Walmart sales forecast

1. 1
(Praxis Business School)
Data Mining Assignment
A report on
Sales forecasting for Walmart
Submitted to
Prof. Suman K Mazumdar
In partial fulfillment of the requirements of the subject
(iSAS)
On (26th
September, 2015)
By
Anurag Mukherjee

2. 2
Sales forecasting for Walmart

3. 3
Table of Content
Sl
No Topic Page
1 Cover Page 1
2 Title Page 2
3 Executive Summary 3
4 Background 3
5 Business Problem 3
6 Data Overview 4
7 Exploratory Analysis 5
8 Examining the final features dataset : 19
9
Merging of train and features for the final data set
creation 20
10 Model Building 23

4. 4
Executive Summary :
Walmart is the world'slargestcompanybyrevenue, according to the Fortune Global 500 list in
2014, as well as the biggestprivate employerin the world with 2.2 million employees.
Walmart is a family-owned business, as the company is controlled by the Waltonfamily. Sam
Walton's heirs own over 50 percent of Walmart through their holding company, Walton
Enterprises, and through their individual holdings. It is also one of the world'smostvaluable
companiesbymarketvalue,[10]and is also the largestgrocery retailer in the U.S. In 2009, it
generated 51 percent of its US$258 billion (equivalent to $284 billion in 2015) sales in the
U.S. from its grocery business.
We are provided with datasets containing sales per store,per department on weekly
basis.We are are about to forecast sales for Walmart to help the company in taking much
better data driven decisions for inventory planning and channel optimization.
Background:
Wal-Mart Stores,Inc.isan Americanmultinational retailcorporation thatoperatesachain
ofdiscountdepartmentstores andwarehousestores.Headquarteredin Bentonville,
Arkansas,UnitedStates,the companywasfoundedby SamWaltonin1962 and incorporated on
October31, 1969. It hasover11,000 storesin28 countries,underatotal of 65 banners.The
companyoperatesunderthe Walmartname inthe UnitedStatesandCanada.It operatesasWalmart
de Méxicoy CentroaméricainMexico,as Asdainthe UnitedKingdom, as SeiyuinJapan,andas Best
Price inIndia.It has whollyownedoperationsinArgentina,Brazil,andCanada.Italsoownsand
operatesthe Sam'sClubretail warehouses.
Business Problem:
Withhistorical salesdatafor45 Walmartstoreslocatedindifferentregions.Eachstore contains
manydepartments,andthe aimisto projectthe salesfor eachdepartmentineachstore.To add to
the challenge,selectedholidaymarkdowneventsare includedinthe dataset.These markdownsare
knownto affectsales.
Data Overview :

5. 5
train.csv
Thisis the historical trainingdata,whichcoversto2010-02-05 to 2012-11-01. Withinthisfile youwill
findthe followingfields:
 Store - the store number
 Dept- the departmentnumber
 Date - the week
 Weekly_Sales - salesforthe givendepartmentinthe givenstore
 IsHoliday - whetherthe weekisaspecial holidayweek
features.csv
Thisfile containsadditional datarelatedtothe store,department,andregional activityforthe given
dates.It containsthe followingfields:
 Store - the store number
 Date - the week
 Temperature - average temperature inthe region
 Fuel_Price - costof fuel inthe region
 MarkDown1-5 - anonymizeddatarelatedtopromotionalmarkdownsthatWalmartisrunning.
MarkDown data isonlyavailable afterNov2011, and isnot available forall storesall the time.Any
missingvalue ismarkedwithanNA.
 CPI - the consumerprice index
 Unemployment- the unemploymentrate
 IsHoliday - whetherthe weekisaspecial holidayweek

6. 6
Exploratory Analysis :
1.train.csv
1.1 Importing the raw dataset :
proc importout=walmart_traindatafile='/folders/myshortcuts/myfolder/train_walmart.csv'
dbms=csvreplace;
getnames=yes;
run;
1.2 Checkingthe contentsof train.csv :
proc contents data=walmart_train;
run;
Alphabetic List of Variables and Attributes
# Variable Type Len Format Informat
3 Date Num 8 DDMMYY10. DDMMYY10.
2 Dept Num 8 BEST12. BEST32.
6 IsHoliday Char 5 $5. $5.
4 Month_Year Num 8 DATETIME. ANYDTDTM40.
1 Store Num 8 BEST12. BEST32.
5 Weekly_Sale
s
Num 8 BEST12. BEST32.
1.3 Checkingthe basic statistical measures

7. 7
proc meansdata=walmart_train;
var Weekly_Sales;
run;
Analysis Variable : Weekly_Sales
N Mean Std Dev Minimum Maximum
42157
0
15981.2
6
22711.1
8
-4988.94 693099.36
Negative SalesIndicate Returns.
1.4 Plot of Weekly_SalesVsDate :

8. 8
1.5 PlottingSales YearWise :
proc sql;
create table walmart_train_dataas
selectDate,sum(Weekly_Sales)asSales
fromwalmart_train
groupby Date;
run;
2010 Sales Report :
data Sales_2010;
setwalmart_train_data(keep=SalesDate where=(Datebetween'05Feb2010'd and '31Dec2010'd));
run;
*plotting2010 SalesbyDate;
ods graphics/ resetimagemap;
proc sgplotdata=WORK.SALES_2010;
vbarDate / response=Salesstat=Meanname='Bar';
yaxisgrid;
run;
ods graphics/ reset;
proc printdata=Sales_2010;
run;

9. 9
Obs Date Sales
1 05/02/2010 49750740.5
0
2 12/02/2010 48336677.6
3
3 19/02/2010 48276993.7
8
4 26/02/2010 43968571.1
3
5 05/03/2010 46871470.3
0
6 12/03/2010 45925396.5
1
7 19/03/2010 44988974.6
4
8 26/03/2010 44133961.0
5
(First 8 Sales figuresfor 2010 for convenience)
0
20000000
40000000
60000000
80000000Sales(Mean)
05/02/2010
12/02/2010
19/02/2010
26/02/2010
05/03/2010
12/03/2010
19/03/2010
26/03/2010
02/04/2010
09/04/2010
16/04/2010
23/04/2010
30/04/2010
07/05/2010
14/05/2010
21/05/2010
28/05/2010
04/06/2010
11/06/2010
18/06/2010
25/06/2010
02/07/2010
09/07/2010
16/07/2010
23/07/2010
30/07/2010
06/08/2010
13/08/2010
20/08/2010
27/08/2010
03/09/2010
10/09/2010
17/09/2010
24/09/2010
01/10/2010
08/10/2010
15/10/2010
22/10/2010
29/10/2010
05/11/2010
12/11/2010
19/11/2010
26/11/2010
03/12/2010
10/12/2010
17/12/2010
24/12/2010
31/12/2010
Date

10. 10
2011 Sales Report :
data Sales_2011;
setwalmart_train_data(keep=SalesDate where=(Datebetween'07Jan2011'd and '30Dec2011'd));
run;
*plotting2011 SalesbyDate;
ods graphics/ resetimagemap;
proc sgplotdata=WORK.SALES_2011;
vbarDate / response=Salesstat=Meanname='Bar';
yaxisgrid;
run;
ods graphics/ reset;
proc printdata=Sales_2011;
run;
0
20000000
40000000
60000000
80000000
Sales(Mean)
07/01/2011
14/01/2011
21/01/2011
28/01/2011
04/02/2011
11/02/2011
18/02/2011
25/02/2011
04/03/2011
11/03/2011
18/03/2011
25/03/2011
01/04/2011
08/04/2011
15/04/2011
22/04/2011
29/04/2011
06/05/2011
13/05/2011
20/05/2011
27/05/2011
03/06/2011
10/06/2011
17/06/2011
24/06/2011
01/07/2011
08/07/2011
15/07/2011
22/07/2011
29/07/2011
05/08/2011
12/08/2011
19/08/2011
26/08/2011
02/09/2011
09/09/2011
16/09/2011
23/09/2011
30/09/2011
07/10/2011
14/10/2011
21/10/2011
28/10/2011
04/11/2011
11/11/2011
18/11/2011
25/11/2011
02/12/2011
09/12/2011
16/12/2011
23/12/2011
30/12/2011
Date

11. 11
Sales in tabular Form :
Obs Date Sales
1 07/01/20
11
42775787.7
7
2 14/01/20
11
40673678.0
4
3 21/01/20
11
40654648.0
3
4 28/01/20
11
39599852.9
9
5 04/02/20
11
46153111.1
2
6 11/02/20
11
47336192.7
9
7 18/02/20
11
48716164.1
2
(First8 Salesfiguresfor 2011 for convenience)

12. 12
2012 Sales Report :
data Sales_2012;
setwalmart_train_data(keep=SalesDate where=(Datebetween'06Jan2012'd and '26Oct2012'd));
run;
*plotting2012 SalesbyDate;
ods graphics/ resetimagemap;
proc sgplotdata=WORK.SALES_2012;
vbarDate / response=Salesstat=Meanname='Bar';
yaxisgrid;
run;
ods graphics/ reset;
proc printdata=Sales_2012; run;
;
0
10000000
20000000
30000000
40000000
50000000
Sales(Mean)
06/01/2012
13/01/2012
20/01/2012
27/01/2012
03/02/2012
10/02/2012
17/02/2012
24/02/2012
02/03/2012
09/03/2012
16/03/2012
23/03/2012
30/03/2012
06/04/2012
13/04/2012
20/04/2012
27/04/2012
04/05/2012
11/05/2012
18/05/2012
25/05/2012
01/06/2012
08/06/2012
15/06/2012
22/06/2012
29/06/2012
06/07/2012
13/07/2012
20/07/2012
27/07/2012
03/08/2012
10/08/2012
17/08/2012
24/08/2012
31/08/2012
07/09/2012
14/09/2012
21/09/2012
28/09/2012
05/10/2012
12/10/2012
19/10/2012
26/10/2012
Date

13. 13
Sales intabular form - 2012
Obs Date Sales
1 06/01/20
12
44955421.9
5
2 13/01/20
12
42023078.4
8
3 20/01/20
12
42080996.5
6
4 27/01/20
12
39834974.6
7
5 03/02/20
12
46085608.0
9
6 10/02/20
12
50009407.9
2
7 17/02/20
12
50197056.9
6
8 24/02/20
12
45771506.5
7

14. 14
1.6.Outlier Treatment for train.csv :
The data being a time series record have some seasonalities .During the month of December
there’s a sales spike.This can be explained further by Markdowns.
Markdown 1,2,4,5 doesnt seem to be that effective as compared to Markdown 3.

15. 15

16. 16
As the spike in the sales would affect the entire model,the difference of excess sales has
been distributed across all the records.
data wal;
set walmart_train_data;
where Sales > 50000000;
sales_diff=Sales-46243899.58;
run;
proc sql;
create table mapper as
select sum(Sales_diff) from
wal;
run;
*total excess sales from weeks having > 50000000 = 181638262.18;
data walmart_final;
set walmart_train;
if Weekly_Sales > 50000000 then Weekly_Sales=46243899.58;
Weekly_Sales_new=Weekly_Sales+(181638262.18/421570);
run;
proc univariate data=walmart_final;
var Weekly_Sales;
run;

17. 17
2.features.csv
2.1 Importing raw data set :
proc import out=walmart_features datafile='/folders/myshortcuts/myfolder/features.csv'
dbms=csv replace;
getnames=yes;
guessingrows=200;
run;
2.2 Checking the contents of features.csv :
Alphabetic List of Variables and Attributes
# Variable Type Len Format Informat
4 CPI Char 11 $11. $11.
2 Date Num 8 YYMMDD10. YYMMDD10.
6 Fuel_Price Num 8 BEST12. BEST32.
13 IsHoliday Char 5 $5. $5.
7 MarkDown1 Char 8 $8. $8.
8 MarkDown2 Char 8 $8. $8.
9 MarkDown3 Char 8 $8. $8.
10 MarkDown4 Char 8 $8. $8.
11 MarkDown5 Char 8 $8. $8.
1 Store Num 8 BEST12. BEST32.
5 Temperature Num 8 BEST12. BEST32.
12 Unemployme
nt
Char 5 $5. $5.
14 VAR14 Char 1 $1. $1.
3 Weekly_Sales Char 8 $8. $8.

18. 18
2.3 Checking the basic statistical measures of features.csv :
proc means data=walmart_features;
run;
2.4 OutlierTreatment :
data walmart_f;
setwalmart_features;
formatDate DDMMYY10.;
if MarkDown1="NA"or MarkDown1="#N/A" thenMarkDown1=0;
if MarkDown2="NA"or MarkDown2="#N/A" thenMarkDown2=0;
if MarkDown3="NA"or MarkDown3="#N/A" thenMarkDown3=0;
if MarkDown4="NA"or MarkDown4="#N/A" thenMarkDown4=0;
if MarkDown5="NA"or MarkDown5="#N/A" thenMarkDown5=0;
if IsHoliday="TRUE"thenIsHoliday_Yes=1;
else IsHoliday_Yes=0;
if Weekly_Sales="#N/A"thenWeekly_Sales=0;
run;

19. 19
data walmart_features_1(keep=StoreDate Weekly_Sales_nFuel_Price IsHoliday_YesMarkDown1_n
MarkDown1_n MarkDown2_n MarkDown3_n MarkDown4_n MarkDown5_n Temperature
UnemploymentCPI) ;
setwalmart_f;
MarkDown1_n=MarkDown1*1;
MarkDown2_n=MarkDown2*1;
MarkDown3_n=MarkDown3*1;
MarkDown4_n=MarkDown4*1;
MarkDown5_n=MarkDown5*1;
Weekly_Sales_n=Weekly_Sales*1;
run;

20. 20
Examining the final features dataset :
proc contentsdata=walmart_features_1;
run;
Alphabetic List of Variables and Attributes
# Variable Type Len Format Informat
3 CPI Char 11 $11. $11.
2 Date Num 8 DDMMYY10. YYMMDD10.
5 Fuel_Price Num 8 BEST12. BEST32.
7 IsHoliday_Yes Num 8
8 MarkDown1_n Num 8
9 MarkDown2_n Num 8
10 MarkDown3_n Num 8
11 MarkDown4_n Num 8
12 MarkDown5_n Num 8
1 Store Num 8 BEST12. BEST32.
4 Temperature Num 8 BEST12. BEST32.
6 Unemployment Char 5 $5. $5.
13 Weekly_Sales_n Num 8

21. 21
Merging of trainand features for the final data set creation:
proc sql;
create table walmart_final_1 as
select
a.*,b.CPI,b.Temperature,b.Fuel_Price,b.MarkDown1_n,b.MarkDown2_n,b.MarkDown3_n,b.
MarkDown4_n,b.MarkDown5_n,b.Unemployment,b.IsHoliday_Yes
from walmart_final as a left join walmart_features_1 as b on
a.Date=b.Date and a.Store=b.Store;
run;
data walmart_final_2 (drop=IsHoliday Month_Year Unemployment Weekly_Sales);
set walmart_final_1;
run;

22. 22
proc contents data=walmart_final_2;
run;
Alphabetic List of Variables and Attributes
# Variable Type Len Format Informat
5 CPI Char 11 $11. $11.
3 Date Num 8 DDMMYY10. DDMMYY10.
2 Dept Num 8 BEST12. BEST32.
7 Fuel_Price Num 8 BEST12. BEST32.
13 IsHoliday_Yes Num 8
8 MarkDown1_n Num 8
9 MarkDown2_n Num 8
10 MarkDown3_n Num 8
11 MarkDown4_n Num 8
12 MarkDown5_n Num 8
1 Store Num 8 BEST12. BEST32.
6 Temperature Num 8 BEST12. BEST32.
4 Weekly_Sales_new Num 8

23. 23
Printing the final dataset after merge :
proc print data=walmart_final_2(obs=10);
run;
O
b
s
St
or
e
D
e
pt
Date Weekly_
Sales_ne
w
CPI Temp
eratur
e
Fuel
_Pric
e
MarkD
own1_
n
MarkD
own2_
n
MarkD
own3_
n
MarkD
own4_
n
MarkD
own5_
n
IsHoli
day_Y
es
1 1 4
5
05/0
2/20
10
468.30 211.0
96358
2
42.31 2.57
2
0 0 0 0 0 0
2 1 5 05/0
2/20
10
32660.24 211.0
96358
2
42.31 2.57
2
0 0 0 0 0 0
3 1 9 05/0
2/20
10
17361.85 211.0
96358
2
42.31 2.57
2
0 0 0 0 0 0
4 1 2
9
05/0
2/20
10
7455.81 211.0
96358
2
42.31 2.57
2
0 0 0 0 0 0
5 1 9
2
05/0
2/20
10
140315.8
0
211.0
96358
2
42.31 2.57
2
0 0 0 0 0 0
6 1 4
2
05/0
2/20
10
8797.57 211.0
96358
2
42.31 2.57
2
0 0 0 0 0 0
7 1 8
0
05/0
2/20
10
16125.03 211.0
96358
2
42.31 2.57
2
0 0 0 0 0 0
8 1 1
9
05/0
2/20
10
2377.91 211.0
96358
2
42.31 2.57
2
0 0 0 0 0 0
9 1 3
2
05/0
2/20
10
12306.70 211.0
96358
2
42.31 2.57
2
0 0 0 0 0 0
1
0
1 4
0
05/0
2/20
10
67211.49 211.0
96358
2
42.31 2.57
2
0 0 0 0 0 0

24. 24
Model Building :
proc reg data=walmart_final_2;
model Weekly_Sales_new= Fuel_Price MarkDown3_n Temperature ;
run;