R Notebook
E-commerce Project One.
By Priestam Murithi.
Project Overview This project analyzes an e-commerce dataset containing 300 transactions with customer details, product information, and transaction records. The goal is to uncover insights through data cleaning, exploratory analysis, statistical tests, and visualization. # Set a working directory.
Loading Libraries.
Importing the dataset.
Check the structure of the dataset.
spc_tbl_ [300 × 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ TransactionID : num [1:300] 1 2 3 4 5 6 7 8 9 10 ...
$ CustomerID : num [1:300] 207 253 110 256 274 52 191 165 18 169 ...
$ ProductID : num [1:300] 14 15 19 1 2 15 10 5 5 17 ...
$ Quantity : num [1:300] 5 1 5 3 5 2 1 5 4 2 ...
$ PaymentMethod : chr [1:300] "UPI" "Cash on Delivery" "Cash on Delivery" "Debit Card" ...
$ TransactionDate : chr [1:300] "12/28/2023" "4/17/2023" "1/17/2023" "10/23/2023" ...
$ ProductCategory : chr [1:300] "Clothing" "Electronics" "Clothing" "Home Appliances" ...
$ Price : num [1:300] 33.4 389 145.9 215 325.5 ...
$ Rating : num [1:300] 4.2 2.2 1.7 2.7 3.4 2.2 3.7 1.4 1.4 1.9 ...
$ TotalAmount : num [1:300] 167 389 729 645 1627 ...
$ Age : num [1:300] 50 19 37 50 24 50 40 37 25 53 ...
$ Gender : chr [1:300] "Male" "Female" "Female" "Male" ...
$ Location : chr [1:300] "Houston" "Chicago" "Houston" "Los Angeles" ...
$ MembershipStatus: chr [1:300] "Basic" "Premium" "Basic" "Premium" ...
- attr(*, "spec")=
.. cols(
.. TransactionID = col_double(),
.. CustomerID = col_double(),
.. ProductID = col_double(),
.. Quantity = col_double(),
.. PaymentMethod = col_character(),
.. TransactionDate = col_character(),
.. ProductCategory = col_character(),
.. Price = col_double(),
.. Rating = col_double(),
.. TotalAmount = col_double(),
.. Age = col_double(),
.. Gender = col_character(),
.. Location = col_character(),
.. MembershipStatus = col_character()
.. )
- attr(*, "problems")=<externalptr> 1. Handling Missing Values
Check for missing values
TransactionID CustomerID ProductID Quantity PaymentMethod TransactionDate ProductCategory Price
0 0 0 0 0 0 0 0
Rating TotalAmount Age Gender Location MembershipStatus
0 0 8 13 0 0 Impute missing Age with median
[1] 50 19 37 50 24 50 40 37 25 53 65 62 42 42 42 42 42 42 42 42 34 24 25 52 53 29 24 24 44 19 65 22 52 24 43 44 47 21 42 50 43 23 25
[44] 27 18 35 42 53 34 49 63 27 49 18 62 43 41 41 52 44 31 43 37 50 26 24 36 24 28 62 61 50 37 52 38 25 25 65 49 44 63 59 24 41 56 56
[87] 49 63 46 60 28 46 22 52 61 62 36 42 47 57 54 43 47 32 40 27 39 26 43 20 24 58 59 19 28 59 42 18 42 22 38 65 56 50 40 52 31 24 34
[130] 33 41 38 31 40 43 58 23 59 53 62 24 32 24 63 42 22 25 45 35 21 30 44 37 46 29 62 37 42 21 42 44 55 33 56 40 32 63 39 39 24 52 60
[173] 48 53 35 63 30 33 44 54 58 40 44 22 41 22 39 24 50 63 59 20 43 40 19 63 42 58 56 52 64 56 41 49 54 35 52 26 51 63 42 25 31 46 45
[216] 36 65 41 43 35 38 42 54 39 59 36 42 57 19 55 32 57 59 27 48 56 44 43 43 51 57 52 33 27 42 50 19 47 46 60 29 41 40 40 42 60 49 24
[259] 57 25 34 49 46 42 40 27 29 49 28 60 41 63 23 58 45 59 27 31 38 29 50 65 59 27 24 24 55 53 26 28 40 32 35 59 30 57 59 31 63 23` ## handling missing value for gender # For Gender,replace with “male” category
[1] "Male" "Female" "Female" "Male" "Male" "Male" "Male" "Male" "Female" "Female" "Male" "Male" "Male" "Female"
[15] "Male" "Female" "Male" "Male" "Male" "Female" "Female" "Male" "Male" "Male" "Female" "Male" "Male" "Male"
[29] "Female" "Male" "Female" "Female" "Male" "Male" "Female" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
[43] "Female" "Female" "Female" "Female" NA NA NA NA NA NA NA NA NA NA
[57] NA NA NA "Female" "Male" "Female" "Female" "Male" "Female" "Female" "Female" "Male" "Female" "Female"
[71] "Female" "Female" "Female" "Female" "Female" "Female" "Male" "Female" "Female" "Female" "Female" "Male" "Male" "Male"
[85] "Male" "Female" "Male" "Female" "Female" "Female" "Female" "Female" "Female" "Male" "Male" "Female" "Male" "Female"
[99] "Male" "Male" "Male" "Male" "Female" "Female" "Female" "Male" "Male" "Male" "Female" "Female" "Female" "Male"
[113] "Male" "Male" "Female" "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
[127] "Male" "Male" "Female" "Female" "Male" "Male" "Female" "Female" "Male" "Male" "Male" "Male" "Male" "Male"
[141] "Male" "Male" "Male" "Female" "Male" "Female" "Male" "Male" "Female" "Male" "Male" "Female" "Female" "Female"
[155] "Female" "Female" "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Female" "Male" "Male"
[169] "Male" "Female" "Male" "Male" "Female" "Female" "Female" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
[183] "Female" "Male" "Male" "Female" "Male" "Male" "Female" "Female" "Male" "Female" "Female" "Female" "Female" "Female"
[197] "Female" "Female" "Female" "Male" "Female" "Female" "Female" "Male" "Male" "Female" "Male" "Female" "Female" "Female"
[211] "Female" "Female" "Male" "Female" "Female" "Female" "Female" "Male" "Female" "Female" "Male" "Female" "Male" "Male"
[225] "Male" "Female" "Female" "Female" "Male" "Male" "Female" "Female" "Male" "Female" "Female" "Male" "Male" "Male"
[239] "Male" "Male" "Female" "Female" "Female" "Female" "Male" "Male" "Male" "Male" "Female" "Male" "Female" "Male"
[253] "Female" "Male" "Male" "Female" "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Female" "Male"
[267] "Female" "Male" "Male" "Female" "Male" "Male" "Female" "Female" "Male" "Male" "Male" "Male" "Male" "Female"
[281] "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Male" "Female" "Female" "Female"
[295] "Male" "Male" "Male" "Male" "Female" "Female" TransactionID CustomerID ProductID Quantity PaymentMethod TransactionDate ProductCategory Price
0 0 0 0 0 0 0 0
Rating TotalAmount Age Gender Location MembershipStatus
0 0 0 13 0 0 2. Handling Outliers
Outlier Detection
• Detect and handle outliers in numerical variables (e.g., Age, Price, TotalAmount) using methods like the IQR rule or Z-score.
check for outliers using IQR
Variable: TransactionID - Outliers found: 0
Variable: CustomerID - Outliers found: 0
Variable: ProductID - Outliers found: 0
Variable: Quantity - Outliers found: 0
Variable: Price - Outliers found: 0
Variable: Rating - Outliers found: 0
Variable: TotalAmount - Outliers found: 4
Variable: Age - Outliers found: 0 Visualize outliers with boxplot
Remove outliers
[1] "Number of outliers removed: 4"3. Exploratory Data Analysis (EDA)
Perform univariate and bivariate analysis to understand the distribution of variables and relationships between them # Summary statistics
TransactionID CustomerID ProductID Quantity PaymentMethod TransactionDate ProductCategory
Min. : 1.00 Min. : 4.00 Min. : 1.00 Min. :1.000 Length:296 Length:296 Length:296
1st Qu.: 74.75 1st Qu.: 76.75 1st Qu.: 6.00 1st Qu.:2.000 Class :character Class :character Class :character
Median :149.50 Median :156.50 Median :11.00 Median :3.000 Mode :character Mode :character Mode :character
Mean :149.80 Mean :154.93 Mean :10.64 Mean :3.101
3rd Qu.:224.25 3rd Qu.:230.25 3rd Qu.:15.25 3rd Qu.:4.000
Max. :300.00 Max. :299.00 Max. :20.00 Max. :5.000
Price Rating TotalAmount Age Gender Location MembershipStatus
Min. : 33.36 Min. :1.400 Min. : 33.36 Min. :18.00 Length:296 Length:296 Length:296
1st Qu.:145.89 1st Qu.:2.100 1st Qu.: 348.95 1st Qu.:31.00 Class :character Class :character Class :character
Median :215.02 Median :2.600 Median : 650.96 Median :42.00 Mode :character Mode :character Mode :character
Mean :250.65 Mean :2.932 Mean : 782.45 Mean :41.81
3rd Qu.:341.40 3rd Qu.:3.700 3rd Qu.:1087.23 3rd Qu.:52.00
Max. :466.49 Max. :5.000 Max. :2252.85 Max. :65.00 Univariate Analysis
Age distribution
product category distribution
Histogram of price
Bivariate Analysis
Relationship between Age and Total Amount
Bivariate analysise
BoxPlot of TotalAmount by ProductCategory
Payment method distribution by gender
contigency table of payment method vs membership status
Basic Gold Premium
Cash on Delivery 24 34 21
Credit Card 22 16 25
Debit Card 22 20 23
UPI 35 23 314 REGRESSION MODEL
• Build a regression model to predict TotalAmount based on variables like Age, Price, Quantity, and Rating.
Call:
lm(formula = TotalAmount ~ Age + Price + Rating + Quantity, data = ecommerce_cleaned)
Residuals:
Min 1Q Median 3Q Max
-493.37 -98.03 8.40 99.71 447.75
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -725.30851 52.37741 -13.848 <2e-16 ***
Age 0.08560 0.74111 0.116 0.908
Price 3.14353 0.08184 38.412 <2e-16 ***
Rating -8.46592 9.18348 -0.922 0.357
Quantity 238.95715 6.71914 35.564 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 166.5 on 291 degrees of freedom
Multiple R-squared: 0.9076, Adjusted R-squared: 0.9063
F-statistic: 714.4 on 4 and 291 DF, p-value: < 2.2e-16model diagnostics
Age Price Rating Quantity
1.003959 1.006389 1.015030 1.011156 
Visualize residuals
5. Correlation analysis
Analyze the correlation between numerical variables (e.g., Price vs. TotalAmount, Age vs. TotalAmount).Hypothesis:H0:There is significant relationship between the variables H1:There is no significant Relationship in atleast one of the variables # Select numeric variables
Quantity Price Rating TotalAmount Age
Quantity 1.00000000 0.02973474 -0.09877651 0.65943058 0.02206473
Price 0.02973474 1.00000000 -0.06313723 0.70665706 -0.04423694
Rating -0.09877651 -0.06313723 1.00000000 -0.12278622 0.03821019
TotalAmount 0.65943058 0.70665706 -0.12278622 1.00000000 -0.01488728
Age 0.02206473 -0.04423694 0.03821019 -0.01488728 1.00000000Correlation matrix
##pearson Rank
Age Price Quantity Rating TotalAmount
Age 1.00000000 -0.04423694 0.02206473 0.03821019 -0.01488728
Price -0.04423694 1.00000000 0.02973474 -0.06313723 0.70665706
Quantity 0.02206473 0.02973474 1.00000000 -0.09877651 0.65943058
Rating 0.03821019 -0.06313723 -0.09877651 1.00000000 -0.12278622
TotalAmount -0.01488728 0.70665706 0.65943058 -0.12278622 1.00000000spearman
Age Price Quantity Rating TotalAmount
Age 1.000000000 -0.042935951 0.01352312 0.026838550 -0.009588703
Price -0.042935951 1.000000000 0.01763169 -0.001029652 0.685290962
Quantity 0.013523121 0.017631694 1.00000000 -0.066746308 0.689802071
Rating 0.026838550 -0.001029652 -0.06674631 1.000000000 -0.057914324
TotalAmount -0.009588703 0.685290962 0.68980207 -0.057914324 1.0000000006. Statistical Tests
• Perform a t-test to compare the average TotalAmount spent by male and female customers. • Perform a chi-square test to check the association between ProductCategory and PaymentMethod. • Perform ANOVA to compare the average TotalAmount across different MembershipStatus levels.
Welch Two Sample t-test
data: TotalAmount by Gender
t = -2.4726, df = 274.83, p-value = 0.01402
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
-285.1822 -32.3603
sample estimates:
mean in group Female mean in group Male
696.0371 854.8084 Chi-squared test for product category and payment method
Pearson's Chi-squared test
data: table(ecommerce_cleaned$ProductCategory, ecommerce_cleaned$PaymentMethod)
X-squared = 2.2394, df = 6, p-value = 0.8964ANOVA for TotalAmount across MembershipStatus
Df Sum Sq Mean Sq F value Pr(>F)
MembershipStatus 2 451973 225986 0.762 0.467
Residuals 293 86840140 296383
[1] "No significant difference found in Total Amount across Membership Status levels."7. DATA VISUALIZATION
Histogram of TotalAmount
Pie chart of membership status
Bar Chart: Avg TotalAmount by Location
Donut Chart of Payment Methods
Scatter Plot: Price vs TotalAmount
Box Plot of Ratings by Product Category
Save the cleaned dataset
Conclusion
This project provided a comprehensive analysis of the e-commerce dataset, revealing insights into customer behavior, product performance, and spending patterns. Key findings include: - The average age of customers is around 35 years, with a significant portion being in the 25-34 age range. - The most popular product categories are Electronics and Fashion, with Electronics having the highest average spending. - Payment methods are predominantly Credit Card and PayPal, with Credit Card being the most common.
---
title: "R Notebook"
output: html_notebook
---
# E-commerce Project One.

# By Priestam Murithi.


Project Overview
This project analyzes an e-commerce dataset containing 300 transactions with customer details, product information, and transaction records. The goal is to uncover insights through data cleaning, exploratory analysis, statistical tests, and visualization.
# Set a working directory.
```{r}
include=FALSE
knitr::opts_knit$set(root.dir = "C:/Users/USER/OneDrive/Documents/R TRAINNING")
```

# Loading Libraries.
```{r}
library(tidyverse)
library(dplyr)
library(ggplot2)
library(readr)
library(readxl)

```

# Importing the dataset.
```{r}
ecommerce <- read_csv("E-commerce_data.csv")
head(ecommerce)
```
# Check the structure of the dataset.
```{r}
str(ecommerce)
```

## 1. Handling Missing Values
# Check for missing values
```{r}
colSums(is.na(ecommerce))
```
# Impute missing Age with median
```{r}
ecommerce$Age <- ifelse(is.na(ecommerce$Age), 
                        median(ecommerce$Age, na.rm = TRUE),
                        ecommerce$Age)
print(ecommerce$Age)
```

`
## handling missing value for gender
# For Gender,replace with "male" category
```{r}
ecommerce$Gender <- ifelse(ecommerce$Gender == "", "NA", ecommerce$Gender)
print(ecommerce$Gender)
```


```{r}
colSums(is.na(ecommerce))
```

# 2. Handling Outliers
## Outlier Detection
•	Detect and handle outliers in numerical variables (e.g., Age, Price, TotalAmount) using methods like the IQR rule or Z-score.

## check for outliers using IQR
```{r}
# Function to find outlier indices using the IQR method
find_outliers_iqr <- function(x) {
  Q1 <- quantile(x, 0.25, na.rm = TRUE)
  Q3 <- quantile(x, 0.75, na.rm = TRUE)
  IQR_value <- Q3 - Q1
  lower_bound <- Q1 - 1.5 * IQR_value
  upper_bound <- Q3 + 1.5 * IQR_value
  return(which(x < lower_bound | x > upper_bound))
}

# Identify numeric columns
numeric_columns <- sapply(ecommerce, is.numeric)

# Apply the outlier detection function to each numeric column
outlier_indices_list <- lapply(ecommerce[, numeric_columns, drop = FALSE], find_outliers_iqr)

# Print summary of outliers for each numeric variable
for (col in names(outlier_indices_list)) {
  num_outliers <- length(outlier_indices_list[[col]])
  cat("Variable:", col, "- Outliers found:", num_outliers, "\n")
}
```
# Visualize outliers with boxplot
```{r}
ggplot(ecommerce, aes(y = TotalAmount)) +
  geom_boxplot() +
  ggtitle("Boxplot of Total Amount Spent")
```

# Remove outliers
```{r}
# Step 1: Calculate IQR bounds for TotalAmount
Q1 <- quantile(ecommerce$TotalAmount, 0.25, na.rm = TRUE)
Q3 <- quantile(ecommerce$TotalAmount, 0.75, na.rm = TRUE)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value

# Step 2: Filter the dataset to remove outliers
ecommerce_cleaned <- ecommerce %>%
  filter(TotalAmount >= lower_bound & TotalAmount <= upper_bound)

# Step 3: Optional - Count how many rows were removed
outliers_removed <- nrow(ecommerce) - nrow(ecommerce_cleaned)
print(paste("Number of outliers removed:", outliers_removed))
```
# 3. Exploratory Data Analysis (EDA)
Perform univariate and bivariate analysis to understand the distribution of variables and relationships between them
# Summary statistics
```{r}
summary(ecommerce_cleaned)
```
# Univariate Analysis
# Age distribution
```{r}
ggplot(ecommerce_cleaned, aes(x = Age)) +
  geom_histogram(binwidth = 5, fill = "blue", color = "black") +
  labs(title = "Age Distribution of Customers", x = "Age", y = "Frequency")
```

# product category distribution
```{r}
ggplot(ecommerce_cleaned, aes(x = ProductCategory, fill = ProductCategory)) +
  geom_bar() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  ggtitle("Product Category Distribution")

```
## Histogram of price
```{r}
hist(ecommerce_cleaned$Price,main="Histogram of Price",xlab="Price",col='pink')
```
# Bivariate Analysis
# Relationship between Age and Total Amount
```{r}
ggplot(ecommerce, aes(x = Age, y = TotalAmount)) +
  geom_point(color = "blue", alpha = 0.5) +
  geom_smooth(method = "lm", color = "red") +
  labs(title = "Age vs Total Amount Spent", x = "Age", y = "Total Amount")
```

# Bivariate analysise
# BoxPlot of TotalAmount by ProductCategory
```{r}
ggplot(ecommerce_cleaned, aes(x = ProductCategory, y = TotalAmount, fill = ProductCategory)) +
  geom_boxplot() +
  ggtitle("Total Amount by Product Category")

```
# Payment method distribution by gender
```{r}
ggplot(ecommerce_cleaned, aes(x = PaymentMethod, fill = Gender)) +
  geom_bar(position = "dodge") +
  ggtitle("Payment Method by Gender")
```
## contigency table of payment method vs membership status
```{r}
contingency_table<-table(ecommerce_cleaned$PaymentMethod,ecommerce_cleaned$MembershipStatus)
print(contingency_table)
```

## 4 REGRESSION MODEL
•	Build a regression model to predict TotalAmount based on variables like Age, Price, Quantity, and Rating.

```{r}
model<-lm(TotalAmount~Age+Price+Rating+Quantity,data=ecommerce_cleaned)
summary(model)
```

## model diagnostics
```{r}
library(car)
vif_values=vif(model)
print(vif_values)
```
```{r}
# Check residuals
par(mfrow=c(2,2))
plot(model)

# Residuals vs Fitted
ggplot(data = model, aes(x = .fitted, y = .resid)) +
  geom_point() +
  geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
  ggtitle("Residuals vs Fitted Values") +
  xlab("Fitted Values") +
  ylab("Residuals")
```
# Visualize residuals
```{r}
ggplot(model, aes(x = .fitted, y = .resid)) +
  geom_point() +
  geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
  ggtitle("Residuals vs Fitted Values") +
  xlab("Fitted Values") +
  ylab("Residuals")
plot(model, which = 1)
```
# 5. Correlation analysis
	Analyze the correlation between numerical variables (e.g., Price vs. TotalAmount, Age vs. TotalAmount).

Hypothesis:H0:There is significant relationship between the variables
           H1:There is no significant Relationship in atleast one of the variables
# Select numeric variables
```{r}
numeric_vars <- ecommerce_cleaned %>% 
  select(Quantity, Price, Rating, TotalAmount, Age)
cor(numeric_vars)
```

# Correlation matrix
```{r}
cor_matrix <- cor(numeric_vars)
corrplot(cor_matrix, method = "circle")
```
##pearson Rank
```{r}
##pearson Rank
library(corrplot)
num_data_clean<-ecommerce_cleaned[,c("Age","Price","Quantity","Rating","TotalAmount")]
pearson_matrix<-cor(num_data_clean,method="pearson")
pearson_matrix
```
## spearman
```{r}
## spearman
library(corrplot)
num_data_clean<-ecommerce_cleaned[,c("Age","Price","Quantity","Rating","TotalAmount")]
spearman_matrix<-cor(num_data_clean,method="spearman")
spearman_matrix
```
# 6. Statistical Tests
•	Perform a t-test to compare the average TotalAmount spent by male and female customers.
•	Perform a chi-square test to check the association between ProductCategory and PaymentMethod.
•	Perform ANOVA to compare the average TotalAmount across different MembershipStatus levels.

```{r}
## H_0:There is a statistically significant difference between male and female spending.")
## H_1:There is no statistically significant difference between male and female spending.") 
t.test(TotalAmount ~ Gender, data = ecommerce_cleaned %>% filter(Gender %in% c("Male", "Female")))

```

# Chi-squared test for product category and payment method
```{r}
# H₀: There is an association between Product Category and Payment Method
##H1:There is no association between Product Category and Payment Method

chisq.test(table(ecommerce_cleaned$ProductCategory, ecommerce_cleaned$PaymentMethod))
```
# ANOVA for TotalAmount across MembershipStatus
```{r}
# H₀: There is no significant difference in Total Amount across different Membership Status levels
anova_model <- aov(TotalAmount ~ MembershipStatus, data = ecommerce_cleaned)
summary(anova_model)

# Post-hoc test if ANOVA is significant
if (summary(anova_model)[[1]][["Pr(>F)"]][1] < 0.05) {
  posthoc <- TukeyHSD(anova_model)
  print(posthoc)
} else {
  print("No significant difference found in Total Amount across Membership Status levels.")
}
```

# 7. DATA VISUALIZATION
# Histogram of TotalAmount
```{r}
ggplot(ecommerce_cleaned, aes(x = TotalAmount)) +
  geom_histogram(bins = 30, fill = "steelblue") +
  ggtitle("Distribution of Total Amount Spent")
```

# Pie chart of membership status
```{r}
membership_counts <- ecommerce_cleaned %>% 
  count(MembershipStatus) %>% 
  mutate(perc = n / sum(n))
membership_counts$MembershipStatus <- factor(membership_counts$MembershipStatus, 
                                              levels = membership_counts$MembershipStatus)
ggplot(membership_counts, aes(x = "", y = perc, fill = MembershipStatus)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0) +
  ggtitle("Membership Status Distribution") +
  theme_void() +
  theme(legend.title = element_blank())
```

# Bar Chart: Avg TotalAmount by Location
```{r}
ecommerce_cleaned %>% 
  group_by(Location) %>% 
  summarise(avg_amount = mean(TotalAmount)) %>% 
  ggplot(aes(x = reorder(Location, avg_amount), y = avg_amount, fill = Location)) +
  geom_col() +
  coord_flip() +
  ggtitle("Average Total Amount by Location") +
  xlab("Location") +
  ylab("Average Total Amount")
```

# Donut Chart of Payment Methods
```{r}
payment_counts <- ecommerce_cleaned %>% 
  count(PaymentMethod) %>% 
  mutate(perc = n / sum(n))
ggplot(payment_counts, aes(x = 2, y = perc, fill = PaymentMethod)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar(theta = "y", start = 0) +
  xlim(0.5, 2.5) +
  ggtitle("Payment Method Distribution") +
  theme_void() +
  theme(legend.title = element_blank())
```

# Scatter Plot: Price vs TotalAmount
```{r}
ggplot(ecommerce_cleaned, aes(x = Price, y = TotalAmount)) +
  geom_point(alpha = 0.6) +
  geom_smooth(method = "lm") +
  ggtitle("Price vs Total Amount Spent")
```

# Box Plot of Ratings by Product Category
```{r}
ggplot(ecommerce_cleaned, aes(x = ProductCategory, y = Rating, fill = ProductCategory)) +
  geom_boxplot() +
  ggtitle("Product Ratings by Category")
```
# Save the cleaned dataset
```{r}
write_csv(ecommerce_cleaned, "ecommerce_cleaned.csv")
```

# Conclusion
This project provided a comprehensive analysis of the e-commerce dataset, revealing insights into customer behavior, product performance, and spending patterns. Key findings include:
- The average age of customers is around 35 years, with a significant portion being in the 25-34 age range.
- The most popular product categories are Electronics and Fashion, with Electronics having the highest average spending.
- Payment methods are predominantly Credit Card and PayPal, with Credit Card being the most common.









