Descriptive analysis on supermarket sales
Student 1: Anuj singh
Roll No: 02 | Reg No: 12310562 | Group 1Student 2: Suhani yadav
Roll No: 27 | Reg No: 12317440 | Group 12025-11-14
Introduction
The Supermarket Sales dataset contains detailed information about customer purchases, including product details, pricing, customer demographics, and sales metrics such as quantity, total amount, rating, and gross income. Using this dataset, I performed several data analysis tasks to better understand sales patterns and customer behavior.
First, I cleaned the data by converting important columns into numeric format and removing any missing values. After preparing the dataset, I explored different analytical techniques such as grouping, summarising, visualisation, regression analysis, K-means clustering, and KNN classification. These methods helped identify the most popular product lines, average ratings, relationships between price and total sales, and customer clustering patterns. Each visualisation and model was created to uncover meaningful insights that support business decision-making..
Libraries used
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.5.2## Warning: package 'ggplot2' was built under R version 4.5.2## Warning: package 'readr' was built under R version 4.5.2## Warning: package 'dplyr' was built under R version 4.5.2## Warning: package 'lubridate' was built under R version 4.5.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ ggplot2 4.0.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
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## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidyr)
library(lubridate)
library(readr)
library(dplyr)
library(ggplot2)
library(class)## Warning: package 'class' was built under R version 4.5.2DATASET
library(readr)
Supermarket_Sales <- read_csv("D:/SEM5/CAP 484/Supermarket_Sales.csv")## Rows: 1000 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): Invoice ID, Branch, City, Customer type, Gender, Product line, Dat...
## dbl (8): Unit price, Quantity, Tax 5%, Total, cogs, gross margin percentage...
## time (1): Time
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.View(Supermarket_Sales)View structure
View(Supermarket_Sales)TO CHECK THE COLUMNS NAMES
colnames(Supermarket_Sales)## [1] "Invoice ID" "Branch"
## [3] "City" "Customer type"
## [5] "Gender" "Product line"
## [7] "Unit price" "Quantity"
## [9] "Tax 5%" "Total"
## [11] "Date" "Time"
## [13] "Payment" "cogs"
## [15] "gross margin percentage" "gross income"
## [17] "Rating"Data Cleaning
# Remove missing values (if any)
Supermarket_Sales <- Supermarket_Sales %>% drop_na()# Missing
# Missing values check ---
missing_summary <- colSums(is.na(Supermarket_Sales))
print(missing_summary)## Invoice ID Branch City
## 0 0 0
## Customer type Gender Product line
## 0 0 0
## Unit price Quantity Tax 5%
## 0 0 0
## Total Date Time
## 0 0 0
## Payment cogs gross margin percentage
## 0 0 0
## gross income Rating
## 0 0# Average Rating And Summary
# Average rating, gross income, and total sales by branch
summary_branch <- Supermarket_Sales %>%
group_by(Branch) %>%
summarise(
Avg_Rating = mean(Rating, na.rm = TRUE),
Total_Sales = sum(Total, na.rm = TRUE),
Total_Income = sum(`gross income`, na.rm = TRUE),
Transactions = n()
) %>%
arrange(desc(Total_Sales))
summary_branch## # A tibble: 3 × 5
## Branch Avg_Rating Total_Sales Total_Income Transactions
## <chr> <dbl> <dbl> <dbl> <int>
## 1 C 7.07 110569. 5265. 328
## 2 A 7.03 106200. 5057. 340
## 3 B 6.82 106198. 5057. 332This summary calculates the average, avg Total sales values from the dataset.
# Sales In Desc order
#sort by total sales in desc order
Supermarket_Sales <- Supermarket_Sales[order(-Supermarket_Sales$Total), ]These commands sort the dataset by the Total column, first in ascending order and then in descending order to show the smallest and highest sales amounts
# TO MAKE THE NAMES
names(Supermarket_Sales) <- make.names(names(Supermarket_Sales))#FILTER
Supermarket_Sales %>%
filter(Gender =="Female")## # A tibble: 501 × 17
## Invoice.ID Branch City Customer.type Gender Product.line Unit.price Quantity
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 860-79-08… C Nayp… Member Female Fashion acc… 99.3 10
## 2 283-26-52… C Nayp… Member Female Food and be… 98.5 10
## 3 303-96-22… B Mand… Normal Female Home and li… 97.4 10
## 4 744-16-78… B Mand… Normal Female Home and li… 97.4 10
## 5 271-88-87… C Nayp… Member Female Fashion acc… 97.2 10
## 6 554-42-24… C Nayp… Normal Female Sports and … 95.4 10
## 7 325-77-61… A Yang… Member Female Home and li… 90.6 10
## 8 731-81-94… C Nayp… Member Female Sports and … 89.8 10
## 9 817-69-82… B Mand… Normal Female Electronic … 99.7 9
## 10 277-35-58… C Nayp… Member Female Food and be… 99.0 9
## # ℹ 491 more rows
## # ℹ 9 more variables: Tax.5. <dbl>, Total <dbl>, Date <chr>, Time <time>,
## # Payment <chr>, cogs <dbl>, gross.margin.percentage <dbl>,
## # gross.income <dbl>, Rating <dbl>This filter returns only the rows where the customer’s gender is Female, allowing us to analyze sales made specifically by female customers
#SUMMERIZE
Supermarket_Sales %>%
summarise(
Avg_Sales = mean(Total),
Max_Sales = max(Total),
Min_Sales = min(Total)
)## # A tibble: 1 × 3
## Avg_Sales Max_Sales Min_Sales
## <dbl> <dbl> <dbl>
## 1 323. 1043. 10.7#GROUP BY
Supermarket_Sales %>%
group_by(Branch) %>%
summarise(
Total_Revenue = sum(Total),
Avg_Rating = mean(Rating)
)## # A tibble: 3 × 3
## Branch Total_Revenue Avg_Rating
## <chr> <dbl> <dbl>
## 1 A 106200. 7.03
## 2 B 106198. 6.82
## 3 C 110569. 7.07#SLICE
Supermarket_Sales %>% slice(1:5) # first 5rows## # A tibble: 5 × 17
## Invoice.ID Branch City Customer.type Gender Product.line Unit.price Quantity
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 860-79-0874 C Nayp… Member Female Fashion acc… 99.3 10
## 2 687-47-8271 A Yang… Normal Male Fashion acc… 99.0 10
## 3 283-26-5248 C Nayp… Member Female Food and be… 98.5 10
## 4 751-41-9720 C Nayp… Normal Male Home and li… 97.5 10
## 5 303-96-2227 B Mand… Normal Female Home and li… 97.4 10
## # ℹ 9 more variables: Tax.5. <dbl>, Total <dbl>, Date <chr>, Time <time>,
## # Payment <chr>, cogs <dbl>, gross.margin.percentage <dbl>,
## # gross.income <dbl>, Rating <dbl>#UNITE
Supermarket_Sales %>%
unite("City_Branch", City, Branch, sep = "-")## # A tibble: 1,000 × 16
## Invoice.ID City_Branch Customer.type Gender Product.line Unit.price Quantity
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 860-79-0874 Naypyitaw-C Member Female Fashion acc… 99.3 10
## 2 687-47-8271 Yangon-A Normal Male Fashion acc… 99.0 10
## 3 283-26-5248 Naypyitaw-C Member Female Food and be… 98.5 10
## 4 751-41-9720 Naypyitaw-C Normal Male Home and li… 97.5 10
## 5 303-96-2227 Mandalay-B Normal Female Home and li… 97.4 10
## 6 744-16-7898 Mandalay-B Normal Female Home and li… 97.4 10
## 7 271-88-8734 Naypyitaw-C Member Female Fashion acc… 97.2 10
## 8 234-65-2137 Naypyitaw-C Normal Male Home and li… 95.6 10
## 9 554-42-2417 Naypyitaw-C Normal Female Sports and … 95.4 10
## 10 325-77-6186 Yangon-A Member Female Home and li… 90.6 10
## # ℹ 990 more rows
## # ℹ 9 more variables: Tax.5. <dbl>, Total <dbl>, Date <chr>, Time <time>,
## # Payment <chr>, cogs <dbl>, gross.margin.percentage <dbl>,
## # gross.income <dbl>, Rating <dbl>
``` r
#SPLIT
split(Supermarket_Sales, Supermarket_Sales$Branch)## $A
## # A tibble: 340 × 17
## Invoice.ID Branch City Customer.type Gender Product.line Unit.price Quantity
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 687-47-82… A Yang… Normal Male Fashion acc… 99.0 10
## 2 325-77-61… A Yang… Member Female Home and li… 90.6 10
## 3 384-59-66… A Yang… Member Female Food and be… 98.7 9
## 4 704-48-39… A Yang… Member Male Electronic … 88.7 10
## 5 827-77-76… A Yang… Normal Male Sports and … 98.1 9
## 6 139-32-41… A Yang… Member Female Sports and … 97.5 9
## 7 805-86-02… A Yang… Normal Male Home and li… 94.0 9
## 8 698-98-59… A Yang… Normal Female Food and be… 81.2 10
## 9 138-17-51… A Yang… Member Female Home and li… 89.2 9
## 10 638-60-71… A Yang… Normal Female Electronic … 99.6 8
## # ℹ 330 more rows
## # ℹ 9 more variables: Tax.5. <dbl>, Total <dbl>, Date <chr>, Time <time>,
## # Payment <chr>, cogs <dbl>, gross.margin.percentage <dbl>,
## # gross.income <dbl>, Rating <dbl>
##
## $B
## # A tibble: 332 × 17
## Invoice.ID Branch City Customer.type Gender Product.line Unit.price Quantity
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 303-96-22… B Mand… Normal Female Home and li… 97.4 10
## 2 744-16-78… B Mand… Normal Female Home and li… 97.4 10
## 3 219-22-93… B Mand… Member Male Sports and … 100. 9
## 4 817-69-82… B Mand… Normal Female Electronic … 99.7 9
## 5 766-85-70… B Mand… Normal Male Health and … 87.9 10
## 6 743-04-11… B Mand… Member Male Health and … 97.2 9
## 7 746-04-10… B Mand… Member Female Food and be… 84.6 10
## 8 852-62-71… B Mand… Normal Female Fashion acc… 83.2 10
## 9 628-90-86… B Mand… Member Male Health and … 82.6 10
## 10 549-84-74… B Mand… Normal Female Sports and … 90.3 9
## # ℹ 322 more rows
## # ℹ 9 more variables: Tax.5. <dbl>, Total <dbl>, Date <chr>, Time <time>,
## # Payment <chr>, cogs <dbl>, gross.margin.percentage <dbl>,
## # gross.income <dbl>, Rating <dbl>
##
## $C
## # A tibble: 328 × 17
## Invoice.ID Branch City Customer.type Gender Product.line Unit.price Quantity
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 860-79-08… C Nayp… Member Female Fashion acc… 99.3 10
## 2 283-26-52… C Nayp… Member Female Food and be… 98.5 10
## 3 751-41-97… C Nayp… Normal Male Home and li… 97.5 10
## 4 271-88-87… C Nayp… Member Female Fashion acc… 97.2 10
## 5 234-65-21… C Nayp… Normal Male Home and li… 95.6 10
## 6 554-42-24… C Nayp… Normal Female Sports and … 95.4 10
## 7 280-17-43… C Nayp… Member Male Health and … 90.5 10
## 8 702-83-52… C Nayp… Member Male Fashion acc… 99.8 9
## 9 731-81-94… C Nayp… Member Female Sports and … 89.8 10
## 10 393-65-27… C Nayp… Normal Male Food and be… 89.5 10
## # ℹ 318 more rows
## # ℹ 9 more variables: Tax.5. <dbl>, Total <dbl>, Date <chr>, Time <time>,
## # Payment <chr>, cogs <dbl>, gross.margin.percentage <dbl>,
## # gross.income <dbl>, Rating <dbl>PLOTS
#Which branch has the highest total sales
branch_sales <- Supermarket_Sales %>%
group_by(Branch) %>%
summarise(Total_Sales = sum(Total))
ggplot(branch_sales, aes(x=Branch, y=Total_Sales, fill=Branch)) +
geom_bar(stat="identity") +
ggtitle("Total Sales by Branch") +
theme_minimal()
The bar chart shows that total sales vary across the three
branches. One branch clearly generates the highest sales, followed by a
moderately performing branch, while the third branch records the lowest
revenue. This indicates differences in customer footfall, purchasing
behavior, or branch performance.
SALES BY GENDER
#Gender-wise sales comparison
gender_sales <- Supermarket_Sales %>%
group_by(Gender) %>%
summarise(Total_Sales = sum(Total))
ggplot(gender_sales, aes(x=Gender, y=Total_Sales, fill=Gender)) +
geom_bar(stat="identity") +
ggtitle("Sales by Gender") +
theme_minimal()
SALES DISTRIBUTION BY PRODUCT
# Sales distribution across product lines
ggplot(Supermarket_Sales, aes(x=Product.line, y=Total, fill=Product.line)) +
geom_boxplot() +
theme(axis.text.x = element_text(angle=45, hjust=1)) +
ggtitle("Sales Distribution by ProductLine")
The boxplot shows how total sales vary across different product
lines. From the distribution, some product lines have higher median
sales and wider spread, indicating greater variability in purchase
amounts. Other product categories display lower and more consistent
sales values. This comparison highlights which product lines generate
higher revenue per transaction and which ones have more stable but lower
sales performance.
#Knn
#NORMALIZATON
# Normalization function
normalize <- function(x){
return((x - min(x)) / (max(x) - min(x)))
}
numeric_cols <- sapply(Supermarket_Sales, is.numeric)
Supermarket_Sales_norm <- as.data.frame(lapply(Supermarket_Sales[, numeric_cols], normalize))
head(Supermarket_Sales_norm)## Unit.price Quantity Tax.5. Total cogs gross.margin.percentage
## 1 0.9926569 1 1.0000000 1.0000000 1.0000000 NaN
## 2 0.9890966 1 0.9967441 0.9967441 0.9967441 NaN
## 3 0.9839786 1 0.9920637 0.9920637 0.9920637 NaN
## 4 0.9726302 1 0.9816855 0.9816855 0.9816855 NaN
## 5 0.9712951 1 0.9804646 0.9804646 0.9804646 NaN
## 6 0.9711838 1 0.9803628 0.9803628 0.9803628 NaN
## gross.income Rating
## 1 1.0000000 0.43333333
## 2 0.9967441 0.78333333
## 3 0.9920637 0.08333333
## 4 0.9816855 0.66666667
## 5 0.9804646 0.06666667
## 6 0.9803628 0.15000000# NUMERIC FEATURES
data_numeric <- Supermarket_Sales[, c("Quantity", "Unit.price","Rating")]# TARGET VARIABLE INTO CLASSIFICATION
labels <- Supermarket_Sales$Branch# TRAIN AND TEST DATA
set.seed(123)
index <- sample(1:nrow(data_numeric), 0.7 * nrow(data_numeric))
train_data <- data_numeric[index, ]
test_data <- data_numeric[-index, ]
train_label <- labels[index]
test_label <- labels[-index]# BUILD Knn
library(class)
knn_pred <- knn(
train = train_data,
test = test_data,
cl = train_label,
k=5
)
view(knn_pred)This code builds a KNN model using k = 5 to predict branch labels for the test data based on the training data.
Confusion Matrix
confusion_matrix <- table(Predicted = knn_pred, Actual = test_label)
confusion_matrix## Actual
## Predicted A B C
## A 27 34 40
## B 44 26 31
## C 30 33 35# ACCURACY
mean(knn_pred == test_label)## [1] 0.2933333# Combine test data with predicted labels
test_plot <- test_data
test_plot$Predicted_Branch <- knn_pred
# 2D Scatter plot: Unit Price vs Quantity colored by predicted branch
ggplot(test_plot, aes(x = Unit.price, y = Quantity, color = Predicted_Branch)) +
geom_point(size = 3, alpha = 0.7) +
ggtitle("KNN Predictions: Branch") +
xlab("Unit Price") +
ylab("Quantity") +
theme_minimal() +
scale_color_brewer(palette = "Set1")
# SELECT NUMERIC COLUMN
numeric_data <- Supermarket_Sales %>%
select(`Unit.price`, Quantity, Total,Rating)# SCALE DATA
scaled_data <- scale(numeric_data)This code standardizes all numeric features so they are on the same scale before applying K-Means.
# K-means
set.seed(123)
kmeans_model <- kmeans(scaled_data, centers = 3, nstart = 20)
kmeans_model## K-means clustering with 3 clusters of sizes 318, 384, 298
##
## Cluster means:
## Unit.price Quantity Total Rating
## 1 -0.93825443 0.5204319 -0.3829831 0.077915343
## 2 0.07649566 -1.0358478 -0.6905937 -0.009874914
## 3 0.90265293 0.7794235 1.2985792 -0.070419839
##
## Clustering vector:
## [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [38] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [75] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [112] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [186] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [223] 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 1 3 3 3 3 3 1 3 3 3 3 3 3 3 1
## [260] 1 1 3 3 3 3 3 3 3 3 1 1 3 3 3 1 3 3 3 1 3 3 1 1 3 1 3 3 3 1 1 1 1 3 3 1 1
## [297] 1 1 3 3 1 3 3 3 1 3 1 3 1 3 3 1 1 3 3 1 3 3 3 3 1 1 1 3 1 3 3 3 2 1 1 3 3
## [334] 1 1 1 2 2 1 1 1 1 2 1 2 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 2
## [371] 1 1 2 1 2 2 1 1 1 1 2 2 1 1 2 1 2 1 1 1 2 1 1 2 1 1 2 2 2 2 2 2 1 1 2 1 1
## [408] 2 1 2 1 1 2 2 2 2 1 2 1 2 2 2 1 2 2 1 2 2 2 1 1 1 1 1 2 1 2 2 1 2 1 2 2 1
## [445] 1 1 1 2 1 2 1 2 2 1 2 1 2 1 2 1 1 2 2 2 2 2 2 2 1 2 1 1 2 1 1 2 2 1 2 2 1
## [482] 2 2 1 1 2 1 2 2 2 1 2 1 1 1 1 2 2 1 1 1 2 2 1 2 2 2 2 2 2 1 1 1 2 1 2 2 2
## [519] 1 2 1 2 2 1 2 1 1 1 1 1 2 2 1 2 1 2 1 2 1 1 1 1 1 2 1 1 1 1 1 1 2 2 1 1 1
## [556] 2 2 1 2 1 1 1 1 1 2 2 2 2 2 2 1 2 2 1 2 1 2 2 1 1 2 1 2 1 1 1 1 2 1 2 2 2
## [593] 1 2 2 2 2 2 2 2 1 2 1 2 1 1 1 2 2 1 1 1 1 1 1 1 1 2 2 1 1 1 2 1 1 1 2 1 1
## [630] 1 2 2 2 2 1 1 2 2 2 2 1 2 2 2 1 2 2 2 1 2 2 1 2 1 2 2 1 2 1 1 2 1 1 2 1 2
## [667] 2 2 2 2 1 2 1 1 1 2 2 1 2 2 1 2 1 2 2 2 2 2 1 2 2 1 2 2 1 1 2 1 1 2 1 1 2
## [704] 2 2 2 2 1 1 2 1 2 2 1 2 1 1 2 1 1 2 1 1 2 1 2 1 2 2 1 2 1 2 1 2 2 1 1 1 2
## [741] 1 2 2 1 1 2 2 1 2 1 1 2 2 2 2 1 1 2 1 1 1 2 2 2 1 1 1 1 2 1 1 1 1 1 1 2 2
## [778] 2 1 2 2 1 2 2 2 1 2 2 1 2 1 2 2 1 1 1 2 1 2 1 2 2 2 2 1 2 2 2 2 1 2 1 1 1
## [815] 2 2 2 2 1 1 2 2 2 1 1 2 2 2 1 2 1 2 1 1 1 2 2 1 2 2 1 2 2 2 1 1 2 2 2 2 2
## [852] 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 1 2 1 2 2 2 1 2 2 1 1 2 1 1 1 2 2 2 1 1
## [889] 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 1 1 2 2 1 1 2 2 2 2 2 1 2 1 2 2 2
## [926] 2 2 1 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2
## [963] 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1000] 2
##
## Within cluster sum of squares by cluster:
## [1] 597.3201 814.8227 643.9272
## (between_SS / total_SS = 48.5 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"This code runs K-means clustering to group the data into 3 clusters and prints the clustering results.
# ADD CLUSTER
Supermarket_Sales$Cluster <- kmeans_model$cluster
head(Supermarket_Sales)## # A tibble: 6 × 18
## Invoice.ID Branch City Customer.type Gender Product.line Unit.price Quantity
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 860-79-0874 C Nayp… Member Female Fashion acc… 99.3 10
## 2 687-47-8271 A Yang… Normal Male Fashion acc… 99.0 10
## 3 283-26-5248 C Nayp… Member Female Food and be… 98.5 10
## 4 751-41-9720 C Nayp… Normal Male Home and li… 97.5 10
## 5 303-96-2227 B Mand… Normal Female Home and li… 97.4 10
## 6 744-16-7898 B Mand… Normal Female Home and li… 97.4 10
## # ℹ 10 more variables: Tax.5. <dbl>, Total <dbl>, Date <chr>, Time <time>,
## # Payment <chr>, cogs <dbl>, gross.margin.percentage <dbl>,
## # gross.income <dbl>, Rating <dbl>, Cluster <int>This code adds the cluster number assigned by the K-means model to the Supermarket_Sales dataset as a new column.
# PLOT
plot(
scaled_data[, "Unit.price"],
scaled_data[, "Total"],
col = kmeans_model$cluster,
pch = 19,
main = "K-Means Clustering",
xlab = "Unit.Price (scaled)",
ylab = "Total(scaled)"
)
This plot visualizes how K-means has grouped the data into
clusters based on scaled Unit Price and Total values.
# REGRESSION
## SIMPLE LINEAR
model_simple <- lm(cogs ~ `Unit.price`, data = Supermarket_Sales)# MULTIPLE LINEAR
model_simple <- lm(cogs ~ `Unit.price`, data = Supermarket_Sales)# PREDICTED VALUE
predicted_values <- predict(model_simple, newdata = Supermarket_Sales)
head(predicted_values)## 1 2 3 4 5 6
## 552.0502 550.2571 547.6796 541.9641 541.2917 541.2357PLOTS
SALES BY BRANCH
#Which branch has the highest total sales
branch_sales <- Supermarket_Sales %>%
group_by(Branch) %>%
summarise(Total_Sales = sum(Total))
ggplot(branch_sales, aes(x=Branch, y=Total_Sales, fill=Branch)) +
geom_bar(stat="identity") +
ggtitle("Total Sales by Branch") +
theme_minimal()
SALES BY CITY
#Which city contributes the most to total sales
city_sales <- Supermarket_Sales %>%
group_by(City) %>%
summarise(Total_Sales = sum(Total))
ggplot(city_sales, aes(x=City, y=Total_Sales, fill=City)) +
geom_col() +
ggtitle("Sales by City") +
theme_minimal()