## 13320 9 2015
## 14176 9 2015
## 15032 8 2015
## 16744 8 2015
## 17600 8 2015
## 18456 8 2015
## 19312 8 2015
## 20168 8 2015
## 21024 8 2015
## 22736 8 2015
## 23592 8 2015
## 24448 8 2015
## 25304 8 2015
## 26160 8 2015
## 27016 8 2015
## 28728 8 2015
## 29584 8 2015
## 30440 8 2015
## 31296 8 2015
## 32152 8 2015
## 33008 8 2015
## 34720 8 2015
## 35576 8 2015
## 36432 8 2015
## 37288 8 2015
## 38144 8 2015
## 39000 8 2015
## 40712 8 2015
#So we see that except on Day 7, which happens to be a Sunday,Store 622 is open on all days.
#Now, let us also check Store 622 data in the training set
subset(training_data,(training_data$Store == 622))
## Store DayOfWeek Sales Customers Open Promo StateHoliday
## 622 622 5 6306 540 1 1 0
## 1737 622 4 5412 406 1 1 0
## 2852 622 3 5326 468 1 1 0
## 3967 622 2 4966 417 1 1 0
## 5082 622 1 5413 517 1 1 0
## 6197 622 7 0 0 0 0 0
## 7312 622 6 2644 257 1 0 0
## 8427 622 5 4183 440 1 0 0
## 9542 622 4 3970 399 1 0 0
## 10657 622 3 3721 376 1 0 0
## 11772 622 2 3529 367 1 0 0
## 12887 622 1 4688 429 1 0 0
## 14002 622 7 0 0 0 0 0
## 15117 622 6 2367 265 1 0 0
## 16232 622 5 5263 462 1 1 0
## 17347 622 4 5615 494 1 1 0
## 18462 622 3 4718 434 1 1 0
## 19577 622 2 5102 439 1 1 0
## 20692 622 1 6349 535 1 1 0
## 21807 622 7 0 0 0 0 0
## 22922 622 6 2511 281 1 0 0
## 24037 622 5 4493 478 1 0 0
## 25152 622 4 4788 477 1 0 0
## 26267 622 3 4042 406 1 0 0
## 27382 622 2 3642 405 1 0 0
## 28497 622 1 4516 453 1 0 0
## 29612 622 7 0 0 0 0 0
## 30727 622 6 3221 319 1 0 0
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#So, we see here in training data too, that except Sunday, in most cases
#Store 622 has been open on all days of week.
#So, it makes sense to impute the NA's for Store 622 by putting 1 under Open variable in place of NA
testing_data$Open[is.na(testing_data$Open)] <- 1
#Now, let us checkback if indeed we have now finished the imputations and removed all NA's
subset(testing_data,(testing_data$Store == 622))
## Id Store DayOfWeek Open Promo StateHoliday SchoolHoliday day
## 480 480 622 4 1 1 0 0 17
## 1336 1336 622 3 1 1 0 0 16
## 2192 2192 622 2 1 1 0 0 15
## 3048 3048 622 1 1 1 0 0 14
## 3904 3904 622 7 0 0 0 0 13
## 4760 4760 622 6 1 0 0 0 12
## 5616 5616 622 5 1 0 0 0 11
## 6472 6472 622 4 1 0 0 0 10
## 7328 7328 622 3 1 0 0 0 9
## 8184 8184 622 2 1 0 0 0 8
## 9040 9040 622 1 1 0 0 0 7
## 9896 9896 622 7 0 0 0 0 6
## 10752 10752 622 6 1 0 0 0 5
## 11608 11608 622 5 1 1 0 0 4
## 12464 12464 622 4 1 1 0 0 3
## 13320 13320 622 3 1 1 0 0 2
## 14176 14176 622 2 1 1 0 0 1
## 15032 15032 622 1 1 1 0 0 31
## 15888 15888 622 7 0 0 0 0 30
## 16744 16744 622 6 1 0 0 0 29
## 17600 17600 622 5 1 0 0 1 28
## 18456 18456 622 4 1 0 0 1 27
## 19312 19312 622 3 1 0 0 1 26
## 20168 20168 622 2 1 0 0 1 25
## 21024 21024 622 1 1 0 0 1 24
## 21880 21880 622 7 0 0 0 0 23
## 22736 22736 622 6 1 0 0 0 22
## 23592 23592 622 5 1 1 0 1 21
## 24448 24448 622 4 1 1 0 1 20
## 25304 25304 622 3 1 1 0 1 19
## 26160 26160 622 2 1 1 0 1 18
## 27016 27016 622 1 1 1 0 1 17
## 27872 27872 622 7 0 0 0 0 16
## 28728 28728 622 6 1 0 0 0 15
## 29584 29584 622 5 1 0 0 1 14
## 30440 30440 622 4 1 0 0 1 13
## 31296 31296 622 3 1 0 0 1 12
## 32152 32152 622 2 1 0 0 1 11
## 33008 33008 622 1 1 0 0 1 10
## 33864 33864 622 7 0 0 0 0 9
## 34720 34720 622 6 1 0 0 0 8
## 35576 35576 622 5 1 1 0 1 7
## 36432 36432 622 4 1 1 0 1 6
## 37288 37288 622 3 1 1 0 1 5
## 38144 38144 622 2 1 1 0 1 4
## 39000 39000 622 1 1 1 0 1 3
## 39856 39856 622 7 0 0 0 0 2
## 40712 40712 622 6 1 0 0 0 1
## month year
## 480 9 2015
## 1336 9 2015
## 2192 9 2015
## 3048 9 2015
## 3904 9 2015
## 4760 9 2015
## 5616 9 2015
## 6472 9 2015
## 7328 9 2015
## 8184 9 2015
## 9040 9 2015
## 9896 9 2015
## 10752 9 2015
## 11608 9 2015
## 12464 9 2015
## 13320 9 2015
## 14176 9 2015
## 15032 8 2015
## 15888 8 2015
## 16744 8 2015
## 17600 8 2015
## 18456 8 2015
## 19312 8 2015
## 20168 8 2015
## 21024 8 2015
## 21880 8 2015
## 22736 8 2015
## 23592 8 2015
## 24448 8 2015
## 25304 8 2015
## 26160 8 2015
## 27016 8 2015
## 27872 8 2015
## 28728 8 2015
## 29584 8 2015
## 30440 8 2015
## 31296 8 2015
## 32152 8 2015
## 33008 8 2015
## 33864 8 2015
## 34720 8 2015
## 35576 8 2015
## 36432 8 2015
## 37288 8 2015
## 38144 8 2015
## 39000 8 2015
## 39856 8 2015
## 40712 8 2015
#Now, we go for merging the data
training_data <- merge(training_data,store_data)
testing_data <- merge(testing_data,store_data)
#After merging the data, let us look at the structure and description of data once again.
str(training_data)
## 'data.frame': 1017209 obs. of 20 variables:
## $ Store : Factor w/ 1115 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ DayOfWeek : Factor w/ 7 levels "1","2","3","4",..: 5 6 5 3 3 7 3 1 5 1 ...
## $ Sales : int 5263 4952 4190 6454 3310 0 3591 4770 3836 3722 ...
## $ Customers : int 555 646 552 695 464 0 453 542 466 480 ...
## $ Open : Factor w/ 2 levels "0","1": 2 2 2 2 2 1 2 2 2 2 ...
## $ Promo : Factor w/ 2 levels "0","1": 2 1 1 2 1 1 1 2 1 1 ...
## $ StateHoliday : Factor w/ 4 levels "0","a","b","c": 1 1 1 1 1 1 1 1 1 1 ...
## $ SchoolHoliday : Factor w/ 2 levels "0","1": 2 1 2 1 1 1 1 1 1 1 ...
## $ day : Factor w/ 31 levels "1","2","3","4",..: 31 12 3 3 13 27 10 23 6 20 ...
## $ month : Factor w/ 12 levels "1","2","3","4",..: 7 1 1 12 11 10 6 9 9 4 ...
## $ year : Factor w/ 3 levels "2013","2014",..: 3 1 2 2 1 1 3 1 1 3 ...
## $ StoreType : Factor w/ 4 levels "a","b","c","d": 3 3 3 3 3 3 3 3 3 3 ...
## $ Assortment : Factor w/ 3 levels "a","b","c": 1 1 1 1 1 1 1 1 1 1 ...
## $ CompetitionDistance : num 1270 1270 1270 1270 1270 1270 1270 1270 1270 1270 ...
## $ CompetitionOpenSinceMonth: int 9 9 9 9 9 9 9 9 9 9 ...
## $ CompetitionOpenSinceYear : int 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 ...
## $ Promo2 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Promo2SinceWeek : int NA NA NA NA NA NA NA NA NA NA ...
## $ Promo2SinceYear : int NA NA NA NA NA NA NA NA NA NA ...
## $ PromoInterval : Factor w/ 4 levels "","Feb,May,Aug,Nov",..: 1 1 1 1 1 1 1 1 1 1 ...
str(testing_data)
## 'data.frame': 41088 obs. of 19 variables:
## $ Store : Factor w/ 856 levels "1","3","7","8",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Id : Factor w/ 41088 levels "1","2","3","4",..: 1 24825 5993 37665 18833 12841 19689 857 15409 38521 ...
## $ DayOfWeek : Factor w/ 7 levels "1","2","3","4",..: 4 3 4 2 3 3 2 3 7 1 ...
## $ Open : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 1 2 ...
## $ Promo : Factor w/ 2 levels "0","1": 2 2 1 2 1 2 1 2 1 2 ...
## $ StateHoliday : Factor w/ 2 levels "0","a": 1 1 1 1 1 1 1 1 1 1 ...
## $ SchoolHoliday : Factor w/ 2 levels "0","1": 1 2 1 2 2 2 2 1 2 2 ...
## $ day : Factor w/ 31 levels "1","2","3","4",..: 17 19 10 4 26 2 25 16 30 3 ...
## $ month : Factor w/ 2 levels "8","9": 2 1 2 1 1 2 1 2 1 1 ...
## $ year : Factor w/ 1 level "2015": 1 1 1 1 1 1 1 1 1 1 ...
## $ StoreType : Factor w/ 4 levels "a","b","c","d": 3 3 3 3 3 3 3 3 3 3 ...
## $ Assortment : Factor w/ 3 levels "a","b","c": 1 1 1 1 1 1 1 1 1 1 ...
## $ CompetitionDistance : num 1270 1270 1270 1270 1270 1270 1270 1270 1270 1270 ...
## $ CompetitionOpenSinceMonth: int 9 9 9 9 9 9 9 9 9 9 ...
## $ CompetitionOpenSinceYear : int 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 ...
## $ Promo2 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Promo2SinceWeek : int NA NA NA NA NA NA NA NA NA NA ...
## $ Promo2SinceYear : int NA NA NA NA NA NA NA NA NA NA ...
## $ PromoInterval : Factor w/ 4 levels "","Feb,May,Aug,Nov",..: 1 1 1 1 1 1 1 1 1 1 ...
dim(training_data)
## [1] 1017209 20
dim(testing_data)
## [1] 41088 19
summary(training_data)
## Store DayOfWeek Sales Customers Open
## 1 : 942 1:144730 Min. : 0 Min. : 0.0 0:172817
## 2 : 942 2:145664 1st Qu.: 3727 1st Qu.: 405.0 1:844392
## 3 : 942 3:145665 Median : 5744 Median : 609.0
## 4 : 942 4:145845 Mean : 5774 Mean : 633.1
## 5 : 942 5:145845 3rd Qu.: 7856 3rd Qu.: 837.0
## 6 : 942 6:144730 Max. :41551 Max. :7388.0
## (Other):1011557 7:144730
## Promo StateHoliday SchoolHoliday day month
## 0:629129 0:986159 0:835488 2 : 33485 3 :103695
## 1:388080 a: 20260 1:181721 3 : 33485 5 :103695
## b: 6690 4 : 33485 1 :103694
## c: 4100 5 : 33485 4 :100350
## 6 : 33485 6 :100350
## 7 : 33485 7 : 98115
## (Other):816299 (Other):407310
## year StoreType Assortment CompetitionDistance
## 2013:406974 a:551627 a:537445 Min. : 20
## 2014:373855 b: 15830 b: 8294 1st Qu.: 710
## 2015:236380 c:136840 c:471470 Median : 2330
## d:312912 Mean : 5430
## 3rd Qu.: 6890
## Max. :75860
## NA's :2642
## CompetitionOpenSinceMonth CompetitionOpenSinceYear Promo2
## Min. : 1.0 Min. :1900 0:508031
## 1st Qu.: 4.0 1st Qu.:2006 1:509178
## Median : 8.0 Median :2010
## Mean : 7.2 Mean :2009
## 3rd Qu.:10.0 3rd Qu.:2013
## Max. :12.0 Max. :2015
## NA's :323348 NA's :323348
## Promo2SinceWeek Promo2SinceYear PromoInterval
## Min. : 1.0 Min. :2009 :508031
## 1st Qu.:13.0 1st Qu.:2011 Feb,May,Aug,Nov :118596
## Median :22.0 Median :2012 Jan,Apr,Jul,Oct :293122
## Mean :23.3 Mean :2012 Mar,Jun,Sept,Dec: 97460
## 3rd Qu.:37.0 3rd Qu.:2013
## Max. :50.0 Max. :2015
## NA's :508031 NA's :508031
summary(testing_data)
## Store Id DayOfWeek Open Promo
## 1 : 48 1 : 1 1:5992 0: 5984 0:24824
## 3 : 48 2 : 1 2:5992 1:35104 1:16264
## 7 : 48 3 : 1 3:5992
## 8 : 48 4 : 1 4:5992
## 9 : 48 5 : 1 5:5136
## 10 : 48 6 : 1 6:5992
## (Other):40800 (Other):41082 7:5992
## StateHoliday SchoolHoliday day month year
## 0:40908 0:22866 1 : 1712 8:26536 2015:41088
## a: 180 1:18222 2 : 1712 9:14552
## 3 : 1712
## 4 : 1712
## 5 : 1712
## 6 : 1712
## (Other):30816
## StoreType Assortment CompetitionDistance CompetitionOpenSinceMonth
## a:22128 a:20304 Min. : 20 Min. : 1.000
## b: 576 b: 432 1st Qu.: 720 1st Qu.: 4.000
## c: 4272 c:20352 Median : 2425 Median : 7.000
## d:14112 Mean : 5089 Mean : 7.035
## 3rd Qu.: 6480 3rd Qu.: 9.000
## Max. :75860 Max. :12.000
## NA's :96 NA's :15216
## CompetitionOpenSinceYear Promo2 Promo2SinceWeek Promo2SinceYear
## Min. :1900 0:17232 Min. : 1.00 Min. :2009
## 1st Qu.:2006 1:23856 1st Qu.:13.00 1st Qu.:2011
## Median :2010 Median :22.00 Median :2012
## Mean :2009 Mean :24.43 Mean :2012
## 3rd Qu.:2012 3rd Qu.:37.00 3rd Qu.:2013
## Max. :2015 Max. :49.00 Max. :2015
## NA's :15216 NA's :17232 NA's :17232
## PromoInterval
## :17232
## Feb,May,Aug,Nov : 5712
## Jan,Apr,Jul,Oct :13776
## Mar,Jun,Sept,Dec: 4368
##
##
##
str(testing_data)
## 'data.frame': 41088 obs. of 19 variables:
## $ Store : Factor w/ 856 levels "1","3","7","8",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Id : Factor w/ 41088 levels "1","2","3","4",..: 1 24825 5993 37665 18833 12841 19689 857 15409 38521 ...
## $ DayOfWeek : Factor w/ 7 levels "1","2","3","4",..: 4 3 4 2 3 3 2 3 7 1 ...
## $ Open : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 1 2 ...
## $ Promo : Factor w/ 2 levels "0","1": 2 2 1 2 1 2 1 2 1 2 ...
## $ StateHoliday : Factor w/ 2 levels "0","a": 1 1 1 1 1 1 1 1 1 1 ...
## $ SchoolHoliday : Factor w/ 2 levels "0","1": 1 2 1 2 2 2 2 1 2 2 ...
## $ day : Factor w/ 31 levels "1","2","3","4",..: 17 19 10 4 26 2 25 16 30 3 ...
## $ month : Factor w/ 2 levels "8","9": 2 1 2 1 1 2 1 2 1 1 ...
## $ year : Factor w/ 1 level "2015": 1 1 1 1 1 1 1 1 1 1 ...
## $ StoreType : Factor w/ 4 levels "a","b","c","d": 3 3 3 3 3 3 3 3 3 3 ...
## $ Assortment : Factor w/ 3 levels "a","b","c": 1 1 1 1 1 1 1 1 1 1 ...
## $ CompetitionDistance : num 1270 1270 1270 1270 1270 1270 1270 1270 1270 1270 ...
## $ CompetitionOpenSinceMonth: int 9 9 9 9 9 9 9 9 9 9 ...
## $ CompetitionOpenSinceYear : int 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 ...
## $ Promo2 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Promo2SinceWeek : int NA NA NA NA NA NA NA NA NA NA ...
## $ Promo2SinceYear : int NA NA NA NA NA NA NA NA NA NA ...
## $ PromoInterval : Factor w/ 4 levels "","Feb,May,Aug,Nov",..: 1 1 1 1 1 1 1 1 1 1 ...
#So, let us start the investigation
#checking out sale statistics for each store on training data
summarise(group_by(training_data,Store),average_sales = mean(Sales),max_sale = max(Sales), stdev_sales=sd(Sales))
## Source: local data frame [1,115 x 4]
##
## Store average_sales max_sale stdev_sales
## (fctr) (dbl) (int) (dbl)
## 1 1 3945.705 9528 2015.418
## 2 2 4122.992 10682 2363.629
## 3 3 5741.254 15689 3298.783
## 4 4 8021.770 17412 4012.593
## 5 5 3867.110 11692 2389.610
## 6 6 4562.376 11139 2527.359
## 7 7 7356.902 18413 3969.300
## 8 8 4610.252 10971 2696.884
## 9 9 5426.816 13457 2891.688
## 10 10 4634.439 9534 2291.821
## .. ... ... ... ...
#checking out monthly and yearly sale statistics for each store on training data
summarise(group_by(training_data,Store,month),average_sales = mean(Sales),max_sale = max(Sales), stdev_sales=sd(Sales) )
## Source: local data frame [13,380 x 5]
## Groups: Store [?]
##
## Store month average_sales max_sale stdev_sales
## (fctr) (fctr) (dbl) (int) (dbl)
## 1 1 1 3944.591 7176 1873.464
## 2 1 2 4075.476 7032 1831.662
## 3 1 3 4091.968 7675 2128.564
## 4 1 4 3821.733 6816 2066.676
## 5 1 5 3668.387 7893 2180.549
## 6 1 6 3681.411 6362 1929.875
## 7 1 7 3998.946 6377 1697.148
## 8 1 8 3698.774 5655 1674.316
## 9 1 9 3587.750 5893 1642.720
## 10 1 10 3702.226 6004 1762.022
## .. ... ... ... ... ...
summarise(group_by(training_data,Store,year),average_sales = mean(Sales),max_sale = max(Sales), stdev_sales=sd(Sales) )
## Source: local data frame [3,345 x 5]
## Groups: Store [?]
##
## Store year average_sales max_sale stdev_sales
## (fctr) (fctr) (dbl) (int) (dbl)
## 1 1 2013 4085.315 9528 2079.557
## 2 1 2014 3927.145 9331 2022.837
## 3 1 2015 3737.292 6816 1875.546
## 4 2 2013 4077.162 10479 2356.318
## 5 2 2014 4154.608 10682 2386.487
## 6 2 2015 4147.462 10107 2346.475
## 7 3 2013 5830.863 14647 3378.399
## 8 3 2014 5679.312 15689 3299.065
## 9 3 2015 5693.618 13261 3168.912
## 10 4 2013 7815.526 17412 3948.567
## .. ... ... ... ... ...
#checking out state holiday sales statistics for each store on training data
summarise(group_by(training_data,Store,StateHoliday),average_sales = mean(Sales),max_sale = max(Sales), stdev_sales=sd(Sales) )
## Source: local data frame [4,460 x 5]
## Groups: Store [?]
##
## Store StateHoliday average_sales max_sale stdev_sales
## (fctr) (fctr) (dbl) (int) (dbl)
## 1 1 0 4062.1355 9528 1925.727
## 2 1 a 0.0000 0 0.000
## 3 1 b 0.0000 0 0.000
## 4 1 c 0.0000 0 0.000
## 5 2 0 4229.5671 10682 2301.395
## 6 2 a 356.3333 2689 940.382
## 7 2 b 0.0000 0 0.000
## 8 2 c 0.0000 0 0.000
## 9 3 0 5923.6156 15689 3185.368
## 10 3 a 0.0000 0 0.000
## .. ... ... ... ... ...
#to arrive at a conclusion, we will have to dig a little deeper, so we check out
#number of state holidays of each type for each store in training data
table(training_data$Store,training_data$StateHoliday)
##
## 0 a b c
## 1 915 17 6 4
## 2 917 15 6 4
## 3 913 19 6 4
## 4 918 14 6 4
## 5 911 21 6 4
## 6 911 21 6 4
## 7 918 14 6 4
## 8 918 14 6 4
## 9 913 19 6 4
## 10 918 14 6 4
## 11 918 14 6 4
## 12 918 14 6 4
## 13 729 21 6 2
## 14 913 19 6 4
## 15 918 14 6 4
## 16 910 22 6 4
## 17 917 15 6 4
## 18 917 15 6 4
## 19 913 19 6 4
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## 21 910 22 6 4
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#Remember, our observation 1, that we saw in the beginning in the test set
#Hence, we attach importance on sales statistics corresponding to state holiday
#level of "a"
a_holiday_sales <- summarise(group_by(select(filter(training_data,StateHoliday == "a"),Store,Sales),Store),max_sale = max(Sales))
#So, we can see a lot of stores have zero sales on "a" state holidays.
#finding out stores for which max sale is zero on "a" state holidays.
a_zero_sale_stores <- filter(a_holiday_sales,a_holiday_sales$max_sale == 0)
dim(a_zero_sale_stores)
## [1] 959 2
#so in testing data, for above stores on "a" state holidays, we predict zero sales
#so, first we find all stores in testing data for which "a" state holidays are applicable
a_test_stores <- select(filter(testing_data,testing_data$StateHoliday == "a"),Store)
dim(a_test_stores)
## [1] 180 1
#find matching stores for zero sales prediction
#checking out school holiday sales statistics for each store on training data
summarise(group_by(training_data,Store,SchoolHoliday),average_sales = mean(Sales),max_sale = max(Sales), stdev_sales=sd(Sales) )
## Source: local data frame [2,230 x 5]
## Groups: Store [?]
##
## Store SchoolHoliday average_sales max_sale stdev_sales
## (fctr) (fctr) (dbl) (int) (dbl)
## 1 1 0 3937.840 8414 1993.628
## 2 1 1 3976.228 9528 2102.907
## 3 2 0 3960.201 10682 2398.269
## 4 2 1 4878.455 10107 2037.989
## 5 3 0 5512.861 15689 3352.835
## 6 3 1 6778.424 14461 2825.903
## 7 4 0 7732.978 16843 4057.570
## 8 4 1 9305.474 17412 3541.671
## 9 5 0 3726.345 11692 2451.393
## 10 5 1 4497.279 10877 1977.354
## .. ... ... ... ... ...
#As expected that people or better, school folks would flock to stores for shopping,
#we can see increased sales on school holidays.
#checking which store is open on which days
table(training_data$Store,training_data$Open)
##
## 0 1
## 1 161 781
## 2 158 784
## 3 163 779
## 4 158 784
## 5 163 779
## 6 162 780
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base::prop.table(base::table(training_data$Store,training_data$Open),1)*100
##
## 0 1
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#this means most of the stores are closed for almost 16-17% during the operational
#period where as they are open for about 82-83% during the operational period
#checking sales statistics on open/closed days for each store on training data
summarise(group_by(training_data,Store,Open),average_sales = mean(Sales),max_sale = max(Sales), stdev_sales=sd(Sales) )
## Source: local data frame [2,220 x 5]
## Groups: Store [?]
##
## Store Open average_sales max_sale stdev_sales
## (fctr) (fctr) (dbl) (int) (dbl)
## 1 1 0 0.000 0 0.000
## 2 1 1 4759.096 9528 1012.106
## 3 2 0 0.000 0 0.000
## 4 2 1 4953.901 10682 1610.149
## 5 3 0 0.000 0 0.000
## 6 3 1 6942.569 15689 2193.384
## 7 4 0 0.000 0 0.000
## 8 4 1 9638.402 17412 1936.032
## 9 5 0 0.000 0 0.000
## 10 5 1 4676.275 11692 1765.746
## .. ... ... ... ... ...
#We can see that on closed days, for each of the stores in the training data the sale recorded was zero.
#So, to begin with predictive thinking, we can safely predict a zero sale for all closed days.
testing_data$Sales[testing_data$Open == 0] <- 0
#checking sales statistics on promo days for each store in training data
summarise(group_by(training_data,Store,Promo),average_sales = mean(Sales),max_sale = max(Sales), stdev_sales=sd(Sales) )
## Source: local data frame [2,230 x 5]
## Groups: Store [?]
##
## Store Promo average_sales max_sale stdev_sales
## (fctr) (fctr) (dbl) (int) (dbl)
## 1 1 0 3198.995 9528 2052.472
## 2 1 1 5152.886 8414 1209.963
## 3 2 0 2855.058 9027 1893.487
## 4 2 1 6172.817 10682 1421.845
## 5 3 0 3967.596 14461 2600.061
## 6 3 1 8608.667 15689 2058.582
## 7 4 0 6568.940 17412 4171.800
## 8 4 1 10370.511 17311 2254.594
## 9 5 0 2582.271 10877 1864.566
## 10 5 1 5944.267 11692 1529.107
## .. ... ... ... ... ...
#As expected, sales figures on promo days are higher than on days when promotions don't run