One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm.
An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.
Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.
Basic Statistics tasks • Which store has maximum sales • Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation • Which store/s has good quarterly growth rate in Q3’2012 • Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together • Provide a monthly and semester view of sales in units and give insights
For Store 1 – Build prediction models to forecast demand • Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales. • Change dates into days by creating new variable. Select the model which gives best accuracy
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## some1.This is the historical data which covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields: 2.Store - the store number. 3.Date - the week of sales. 4.Weekly_Sales - sales for the given store. 5.Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week. 6.Temperature - Temperature on the day of sale. 7.Fuel_Weekly_Sales - Cost of fuel in the region. 8.CPI – Prevailing consumer Weekly_Sales index. 9.Unemployment - Prevailing unemployment rate.
getwd()## [1] "F:/cli/Data_science_portfolios/simplilearn_data_scientist/Data Science with R Programming/projects"setwd("F:/cli/Data_science_portfolios/R_projects/")walmart_sales<- read.csv('F:/cli/Data_science_portfolios/R_projects/Walmart_Store_sales.csv',header=TRUE,",")#View(walmart_sales)
head(walmart_sales)## Store Date Weekly_Sales Holiday_Flag Temperature Fuel_Price CPI
## 1 1 05-02-2010 1643691 0 42.31 2.572 211.0964
## 2 1 12-02-2010 1641957 1 38.51 2.548 211.2422
## 3 1 19-02-2010 1611968 0 39.93 2.514 211.2891
## 4 1 26-02-2010 1409728 0 46.63 2.561 211.3196
## 5 1 05-03-2010 1554807 0 46.50 2.625 211.3501
## 6 1 12-03-2010 1439542 0 57.79 2.667 211.3806
## Unemployment
## 1 8.106
## 2 8.106
## 3 8.106
## 4 8.106
## 5 8.106
## 6 8.106#View(walmart_sales)
str(walmart_sales)## 'data.frame': 6435 obs. of 8 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : chr "05-02-2010" "12-02-2010" "19-02-2010" "26-02-2010" ...
## $ Weekly_Sales: num 1643691 1641957 1611968 1409728 1554807 ...
## $ Holiday_Flag: int 0 1 0 0 0 0 0 0 0 0 ...
## $ Temperature : num 42.3 38.5 39.9 46.6 46.5 ...
## $ Fuel_Price : num 2.57 2.55 2.51 2.56 2.62 ...
## $ CPI : num 211 211 211 211 211 ...
## $ Unemployment: num 8.11 8.11 8.11 8.11 8.11 ...The Data frame has 6435 observations of 8 Variables.
aggregate_sales <- aggregate(Weekly_Sales~ Store, data = walmart_sales, FUN = sum, na.rm = TRUE)
aggregate_sales## Store Weekly_Sales
## 1 1 222402809
## 2 2 275382441
## 3 3 57586735
## 4 4 299543953
## 5 5 45475689
## 6 6 223756131
## 7 7 81598275
## 8 8 129951181
## 9 9 77789219
## 10 10 271617714
## 11 11 193962787
## 12 12 144287230
## 13 13 286517704
## 14 14 288999911
## 15 15 89133684
## 16 16 74252425
## 17 17 127782139
## 18 18 155114734
## 19 19 206634862
## 20 20 301397792
## 21 21 108117879
## 22 22 147075649
## 23 23 198750618
## 24 24 194016021
## 25 25 101061179
## 26 26 143416394
## 27 27 253855917
## 28 28 189263681
## 29 29 77141554
## 30 30 62716885
## 31 31 199613906
## 32 32 166819246
## 33 33 37160222
## 34 34 138249763
## 35 35 131520672
## 36 36 53412215
## 37 37 74202740
## 38 38 55159626
## 39 39 207445542
## 40 40 137870310
## 41 41 181341935
## 42 42 79565752
## 43 43 90565435
## 44 44 43293088
## 45 45 112395341which.max(aggregate_sales$Weekly_Sales)## [1] 20print(paste(" Store number:" , which.max(aggregate_sales$Weekly_Sales),' has maximum sales of ' , max(aggregate_sales$Weekly_Sales)))## [1] " Store number: 20 has maximum sales of 301397792.46"aggregate_sales_sd <- aggregate(Weekly_Sales~ Store, data = walmart_sales, FUN = sd , na.rm = TRUE)
aggregate_sales_sd## Store Weekly_Sales
## 1 1 155980.77
## 2 2 237683.69
## 3 3 46319.63
## 4 4 266201.44
## 5 5 37737.97
## 6 6 212525.86
## 7 7 112585.47
## 8 8 106280.83
## 9 9 69028.67
## 10 10 302262.06
## 11 11 165833.89
## 12 12 139166.87
## 13 13 265507.00
## 14 14 317569.95
## 15 15 120538.65
## 16 16 85769.68
## 17 17 112162.94
## 18 18 176641.51
## 19 19 191722.64
## 20 20 275900.56
## 21 21 128752.81
## 22 22 161251.35
## 23 23 249788.04
## 24 24 167745.68
## 25 25 112976.79
## 26 26 110431.29
## 27 27 239930.14
## 28 28 181758.97
## 29 29 99120.14
## 30 30 22809.67
## 31 31 125855.94
## 32 32 138017.25
## 33 33 24132.93
## 34 34 104630.16
## 35 35 211243.46
## 36 36 60725.17
## 37 37 21837.46
## 38 38 42768.17
## 39 39 217466.45
## 40 40 119002.11
## 41 41 187907.16
## 42 42 50262.93
## 43 43 40598.41
## 44 44 24762.83
## 45 45 130168.53which.max(aggregate_sales_sd$Weekly_Sales)## [1] 14print(paste(" Store number:" , which.max(aggregate_sales_sd$Weekly_Sales),' has maximum Standard deviation of ' , max(aggregate_sales_sd$Weekly_Sales)))## [1] " Store number: 14 has maximum Standard deviation of 317569.949475508"aggregate_sales_mean <- aggregate(Weekly_Sales~ Store, data = walmart_sales, FUN = mean , na.rm = TRUE)
aggregate_sales_mean## Store Weekly_Sales
## 1 1 1555264.4
## 2 2 1925751.3
## 3 3 402704.4
## 4 4 2094713.0
## 5 5 318011.8
## 6 6 1564728.2
## 7 7 570617.3
## 8 8 908749.5
## 9 9 543980.6
## 10 10 1899424.6
## 11 11 1356383.1
## 12 12 1009001.6
## 13 13 2003620.3
## 14 14 2020978.4
## 15 15 623312.5
## 16 16 519247.7
## 17 17 893581.4
## 18 18 1084718.4
## 19 19 1444999.0
## 20 20 2107676.9
## 21 21 756069.1
## 22 22 1028501.0
## 23 23 1389864.5
## 24 24 1356755.4
## 25 25 706721.5
## 26 26 1002911.8
## 27 27 1775216.2
## 28 28 1323522.2
## 29 29 539451.4
## 30 30 438579.6
## 31 31 1395901.4
## 32 32 1166568.2
## 33 33 259861.7
## 34 34 966781.6
## 35 35 919725.0
## 36 36 373512.0
## 37 37 518900.3
## 38 38 385731.7
## 39 39 1450668.1
## 40 40 964128.0
## 41 41 1268125.4
## 42 42 556403.9
## 43 43 633324.7
## 44 44 302748.9
## 45 45 785981.4max(aggregate_sales_mean$Weekly_Sales)## [1] 2107677Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.