o morzu BEZ CENZURY! - opinie internautów

[MickJeff]

As a master's degree student passionate about data science, my academic journey has been a thrilling exploration of the vast realm of statistical analysis. In the quest to enhance my skills, I often find myself delving into real-world datasets and applying the powerful capabilities of R programming for insightful analyses. Today, I'd like to share a hands-on experience with a synthetic dataset, discussing key steps in the analysis process and the valuable insights gained.

Question:

Consider a dataset containing information about sales transactions from an e-commerce website. The dataset includes columns such as "Transaction_ID," "Product_ID," "Customer_ID," "Transaction_Date," and "Transaction_Amount."

Load the dataset into R using an appropriate function.
Explore the summary statistics of the "Transaction_Amount" variable.
Create a histogram to visualize the distribution of transaction amounts.
Use R to identify the top 5 products with the highest average transaction amounts.
Conduct a time-series analysis to examine the trend in transaction amounts over the given period.
Perform a correlation analysis to investigate the relationship between "Transaction_Amount" and "Customer_ID."
Provide explanations for each step and interpret the results obtained from your analysis. Additionally, discuss any insights or potential business implications that can be drawn from the analysis.

answer:
# Step 1: Generate a synthetic dataset
set.seed(123)
n <- 1000
Transaction_ID <- 1:n
Product_ID <- sample(1:20, n, replace = TRUE)
Customer_ID <- sample(1001:1100, n, replace = TRUE)
Transaction_Date <- sample(seq(as.Date('2023-01-01'), as.Date('2023-12-31'), by = 'day'), n, replace = TRUE)
Transaction_Amount <- rnorm(n, mean = 50, sd = 20)

ecom_data <- data.frame(Transaction_ID, Product_ID, Customer_ID, Transaction_Date, Transaction_Amount)

# Step 2: Explore summary statistics
summary(ecom_data$Transaction_Amount)

# Step 3: Create a histogram
hist(ecom_data$Transaction_Amount, main = "Distribution of Transaction Amounts", xlab = "Transaction Amount", col = "lightblue")

# Step 4: Identify top 5 products with highest average transaction amounts
top_products <- aggregate(Transaction_Amount ~ Product_ID, data = ecom_data, mean)
top_products <- top_products[order(-top_products$Transaction_Amount), ]
top5_products <- head(top_products, 5)
print(top5_products)

# Step 5: Time-series analysis
library(ggplot2)
ecom_data$Transaction_Date <- as.Date(ecom_data$Transaction_Date)
ggplot(ecom_data, aes(x = Transaction_Date, y = Transaction_Amount)) +
geom_line() +
labs(title = "Time Series Analysis of Transaction Amounts", x = "Transaction Date", y = "Transaction Amount")

# Step 6: Correlation analysis
correlation <- cor(ecom_data$Transaction_Amount, ecom_data$Customer_ID)
print(paste("Correlation between Transaction Amount and Customer ID:", correlation))

Conclusion: A Continuous Learning Journey

As a student, do my statistical analysis homework using R deepens my appreciation for the power of R in unlocking insights from data. The journey of mastering statistical analysis is ongoing, filled with discoveries that contribute to my growth in the field.[/url]