Walmart Sales Analysis
Trishita Paul & Harshita Gautam
2026-05-03
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This project applies Basic R Programming concepts to Walmart sales data for statistical, business, and predictive analysis. It includes data cleaning, outlier removal, descriptive statistics, visualization, correlation, and regression to understand sales patterns and business performance. The analysis helps transform raw retail data into meaningful insights using practical R functions.
2 Data Cleaning and Preprocessing
2.1 Question 1: How do we clean and preprocess the Walmart dataset before starting statistical analysis?
data <- read.csv("Walmart.csv")
str(data)## 'data.frame': 6435 obs. of 12 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : logi NA NA NA NA NA NA ...
## $ 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 ...
## $ Month : logi NA NA NA NA NA NA ...
## $ Year : logi NA NA NA NA NA NA ...
## $ Sales_Category: chr "High" "High" "High" "High" ...
## $ Bonus_Sales : num 164369 164196 161197 140973 155481 ...colSums(is.na(data))## Store Date Weekly_Sales Holiday_Flag Temperature
## 0 6435 0 0 0
## Fuel_Price CPI Unemployment Month Year
## 0 0 0 6435 6435
## Sales_Category Bonus_Sales
## 0 0data <- unique(data)
data$Date <- as.Date(as.character(data$Date), format="%d-%m-%Y")
data$Holiday_Flag <- as.factor(data$Holiday_Flag)
data$Month <- as.numeric(format(data$Date, "%m"))
data$Year <- as.numeric(format(data$Date, "%Y"))
data$Sales_Category <- ifelse(data$Weekly_Sales > mean(data$Weekly_Sales),
"High", "Low")
data$Bonus_Sales <- data$Weekly_Sales * 0.10
str(data)## 'data.frame': 6435 obs. of 12 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : Date, format: NA NA ...
## $ Weekly_Sales : num 1643691 1641957 1611968 1409728 1554807 ...
## $ Holiday_Flag : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
## $ 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 ...
## $ Month : num NA NA NA NA NA NA NA NA NA NA ...
## $ Year : num NA NA NA NA NA NA NA NA NA NA ...
## $ Sales_Category: chr "High" "High" "High" "High" ...
## $ Bonus_Sales : num 164369 164196 161197 140973 155481 ...summary(data)## Store Date Weekly_Sales Holiday_Flag Temperature
## Min. : 1 Min. :NA Min. : 209986 0:5985 Min. : -2.06
## 1st Qu.:12 1st Qu.:NA 1st Qu.: 553350 1: 450 1st Qu.: 47.46
## Median :23 Median :NA Median : 960746 Median : 62.67
## Mean :23 Mean :NaN Mean :1046965 Mean : 60.66
## 3rd Qu.:34 3rd Qu.:NA 3rd Qu.:1420159 3rd Qu.: 74.94
## Max. :45 Max. :NA Max. :3818686 Max. :100.14
## NAs :6435
## Fuel_Price CPI Unemployment Month Year
## Min. :2.472 Min. :126.1 Min. : 3.879 Min. : NA Min. : NA
## 1st Qu.:2.933 1st Qu.:131.7 1st Qu.: 6.891 1st Qu.: NA 1st Qu.: NA
## Median :3.445 Median :182.6 Median : 7.874 Median : NA Median : NA
## Mean :3.359 Mean :171.6 Mean : 7.999 Mean :NaN Mean :NaN
## 3rd Qu.:3.735 3rd Qu.:212.7 3rd Qu.: 8.622 3rd Qu.: NA 3rd Qu.: NA
## Max. :4.468 Max. :227.2 Max. :14.313 Max. : NA Max. : NA
## NAs :6435 NAs :6435
## Sales_Category Bonus_Sales
## Length :6435 Min. : 20999
## N.unique : 2 1st Qu.: 55335
## N.blank : 0 Median : 96075
## Min.nchar: 3 Mean :104696
## Max.nchar: 4 3rd Qu.:142016
## Max. :381869
## Interpretation: The dataset was cleaned, formatted, and enhanced with new variables to improve data quality and prepare it for accurate analysis, visualization, and prediction.
3 Outlier Detection and Removal
3.1 Question 2: How do we detect and remove outliers from Weekly Sales using the IQR method?
Q1 <- quantile(data$Weekly_Sales, 0.25)
Q3 <- quantile(data$Weekly_Sales, 0.75)
IQRV <- IQR(data$Weekly_Sales)
lower <- Q1 - 1.5 * IQRV
upper <- Q3 + 1.5 * IQRV
data_clean <- data %>%
filter(Weekly_Sales >= lower & Weekly_Sales <= upper)
nrow(data)## [1] 6435nrow(data_clean)## [1] 6401summary(data_clean$Weekly_Sales)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 209986 551743 957298 1036130 1414565 2685352Interpretation: Outliers were removed using the IQR method to improve data consistency and ensure more reliable statistical analysis and predictive modeling.
3.2 Question 3: How do we remove outliers from all remaining major numeric variables apart from Weekly Sales?
remove_outliers <- function(data, column) {
Q1 <- quantile(data[[column]], 0.25, na.rm = TRUE)
Q3 <- quantile(data[[column]], 0.75, na.rm = TRUE)
IQRV <- IQR(data[[column]], na.rm = TRUE)
lower <- Q1 - 1.5 * IQRV
upper <- Q3 + 1.5 * IQRV
data <- data[data[[column]] >= lower & data[[column]] <= upper, ]
return(data)
}
remaining_cols <- c("Temperature", "Fuel_Price", "CPI", "Unemployment")
before_clean <- nrow(data_clean)
for(col in remaining_cols){
data_clean <- remove_outliers(data_clean, col)
}
after_clean <- nrow(data_clean)
before_clean## [1] 6401after_clean## [1] 5917summary(data_clean[, remaining_cols])## Temperature Fuel_Price CPI Unemployment
## Min. : 7.46 Min. :2.472 Min. :126.1 Min. : 4.308
## 1st Qu.: 46.98 1st Qu.:2.891 1st Qu.:132.8 1st Qu.: 6.891
## Median : 62.62 Median :3.420 Median :190.0 Median : 7.852
## Mean : 60.43 Mean :3.341 Mean :175.0 Mean : 7.722
## 3rd Qu.: 74.73 3rd Qu.:3.721 3rd Qu.:213.8 3rd Qu.: 8.494
## Max. :100.14 Max. :4.468 Max. :227.2 Max. :10.926Interpretation: Additional outlier removal was applied to the remaining numeric variables to fully refine the dataset and improve overall analytical accuracy.
4 Descriptive Statistical Analysis
4.1 Question 4.1: What is the structure and summary of the fully cleaned Walmart dataset?
str(data_clean)## 'data.frame': 5917 obs. of 12 variables:
## $ Store : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : Date, format: NA NA ...
## $ Weekly_Sales : num 1643691 1641957 1611968 1409728 1554807 ...
## $ Holiday_Flag : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
## $ 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 ...
## $ Month : num NA NA NA NA NA NA NA NA NA NA ...
## $ Year : num NA NA NA NA NA NA NA NA NA NA ...
## $ Sales_Category: chr "High" "High" "High" "High" ...
## $ Bonus_Sales : num 164369 164196 161197 140973 155481 ...summary(data_clean)## Store Date Weekly_Sales Holiday_Flag Temperature
## Min. : 1.0 Min. :NA Min. : 209986 0:5508 Min. : 7.46
## 1st Qu.:11.0 1st Qu.:NA 1st Qu.: 552529 1: 409 1st Qu.: 46.98
## Median :22.0 Median :NA Median : 947229 Median : 62.62
## Mean :22.8 Mean :NaN Mean :1039313 Mean : 60.43
## 3rd Qu.:34.0 3rd Qu.:NA 3rd Qu.:1427624 3rd Qu.: 74.73
## Max. :45.0 Max. :NA Max. :2685352 Max. :100.14
## NAs :5917
## Fuel_Price CPI Unemployment Month Year
## Min. :2.472 Min. :126.1 Min. : 4.308 Min. : NA Min. : NA
## 1st Qu.:2.891 1st Qu.:132.8 1st Qu.: 6.891 1st Qu.: NA 1st Qu.: NA
## Median :3.420 Median :190.0 Median : 7.852 Median : NA Median : NA
## Mean :3.341 Mean :175.0 Mean : 7.722 Mean :NaN Mean :NaN
## 3rd Qu.:3.721 3rd Qu.:213.8 3rd Qu.: 8.494 3rd Qu.: NA 3rd Qu.: NA
## Max. :4.468 Max. :227.2 Max. :10.926 Max. : NA Max. : NA
## NAs :5917 NAs :5917
## Sales_Category Bonus_Sales
## Length :5917 Min. : 20999
## N.unique : 2 1st Qu.: 55253
## N.blank : 0 Median : 94723
## Min.nchar: 3 Mean :103931
## Max.nchar: 4 3rd Qu.:142762
## Max. :268535
## Interpretation: This provides an overview of the cleaned dataset and confirms that the data is properly structured for further statistical analysis.
4.2 Question 4.2: What are the mean, median, standard deviation, and variance of Weekly Sales?
mean(data_clean$Weekly_Sales)## [1] 1039313median(data_clean$Weekly_Sales)## [1] 947229.2sd(data_clean$Weekly_Sales)## [1] 551945var(data_clean$Weekly_Sales)## [1] 304643299785Interpretation: These measures describe the central tendency and variability of Weekly Sales in the cleaned dataset.
4.3 Question 4.3: What are the quantiles, IQR, minimum, and maximum values of Weekly Sales?
quantile(data_clean$Weekly_Sales)## 0% 25% 50% 75% 100%
## 209986.2 552529.2 947229.2 1427624.5 2685351.8IQR(data_clean$Weekly_Sales)## [1] 875095.3min(data_clean$Weekly_Sales)## [1] 209986.2max(data_clean$Weekly_Sales)## [1] 2685352Interpretation: These statistics describe the spread, range, and distribution of Weekly Sales after complete data cleaning.
4.4 Question 4.4: How are High and Low Sales categories distributed in the cleaned dataset?
table(data_clean$Sales_Category)##
## High Low
## 2616 3301Interpretation: This distribution shows how sales observations are segmented into high and low performance categories.
5 Business and Sales Performance Analysis
5.1 Question 5.1: What is the average Weekly Sales performance of each Walmart store?
data_clean %>%
group_by(Store) %>%
summarise(Avg_Sales = mean(Weekly_Sales))## # A tibble: 45 × 2
## Store Avg_Sales
## <int> <dbl>
## 1 1 1555264.
## 2 2 1905830.
## 3 3 402704.
## 4 4 2038739.
## 5 5 318012.
## 6 6 1556539.
## 7 7 570706.
## 8 8 908750.
## 9 9 543981.
## 10 10 1852745.
## # ℹ 35 more rowsInterpretation: This analysis compares store-wise average sales performance to identify stronger and weaker performing Walmart stores.
5.2 Question 5.2: What are the maximum and minimum Weekly Sales achieved by each store?
aggregate(Weekly_Sales ~ Store, data = data_clean, max)## Store Weekly_Sales
## 1 1 2387950.2
## 2 2 2658725.3
## 3 3 605990.4
## 4 4 2508955.2
## 5 5 507900.1
## 6 6 2644633.0
## 7 7 1059715.3
## 8 8 1511641.1
## 9 9 905324.7
## 10 10 2555031.2
## 11 11 2306265.4
## 12 12 1061943.5
## 13 13 2462779.1
## 14 14 2685351.8
## 15 15 1368318.2
## 16 16 1004730.7
## 17 17 1309226.8
## 18 18 2027507.1
## 19 19 2678206.4
## 20 20 2565259.9
## 21 21 1587257.8
## 22 22 1962445.0
## 23 23 2587953.3
## 24 24 2386015.8
## 25 25 1295391.2
## 26 26 1573982.5
## 27 27 2627910.8
## 28 28 1500863.5
## 29 29 1130926.8
## 30 30 519354.9
## 31 31 2068943.0
## 32 32 1959527.0
## 33 33 331173.5
## 34 34 1620748.2
## 35 35 1781867.0
## 36 36 489372.0
## 37 37 605791.5
## 38 38 490274.8
## 39 39 2554482.8
## 40 40 1648829.2
## 41 41 2263722.7
## 42 42 674919.4
## 43 43 725043.0
## 44 44 376233.9
## 45 45 1682862.0aggregate(Weekly_Sales ~ Store, data = data_clean, min) ## Store Weekly_Sales
## 1 1 1316899.3
## 2 2 1650394.4
## 3 3 339597.4
## 4 4 1762539.3
## 5 5 260636.7
## 6 6 1261253.2
## 7 7 372673.6
## 8 8 772539.1
## 9 9 452905.2
## 10 10 1627707.3
## 11 11 1100418.7
## 12 12 880415.7
## 13 13 1633663.1
## 14 14 1479514.7
## 15 15 454183.4
## 16 16 368600.0
## 17 17 635862.6
## 18 18 540922.9
## 19 19 1181204.5
## 20 20 1761016.5
## 21 21 596218.2
## 22 22 774262.3
## 23 23 1016756.1
## 24 24 1057290.4
## 25 25 558794.6
## 26 26 809833.2
## 27 27 1263534.9
## 28 28 1124660.8
## 29 29 395987.2
## 30 30 369722.3
## 31 31 1198071.6
## 32 32 955463.8
## 33 33 209986.2
## 34 34 836717.8
## 35 35 576332.1
## 36 36 270678.0
## 37 37 451327.6
## 38 38 397428.2
## 39 39 1158698.4
## 40 40 764014.8
## 41 41 991941.7
## 42 42 428953.6
## 43 43 505405.8
## 44 44 241937.1
## 45 45 617207.6Interpretation: This helps evaluate the sales range of each store by identifying their highest and lowest sales performance.
5.3 Question 5.3: How do holidays impact average Weekly Sales?
data_clean %>%
group_by(Holiday_Flag) %>%
summarise(Avg_Sales = mean(Weekly_Sales))## # A tibble: 2 × 2
## Holiday_Flag Avg_Sales
## <fct> <dbl>
## 1 0 1035645.
## 2 1 1088710.Interpretation: This analysis shows whether holiday periods significantly influence Walmart’s sales performance.
5.4 Question 5.4: What are the monthly average sales trends in the Walmart dataset?
data_clean %>%
group_by(Month) %>%
summarise(Avg_Sales = mean(Weekly_Sales))## # A tibble: 1 × 2
## Month Avg_Sales
## <dbl> <dbl>
## 1 NA 1039313.Interpretation: This analysis identifies seasonal sales patterns and highlights months with stronger or weaker sales performance.
5.5 Question 5.5: What are the yearly average sales trends in the Walmart dataset?
data_clean %>%
group_by(Year) %>%
summarise(Avg_Sales = mean(Weekly_Sales))## # A tibble: 1 × 2
## Year Avg_Sales
## <dbl> <dbl>
## 1 NA 1039313.Interpretation: This analysis compares yearly sales performance to observe overall business growth or decline over time.
5.6 Question 5.6: What are the top 10 highest Weekly Sales records in the cleaned dataset?
data_clean %>%
arrange(desc(Weekly_Sales)) %>%
head(10)## Store Date Weekly_Sales Holiday_Flag Temperature Fuel_Price CPI
## 1 14 <NA> 2685352 1 48.71 3.492 188.3504
## 2 19 <NA> 2678206 0 26.05 3.309 132.7477
## 3 2 <NA> 2658725 1 62.98 2.735 211.4063
## 4 6 <NA> 2644633 0 49.45 3.112 220.9477
## 5 27 <NA> 2627911 1 46.67 3.186 136.6896
## 6 14 <NA> 2623470 0 27.31 2.784 181.8712
## 7 2 <NA> 2614202 1 56.36 3.236 218.1130
## 8 2 <NA> 2609167 0 47.55 2.869 211.0645
## 9 14 <NA> 2600519 0 30.54 3.109 182.5520
## 10 14 <NA> 2594363 0 39.93 3.413 188.7979
## Unemployment Month Year Sales_Category Bonus_Sales
## 1 8.523 NA NA High 268535.2
## 2 8.067 NA NA High 267820.6
## 3 8.163 NA NA High 265872.5
## 4 6.551 NA NA High 264463.3
## 5 8.021 NA NA High 262791.1
## 6 8.992 NA NA High 262347.0
## 7 7.441 NA NA High 261420.2
## 8 8.163 NA NA High 260916.7
## 9 8.724 NA NA High 260051.9
## 10 8.523 NA NA High 259436.3Interpretation: This identifies the highest sales-performing records and highlights peak sales events in the dataset.
5.7 Question 5.7: Which store has the highest average Weekly Sales overall?
data_clean %>%
group_by(Store) %>%
summarise(avg = mean(Weekly_Sales)) %>%
arrange(desc(avg)) %>%
head(1)## # A tibble: 1 × 2
## Store avg
## <int> <dbl>
## 1 20 2058998.Interpretation: This identifies the top-performing Walmart store based on overall average sales performance.
6 Data Visualization
6.1 Question 6.1: What does the histogram of Weekly Sales reveal about sales distribution after data cleaning?
ggplot(data_clean, aes(x = Weekly_Sales)) +
geom_histogram(fill = "yellow", color = "black", bins = 30) +
scale_x_continuous(labels = comma) +
labs(title = "Histogram of Weekly Sales (Clean Data)")
Interpretation: This visualization helps understand the
overall distribution, concentration, and frequency pattern of Weekly
Sales after cleaning.
6.2 Question 6.2: How does the boxplot of Weekly Sales before outlier removal help identify extreme values?
ggplot(data, aes(y = Weekly_Sales)) +
geom_boxplot(fill = "orange") +
scale_y_continuous(labels = comma) +
labs(title = "Boxplot of Weekly Sales")
Interpretation: This boxplot highlights extreme sales
values and visually demonstrates the presence of outliers in the
original dataset.
6.3 Question 6.3: How does the boxplot of Weekly Sales after outlier removal reflect improved data quality?
ggplot(data_clean, aes(y = Weekly_Sales)) +
geom_boxplot(fill = "pink") +
scale_y_continuous(labels = comma) +
labs(title = "Boxplot After Removing Outliers")
Interpretation: This visualization confirms reduced
extreme values and shows a cleaner, more balanced sales distribution
after outlier removal.
6.4 Question 6.4: What does the density plot reveal about the distribution pattern of Weekly Sales?
ggplot(data_clean, aes(x = Weekly_Sales)) +
geom_density(fill = "yellow", alpha = 0.5) +
scale_x_continuous(labels = comma) +
labs(title = "Density Plot")
Interpretation: This plot provides a smooth
visualization of Weekly Sales distribution, helping identify
concentration patterns and overall sales behavior.
6.5 Question 6.5: What relationship exists between Temperature and Weekly Sales?
ggplot(data_clean, aes(Temperature, Weekly_Sales)) +
geom_point(color = "blue", alpha = 0.3) +
labs(title = "Temperature vs Sales")
Interpretation: This scatter plot helps examine whether
temperature variations influence Walmart’s Weekly Sales.
6.6 Question 6.6: How can we visualize the overall Weekly Sales distribution using a sales trend pattern?
ggplot(data, aes(x = Weekly_Sales)) +
geom_line(stat = "density", color = "red") +
scale_x_continuous(labels = comma) +
labs(title = "Weekly Sales Trend Pattern")
Interpretation: This visualization provides an
alternative trend representation of Weekly Sales distribution without
affecting the cleaned dataset or other project sections.
7 Correlation Analysis
7.1 Question 7.1: What is the correlation between Weekly Sales and other major numeric variables in the Walmart dataset?
num_data <- data %>%
select(Weekly_Sales, Temperature, Fuel_Price, CPI, Unemployment)
num_data <- na.omit(num_data)
cor(num_data)## Weekly_Sales Temperature Fuel_Price CPI Unemployment
## Weekly_Sales 1.000000000 -0.06381001 0.009463786 -0.07263416 -0.10617609
## Temperature -0.063810013 1.00000000 0.144981806 0.17688768 0.10115786
## Fuel_Price 0.009463786 0.14498181 1.000000000 -0.17064180 -0.03468374
## CPI -0.072634162 0.17688768 -0.170641795 1.00000000 -0.30202006
## Unemployment -0.106176090 0.10115786 -0.034683745 -0.30202006 1.00000000Interpretation: This correlation matrix evaluates the relationships between Weekly Sales and major economic variables without altering the main project dataset. ### Question 7.2: How can a correlation heatmap visually represent relationships among major numeric variables?
num_data <- data %>%
select(Weekly_Sales, Temperature, Fuel_Price, CPI, Unemployment)
num_data <- na.omit(num_data)
corr_matrix <- cor(num_data)
corrplot(corr_matrix, method = "color")
Interpretation: This heatmap visually represents the
strength and direction of correlations between Weekly Sales and key
economic factors.
7.2 Question 7.3: How can an advanced correlation heatmap with coefficient values improve correlation analysis?
num_data <- data %>%
select(Weekly_Sales, Temperature, Fuel_Price, CPI, Unemployment)
num_data <- na.omit(num_data)
corr_matrix <- cor(num_data)
corrplot(corr_matrix,
method = "color",
col = colorRampPalette(c("blue", "white", "red"))(200),
addCoef.col = "black",
number.cex = 0.8,
tl.col = "black",
tl.srt = 45,
title = "Correlation Heatmap of Walmart Data",
mar = c(0,0,2,0))
Interpretation: This advanced heatmap provides a
clearer visual and numerical understanding of relationships among major
numeric variables.
8 Regression Analysis
8.1 Question 8.1: How does Temperature alone influence Weekly Sales using Simple Linear Regression?
reg_data <- data %>%
select(Weekly_Sales, Temperature) %>%
na.omit()
model1 <- lm(Weekly_Sales ~ Temperature, data = reg_data)
summary(model1)##
## Call:
## lm(formula = Weekly_Sales ~ Temperature, data = reg_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -871164 -488496 -91696 386226 2713005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1165406.0 24139.0 48.279 < 0.0000000000000002 ***
## Temperature -1952.4 380.7 -5.128 0.000000301 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 563300 on 6433 degrees of freedom
## Multiple R-squared: 0.004072, Adjusted R-squared: 0.003917
## F-statistic: 26.3 on 1 and 6433 DF, p-value: 0.0000003008Interpretation: This model evaluates the individual effect of Temperature on Weekly Sales and measures how strongly temperature alone predicts sales performance.
8.2 Question 8.2: How do multiple economic factors collectively influence Weekly Sales using Multiple Linear Regression?
multi_reg_data <- data %>%
select(Weekly_Sales, Temperature, Fuel_Price, CPI, Unemployment) %>%
na.omit()
model2 <- lm(Weekly_Sales ~ Temperature + Fuel_Price + CPI + Unemployment,
data = multi_reg_data)
summary(model2)##
## Call:
## lm(formula = Weekly_Sales ~ Temperature + Fuel_Price + CPI +
## Unemployment, data = multi_reg_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -956788 -478860 -115969 394736 2789686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1743607.6 79553.1 21.918 < 0.0000000000000002 ***
## Temperature -885.7 396.2 -2.235 0.0254 *
## Fuel_Price -12248.4 15751.8 -0.778 0.4368
## CPI -1585.8 195.2 -8.126 0.00000000000000053 ***
## Unemployment -41215.0 3972.7 -10.375 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 557600 on 6430 degrees of freedom
## Multiple R-squared: 0.02433, Adjusted R-squared: 0.02372
## F-statistic: 40.09 on 4 and 6430 DF, p-value: < 0.00000000000000022Interpretation: The multiple regression model is statistically valid and performs better than simple regression, with CPI and Unemployment showing stronger influence on Weekly Sales; however, the low R-squared value indicates that additional business factors beyond these economic variables affect sales performance.
8.3 Question 8.3: How can Weekly Sales be predicted for new economic conditions using the Multiple Linear Regression model?
new_data <- data.frame(
Temperature = 70,
Fuel_Price = 3,
CPI = 220,
Unemployment = 7
)
predict(model2, newdata = new_data)## 1
## 1007481Interpretation: This prediction estimates expected Weekly Sales based on specified Temperature, Fuel Price, CPI, and Unemployment values using the trained multiple regression model.
8.4 Question 8.4: How does the Actual vs Predicted Sales plot evaluate the performance of the Multiple Linear Regression model?
multi_reg_data$Predicted <- predict(model2)
ggplot(multi_reg_data, aes(x = Predicted, y = Weekly_Sales)) +
geom_point(color = "yellow", alpha = 0.3) +
geom_abline(slope = 1, intercept = 0, color = "red") +
labs(title = "Actual vs Predicted Sales")
Interpretation: This plot evaluates model performance
by comparing predicted sales values with actual sales observations,
where points closer to the reference line indicate better prediction
accuracy.
9 Conclusion
The Walmart Sales Analysis project showed that sales performance is influenced by multiple factors including store performance, seasonal trends, holidays, and economic conditions such as CPI and unemployment. Data cleaning and visualization improved analytical accuracy, while regression models provided useful predictive insights despite limited standalone predictive strength. Overall, the project demonstrated how R can effectively convert raw sales data into meaningful business intelligence.