Data Analysis on Sales Data
MD Faishal
2025-11-17
PHASE 1 — DATA UNDERSTANDING & PREPROCESSING
Q1. What is the structure of the dataset? What do the variables represent?
Q2. Are there any missing values in the dataset? How can you handle them?
Q3. Are there any duplicate records? Should they be removed?
Q4. Apply Min–Max Normalization to numeric variables. What changes do you observe?
Q5. Provide descriptive statistics (mean, median, mode, SD) for key numeric features like Price & Quantity.
PHASE 2 — EDA & STATISTICAL INSIGHTS
Q6. What is the distribution of Quantity purchased? (Histogram + Boxplot)
Q7. How does Price vary across Gender? (Boxplot + comparison)
Q8. Which Product Categories are most purchased? (Bar chart)
Q9. Is there a relationship between Price and Quantity? (Scatter + correlation)
Q10. Create a correlation heatmap. Which numeric features are strongly related?
PHASE 3 — MODELING & MACHINE LEARNING
Q11. Build a Simple Linear Regression model to predict Price using Quantity.
Q12. Perform ANOVA to test whether Price varies significantly by Gender.
Q13. Apply K-Means clustering on numeric features.
Q14. Use KNN Classification to predict Gender based on numeric features (Price, Quantity).
PHASE 4 — ASSOCIATION RULE MINING & FINAL INSIGHTS
Q15. Convert items purchased into transaction format.
Q16. Apply Apriori algorithm to identify association rules.
Q17. What products are most commonly bought together?
ADDITIONAL IMPORTANT QUESTIONS
Q18. Which customer segment (via clustering) spends the most?
Q19. Which age group buys the highest-priced products?
Q20. What time of the year/month/week shows the highest purchase activity?
- Data import
- Missing value handling
- Normalization
- Histograms, boxplots, scatterplots
- Correlation heatmap
- Linear regression
- ANOVA
- K-Means
- KNN
- Apriori
##Q1. What is the structure of the dataset? How many rows and columns does it have?
# Load dataset
data <- read.csv("C:\\Users\\VICTUS\\Downloads\\customer_shopping_data.csv\\customer_shopping_data.csv")
head(data)## invoice_no customer_id gender age category quantity price payment_method
## 1 I138884 C241288 Female 28 Clothing 5 1500.40 Credit Card
## 2 I317333 C111565 Male 21 Shoes 3 1800.51 Debit Card
## 3 I127801 C266599 Male 20 Clothing 1 300.08 Cash
## 4 I173702 C988172 Female 66 Shoes 5 3000.85 Credit Card
## 5 I337046 C189076 Female 53 Books 4 60.60 Cash
## 6 I227836 C657758 Female 28 Clothing 5 1500.40 Credit Card
## invoice_date shopping_mall
## 1 5/8/2022 Kanyon
## 2 12/12/2021 Forum Istanbul
## 3 9/11/2021 Metrocity
## 4 16/05/2021 Metropol AVM
## 5 24/10/2021 Kanyon
## 6 24/05/2022 Forum Istanbul# Q1: Structure of dataset
str(data)## 'data.frame': 99457 obs. of 10 variables:
## $ invoice_no : chr "I138884" "I317333" "I127801" "I173702" ...
## $ customer_id : chr "C241288" "C111565" "C266599" "C988172" ...
## $ gender : chr "Female" "Male" "Male" "Female" ...
## $ age : int 28 21 20 66 53 28 49 32 69 60 ...
## $ category : chr "Clothing" "Shoes" "Clothing" "Shoes" ...
## $ quantity : int 5 3 1 5 4 5 1 2 3 2 ...
## $ price : num 1500.4 1800.5 300.1 3000.8 60.6 ...
## $ payment_method: chr "Credit Card" "Debit Card" "Cash" "Credit Card" ...
## $ invoice_date : chr "5/8/2022" "12/12/2021" "9/11/2021" "16/05/2021" ...
## $ shopping_mall : chr "Kanyon" "Forum Istanbul" "Metrocity" "Metropol AVM" ...# Number of rows and columns
cat("Rows:", nrow(data), "\n")## Rows: 99457cat("Columns:", ncol(data), "\n")## Columns: 10# column names
colnames(data)## [1] "invoice_no" "customer_id" "gender" "age"
## [5] "category" "quantity" "price" "payment_method"
## [9] "invoice_date" "shopping_mall"Q2. Which columns contain missing values, and how much missing data does each column have?
# Q2: Check missing values per column
colSums(is.na(data))## invoice_no customer_id gender age category
## 0 0 0 0 0
## quantity price payment_method invoice_date shopping_mall
## 0 0 0 0 0Q3. Are there any outliers present in the numerical variables (Quantity and Price)? If yes, detect them and apply an appropriate method (such as capping) to handle the outliers.
# Detect outliers for Quantity
quantity_outliers <- boxplot.stats(data$quantity)$out
quantity_outliers## integer(0)# Detect outliers for Price
price_outliers <- boxplot.stats(data$price)$out
price_outliers## [1] 3000.85 3000.85 5250.00 4200.00 5250.00 3000.85 3000.85 3000.85 3150.00
## [10] 3000.85 3150.00 3150.00 3000.85 3000.85 3150.00 3150.00 5250.00 3000.85
## [19] 3000.85 5250.00 3150.00 3000.85 4200.00 3150.00 4200.00 3000.85 3000.85
## [28] 5250.00 4200.00 5250.00 3000.85 4200.00 3150.00 4200.00 3000.85 3000.85
## [37] 5250.00 3150.00 4200.00 4200.00 3150.00 4200.00 5250.00 4200.00 3000.85
## [46] 3000.85 5250.00 5250.00 5250.00 3000.85 4200.00 3000.85 5250.00 4200.00
## [55] 3000.85 3000.85 3150.00 3000.85 4200.00 3150.00 3000.85 4200.00 4200.00
## [64] 5250.00 3000.85 3150.00 3150.00 3000.85 5250.00 5250.00 3000.85 3000.85
## [73] 3000.85 3000.85 3000.85 3000.85 3150.00 5250.00 3000.85 3150.00 5250.00
## [82] 4200.00 4200.00 4200.00 5250.00 3000.85 4200.00 4200.00 5250.00 3000.85
## [91] 5250.00 5250.00 5250.00 3150.00 4200.00 3150.00 3150.00 3000.85 3000.85
## [100] 3000.85 3000.85 3000.85 3000.85 3150.00 4200.00 5250.00 3000.85 4200.00
## [109] 3000.85 3000.85 3000.85 3000.85 5250.00 5250.00 5250.00 3000.85 3150.00
## [118] 3150.00 5250.00 4200.00 3000.85 3000.85 3150.00 4200.00 4200.00 4200.00
## [127] 5250.00 4200.00 4200.00 3000.85 5250.00 3150.00 3000.85 3000.85 3000.85
## [136] 3000.85 5250.00 4200.00 3000.85 4200.00 3150.00 4200.00 5250.00 5250.00
## [145] 3150.00 3000.85 5250.00 3000.85 3150.00 3150.00 3150.00 3000.85 4200.00
## [154] 3000.85 3000.85 3000.85 5250.00 3150.00 5250.00 5250.00 3000.85 3150.00
## [163] 3000.85 3150.00 3000.85 5250.00 5250.00 5250.00 3000.85 4200.00 3000.85
## [172] 3150.00 4200.00 5250.00 3000.85 3000.85 3000.85 3000.85 4200.00 5250.00
## [181] 3150.00 3000.85 5250.00 3000.85 3000.85 3000.85 4200.00 5250.00 3000.85
## [190] 4200.00 3000.85 4200.00 3000.85 3000.85 3000.85 3000.85 4200.00 3150.00
## [199] 5250.00 3000.85 4200.00 3000.85 5250.00 3000.85 3000.85 3000.85 5250.00
## [208] 3000.85 5250.00 3150.00 3000.85 4200.00 4200.00 3150.00 3150.00 5250.00
## [217] 4200.00 5250.00 5250.00 4200.00 5250.00 5250.00 3150.00 5250.00 3000.85
## [226] 3000.85 3000.85 3000.85 3150.00 5250.00 3000.85 3000.85 3150.00 5250.00
## [235] 4200.00 3150.00 3150.00 4200.00 4200.00 3000.85 4200.00 3150.00 4200.00
## [244] 3000.85 5250.00 3000.85 3150.00 4200.00 3000.85 5250.00 3150.00 3150.00
## [253] 3000.85 3000.85 3150.00 3150.00 3000.85 5250.00 3150.00 5250.00 3150.00
## [262] 4200.00 4200.00 4200.00 3000.85 5250.00 3150.00 3000.85 3150.00 3000.85
## [271] 5250.00 3000.85 3150.00 3150.00 3000.85 3000.85 5250.00 3000.85 3000.85
## [280] 5250.00 4200.00 5250.00 5250.00 3000.85 3000.85 4200.00 3150.00 3000.85
## [289] 5250.00 3150.00 3000.85 4200.00 5250.00 3000.85 3150.00 3000.85 5250.00
## [298] 3000.85 5250.00 3000.85 3000.85 3000.85 4200.00 5250.00 3000.85 3150.00
## [307] 3000.85 3000.85 3000.85 3000.85 3150.00 5250.00 3000.85 4200.00 4200.00
## [316] 3000.85 5250.00 5250.00 3150.00 3150.00 3000.85 5250.00 3000.85 3150.00
## [325] 3150.00 3150.00 3000.85 5250.00 3000.85 4200.00 5250.00 4200.00 3000.85
## [334] 3150.00 5250.00 3150.00 3150.00 3150.00 3000.85 3000.85 3000.85 3000.85
## [343] 5250.00 4200.00 3000.85 4200.00 3150.00 3000.85 4200.00 5250.00 4200.00
## [352] 3000.85 3000.85 5250.00 4200.00 3000.85 3000.85 3150.00 4200.00 3150.00
## [361] 3150.00 3150.00 3000.85 3150.00 3000.85 3000.85 4200.00 4200.00 3000.85
## [370] 5250.00 3000.85 3000.85 3150.00 4200.00 3000.85 5250.00 3000.85 3000.85
## [379] 3150.00 3000.85 3000.85 3000.85 4200.00 3000.85 5250.00 3000.85 5250.00
## [388] 5250.00 3000.85 5250.00 3150.00 3150.00 4200.00 5250.00 3150.00 3150.00
## [397] 4200.00 4200.00 3000.85 4200.00 5250.00 5250.00 4200.00 5250.00 3150.00
## [406] 5250.00 3000.85 3150.00 3000.85 3150.00 3000.85 3000.85 3150.00 3000.85
## [415] 5250.00 4200.00 3000.85 5250.00 5250.00 4200.00 3000.85 3000.85 3000.85
## [424] 3000.85 4200.00 3150.00 5250.00 3150.00 3000.85 5250.00 3150.00 3000.85
## [433] 3000.85 3000.85 3000.85 3000.85 5250.00 4200.00 3000.85 5250.00 5250.00
## [442] 3000.85 3000.85 3000.85 3000.85 3000.85 3000.85 5250.00 5250.00 4200.00
## [451] 3150.00 3000.85 3000.85 5250.00 3000.85 5250.00 3000.85 4200.00 3150.00
## [460] 4200.00 5250.00 3000.85 5250.00 3000.85 3150.00 3150.00 3000.85 3000.85
## [469] 4200.00 3000.85 5250.00 5250.00 3150.00 3000.85 5250.00 3000.85 3000.85
## [478] 5250.00 5250.00 4200.00 3150.00 4200.00 3000.85 3000.85 3000.85 3000.85
## [487] 3150.00 3000.85 4200.00 3150.00 3000.85 4200.00 4200.00 5250.00 4200.00
## [496] 3000.85 3000.85 5250.00 4200.00 3150.00 3000.85 5250.00 4200.00 3000.85
## [505] 5250.00 3150.00 5250.00 3000.85 5250.00 3000.85 3150.00 3150.00 3150.00
## [514] 4200.00 3150.00 3000.85 5250.00 3150.00 3150.00 3150.00 3000.85 3000.85
## [523] 5250.00 4200.00 4200.00 3150.00 3150.00 3000.85 3000.85 3000.85 4200.00
## [532] 5250.00 4200.00 3150.00 4200.00 5250.00 3150.00 3150.00 4200.00 3000.85
## [541] 5250.00 4200.00 4200.00 3000.85 5250.00 3000.85 4200.00 3000.85 4200.00
## [550] 4200.00 3000.85 3000.85 4200.00 3000.85 3150.00 3150.00 3000.85 3000.85
## [559] 3000.85 3000.85 3000.85 5250.00 4200.00 4200.00 5250.00 4200.00 3000.85
## [568] 3150.00 3000.85 5250.00 5250.00 3000.85 3000.85 4200.00 3000.85 3000.85
## [577] 3000.85 4200.00 5250.00 3000.85 3000.85 3150.00 3150.00 5250.00 3000.85
## [586] 5250.00 5250.00 5250.00 3000.85 3150.00 4200.00 3000.85 3000.85 3000.85
## [595] 5250.00 4200.00 3000.85 3150.00 3000.85 3000.85 3150.00 3000.85 4200.00
## [604] 5250.00 3000.85 5250.00 3150.00 3000.85 4200.00 3150.00 3150.00 5250.00
## [613] 3000.85 4200.00 3150.00 5250.00 5250.00 4200.00 3150.00 3150.00 3000.85
## [622] 3000.85 3000.85 3150.00 3000.85 3000.85 4200.00 3150.00 3000.85 5250.00
## [631] 3000.85 3000.85 3000.85 3150.00 4200.00 3150.00 3150.00 3000.85 3000.85
## [640] 3150.00 5250.00 3150.00 3000.85 3000.85 4200.00 4200.00 3000.85 3000.85
## [649] 3150.00 4200.00 5250.00 3150.00 3000.85 3000.85 4200.00 3000.85 4200.00
## [658] 5250.00 5250.00 3000.85 3000.85 4200.00 3000.85 5250.00 4200.00 4200.00
## [667] 5250.00 5250.00 3000.85 3000.85 3000.85 3000.85 3000.85 3000.85 3000.85
## [676] 3000.85 3150.00 5250.00 4200.00 5250.00 4200.00 5250.00 5250.00 4200.00
## [685] 3000.85 3000.85 3150.00 3000.85 4200.00 5250.00 3000.85 3000.85 3000.85
## [694] 3000.85 4200.00 3000.85 3000.85 5250.00 3000.85 3000.85 3000.85 5250.00
## [703] 4200.00 4200.00 4200.00 3150.00 3000.85 4200.00 3000.85 3000.85 3000.85
## [712] 3000.85 4200.00 3150.00 4200.00 5250.00 5250.00 3000.85 3000.85 5250.00
## [721] 4200.00 4200.00 5250.00 3000.85 3150.00 3000.85 5250.00 3150.00 3150.00
## [730] 3000.85 5250.00 3000.85 3150.00 3150.00 3000.85 4200.00 3000.85 3150.00
## [739] 3150.00 4200.00 3000.85 5250.00 3000.85 5250.00 4200.00 5250.00 3150.00
## [748] 3000.85 3000.85 3150.00 3150.00 3000.85 3150.00 4200.00 3150.00 5250.00
## [757] 3000.85 5250.00 5250.00 3150.00 4200.00 3000.85 3150.00 3150.00 3150.00
## [766] 3000.85 3000.85 3150.00 3000.85 3000.85 3000.85 4200.00 3150.00 5250.00
## [775] 3000.85 3000.85 4200.00 5250.00 3000.85 4200.00 4200.00 5250.00 3150.00
## [784] 3150.00 3000.85 3000.85 3000.85 4200.00 4200.00 4200.00 4200.00 4200.00
## [793] 3000.85 3000.85 4200.00 3000.85 5250.00 4200.00 3000.85 3000.85 3150.00
## [802] 4200.00 3150.00 3000.85 4200.00 3000.85 4200.00 3150.00 5250.00 3150.00
## [811] 3150.00 3000.85 3000.85 4200.00 3000.85 4200.00 3000.85 4200.00 3000.85
## [820] 4200.00 5250.00 3000.85 4200.00 4200.00 4200.00 3000.85 4200.00 3150.00
## [829] 3150.00 5250.00 5250.00 4200.00 3150.00 4200.00 4200.00 3000.85 3000.85
## [838] 3000.85 3150.00 3000.85 3000.85 4200.00 3000.85 3000.85 3000.85 5250.00
## [847] 3000.85 3150.00 3000.85 3000.85 4200.00 3150.00 5250.00 3150.00 3150.00
## [856] 3150.00 4200.00 5250.00 4200.00 5250.00 5250.00 3000.85 3000.85 3000.85
## [865] 5250.00 3150.00 3150.00 3150.00 5250.00 3000.85 3000.85 3000.85 3150.00
## [874] 5250.00 4200.00 4200.00 3000.85 3000.85 5250.00 4200.00 3150.00 4200.00
## [883] 3000.85 3150.00 3000.85 5250.00 4200.00 3000.85 3000.85 4200.00 5250.00
## [892] 5250.00 5250.00 3000.85 4200.00 3000.85 3000.85 4200.00 3000.85 3150.00
## [901] 3150.00 3000.85 3000.85 4200.00 3150.00 5250.00 5250.00 3000.85 3150.00
## [910] 5250.00 3000.85 5250.00 3000.85 3000.85 3000.85 4200.00 5250.00 3000.85
## [919] 4200.00 4200.00 3150.00 3150.00 5250.00 5250.00 4200.00 4200.00 3150.00
## [928] 3150.00 4200.00 3000.85 4200.00 3150.00 3150.00 4200.00 4200.00 4200.00
## [937] 3150.00 3150.00 5250.00 3150.00 3000.85 5250.00 3000.85 5250.00 3150.00
## [946] 4200.00 3150.00 3000.85 4200.00 5250.00 5250.00 3000.85 4200.00 3150.00
## [955] 4200.00 3000.85 4200.00 3000.85 3000.85 3150.00 3000.85 3000.85 4200.00
## [964] 5250.00 3000.85 3150.00 3000.85 5250.00 3000.85 4200.00 5250.00 3000.85
## [973] 4200.00 3000.85 5250.00 3000.85 3150.00 5250.00 5250.00 5250.00 3150.00
## [982] 3000.85 3000.85 3000.85 4200.00 3000.85 4200.00 5250.00 3000.85 3000.85
## [991] 4200.00 3000.85 3000.85 3150.00 3000.85 3150.00 4200.00 4200.00 3000.85
## [1000] 3150.00 3000.85 4200.00 5250.00 3150.00 5250.00 3000.85 3000.85 3000.85
## [1009] 3000.85 3000.85 5250.00 3000.85 3150.00 3150.00 3000.85 5250.00 3000.85
## [1018] 3000.85 4200.00 4200.00 3150.00 3150.00 4200.00 5250.00 3000.85 4200.00
## [1027] 3150.00 3150.00 5250.00 3000.85 3000.85 3150.00 4200.00 4200.00 3150.00
## [1036] 3000.85 5250.00 5250.00 5250.00 3000.85 4200.00 3150.00 3000.85 3000.85
## [1045] 3000.85 5250.00 3000.85 4200.00 3000.85 3150.00 3000.85 3000.85 3150.00
## [1054] 4200.00 5250.00 3150.00 3150.00 4200.00 3150.00 5250.00 4200.00 4200.00
## [1063] 5250.00 3000.85 5250.00 4200.00 5250.00 5250.00 3150.00 3000.85 3150.00
## [1072] 4200.00 3150.00 4200.00 3000.85 4200.00 3000.85 3000.85 3150.00 3000.85
## [1081] 4200.00 5250.00 5250.00 5250.00 3000.85 3000.85 3150.00 3150.00 3000.85
## [1090] 4200.00 3000.85 3150.00 4200.00 3150.00 5250.00 5250.00 4200.00 3150.00
## [1099] 3000.85 5250.00 5250.00 4200.00 5250.00 3000.85 3000.85 3000.85 5250.00
## [1108] 3000.85 4200.00 4200.00 3000.85 4200.00 3150.00 3000.85 4200.00 3000.85
## [1117] 3000.85 3150.00 4200.00 5250.00 3000.85 3000.85 3000.85 4200.00 3150.00
## [1126] 3000.85 4200.00 3000.85 4200.00 4200.00 4200.00 3150.00 4200.00 3000.85
## [1135] 4200.00 4200.00 3000.85 4200.00 5250.00 5250.00 5250.00 3150.00 4200.00
## [1144] 3000.85 3000.85 3000.85 5250.00 3000.85 3000.85 3000.85 4200.00 3000.85
## [1153] 3150.00 3000.85 3150.00 3000.85 3150.00 3000.85 3150.00 5250.00 3000.85
## [1162] 3000.85 4200.00 3000.85 3000.85 3000.85 3150.00 4200.00 3000.85 3150.00
## [1171] 5250.00 3000.85 4200.00 3000.85 3150.00 3150.00 3000.85 3000.85 4200.00
## [1180] 3150.00 3000.85 3150.00 3150.00 3000.85 3000.85 5250.00 3150.00 3150.00
## [1189] 4200.00 3000.85 5250.00 5250.00 4200.00 3000.85 4200.00 3000.85 3150.00
## [1198] 4200.00 4200.00 5250.00 4200.00 3150.00 3000.85 4200.00 5250.00 3000.85
## [1207] 3150.00 5250.00 5250.00 4200.00 3000.85 4200.00 4200.00 3150.00 3150.00
## [1216] 4200.00 3000.85 3000.85 3000.85 3000.85 4200.00 3000.85 3000.85 3150.00
## [1225] 3000.85 5250.00 3000.85 5250.00 3150.00 4200.00 3150.00 3000.85 3000.85
## [1234] 5250.00 3000.85 3000.85 3000.85 3150.00 3150.00 3000.85 3150.00 3000.85
## [1243] 4200.00 3000.85 3150.00 5250.00 5250.00 3000.85 3000.85 4200.00 3150.00
## [1252] 3000.85 3150.00 3150.00 5250.00 3150.00 3150.00 3150.00 3000.85 5250.00
## [1261] 3000.85 3000.85 4200.00 5250.00 4200.00 3000.85 3150.00 3150.00 5250.00
## [1270] 3150.00 3150.00 3000.85 3000.85 3000.85 3150.00 3000.85 4200.00 3000.85
## [1279] 3150.00 3000.85 3150.00 4200.00 3000.85 5250.00 4200.00 4200.00 3150.00
## [1288] 3150.00 3000.85 3000.85 3000.85 4200.00 4200.00 4200.00 3000.85 3000.85
## [1297] 3000.85 4200.00 3000.85 5250.00 4200.00 4200.00 5250.00 3000.85 3150.00
## [1306] 5250.00 4200.00 5250.00 3150.00 3000.85 3000.85 3000.85 3000.85 3000.85
## [1315] 4200.00 3000.85 4200.00 4200.00 5250.00 3150.00 4200.00 3000.85 3150.00
## [1324] 5250.00 3000.85 3150.00 4200.00 3000.85 5250.00 3000.85 3000.85 4200.00
## [1333] 4200.00 5250.00 3000.85 3000.85 3150.00 4200.00 3000.85 3150.00 3000.85
## [1342] 4200.00 3150.00 4200.00 5250.00 3000.85 3000.85 3000.85 3150.00 3000.85
## [1351] 5250.00 3150.00 3000.85 3150.00 4200.00 3000.85 3000.85 5250.00 4200.00
## [1360] 4200.00 4200.00 3150.00 4200.00 5250.00 3150.00 3000.85 4200.00 4200.00
## [1369] 3000.85 4200.00 3000.85 5250.00 3000.85 5250.00 5250.00 4200.00 4200.00
## [1378] 3150.00 4200.00 5250.00 5250.00 3000.85 3000.85 3000.85 3000.85 5250.00
## [1387] 5250.00 4200.00 3000.85 3000.85 4200.00 3150.00 3150.00 4200.00 5250.00
## [1396] 3150.00 3150.00 5250.00 5250.00 3150.00 3000.85 3150.00 3000.85 3000.85
## [1405] 3000.85 5250.00 3150.00 3000.85 5250.00 3000.85 3150.00 3000.85 3000.85
## [1414] 4200.00 3150.00 5250.00 3000.85 5250.00 5250.00 3000.85 3000.85 4200.00
## [1423] 3000.85 3000.85 3000.85 5250.00 3000.85 3000.85 3150.00 4200.00 3000.85
## [1432] 3000.85 3000.85 3000.85 4200.00 3000.85 3000.85 5250.00 5250.00 3150.00
## [1441] 3000.85 3000.85 3000.85 3150.00 3000.85 3000.85 5250.00 3000.85 3000.85
## [1450] 3000.85 3150.00 3150.00 3150.00 3150.00 3000.85 5250.00 3000.85 3150.00
## [1459] 4200.00 3000.85 4200.00 3000.85 4200.00 3150.00 3150.00 3000.85 3000.85
## [1468] 5250.00 3000.85 3000.85 3000.85 4200.00 3000.85 3000.85 3150.00 5250.00
## [1477] 3000.85 3000.85 4200.00 4200.00 4200.00 4200.00 5250.00 4200.00 4200.00
## [1486] 3000.85 3000.85 5250.00 3150.00 5250.00 5250.00 3150.00 3150.00 3150.00
## [1495] 5250.00 3150.00 5250.00 4200.00 3000.85 5250.00 3150.00 3000.85 3150.00
## [1504] 5250.00 3000.85 5250.00 3150.00 5250.00 3000.85 3000.85 3000.85 5250.00
## [1513] 4200.00 4200.00 3000.85 3000.85 4200.00 4200.00 3000.85 3000.85 4200.00
## [1522] 5250.00 4200.00 3000.85 3150.00 3000.85 3150.00 3150.00 3000.85 3000.85
## [1531] 4200.00 3000.85 3000.85 3150.00 3000.85 4200.00 4200.00 5250.00 3000.85
## [1540] 3150.00 3150.00 3150.00 3000.85 3000.85 5250.00 4200.00 4200.00 3000.85
## [1549] 3000.85 4200.00 4200.00 5250.00 3150.00 3150.00 4200.00 3150.00 5250.00
## [1558] 4200.00 3000.85 3000.85 5250.00 3150.00 3000.85 3150.00 3000.85 5250.00
## [1567] 3000.85 5250.00 3000.85 3000.85 5250.00 3000.85 3150.00 3150.00 3000.85
## [1576] 4200.00 3150.00 4200.00 4200.00 5250.00 4200.00 3150.00 3000.85 5250.00
## [1585] 5250.00 3000.85 3150.00 3000.85 3000.85 3150.00 4200.00 5250.00 4200.00
## [1594] 5250.00 3000.85 4200.00 3000.85 3000.85 3000.85 3000.85 4200.00 5250.00
## [1603] 3000.85 3150.00 3150.00 5250.00 4200.00 3000.85 5250.00 3150.00 4200.00
## [1612] 4200.00 3000.85 5250.00 4200.00 3000.85 3000.85 3000.85 3000.85 3000.85
## [1621] 4200.00 3150.00 3150.00 3000.85 5250.00 3150.00 5250.00 4200.00 3150.00
## [1630] 5250.00 3000.85 3000.85 3150.00 3000.85 3000.85 3150.00 4200.00 3000.85
## [1639] 3000.85 3000.85 5250.00 3000.85 5250.00 3000.85 4200.00 3000.85 3000.85
## [1648] 3000.85 3000.85 3000.85 3150.00 3000.85 3000.85 3000.85 4200.00 4200.00
## [1657] 5250.00 3150.00 3000.85 3000.85 3150.00 3150.00 5250.00 3000.85 4200.00
## [1666] 3000.85 3150.00 3000.85 4200.00 3150.00 3000.85 4200.00 3000.85 4200.00
## [1675] 5250.00 3000.85 3000.85 3150.00 3150.00 5250.00 3000.85 3000.85 3000.85
## [1684] 3000.85 3000.85 3000.85 5250.00 3000.85 4200.00 3000.85 3150.00 4200.00
## [1693] 3150.00 3000.85 4200.00 4200.00 5250.00 3150.00 4200.00 4200.00 3150.00
## [1702] 4200.00 3000.85 3000.85 5250.00 3000.85 3000.85 3000.85 3150.00 3000.85
## [1711] 4200.00 3000.85 4200.00 3000.85 5250.00 3000.85 3000.85 3150.00 3000.85
## [1720] 3000.85 3000.85 4200.00 3000.85 5250.00 3150.00 5250.00 4200.00 3000.85
## [1729] 3000.85 3000.85 3150.00 3000.85 4200.00 5250.00 4200.00 4200.00 3000.85
## [1738] 5250.00 3000.85 3150.00 5250.00 4200.00 5250.00 5250.00 5250.00 4200.00
## [1747] 5250.00 3150.00 3150.00 3000.85 4200.00 4200.00 3000.85 3000.85 3150.00
## [1756] 3000.85 3000.85 3000.85 3000.85 5250.00 4200.00 3000.85 3000.85 4200.00
## [1765] 3000.85 3000.85 4200.00 5250.00 3150.00 5250.00 3150.00 3150.00 3150.00
## [1774] 3000.85 5250.00 3000.85 4200.00 3150.00 3150.00 3150.00 3000.85 3150.00
## [1783] 3000.85 4200.00 5250.00 3000.85 3000.85 5250.00 3000.85 5250.00 3150.00
## [1792] 5250.00 5250.00 5250.00 5250.00 3000.85 3150.00 3150.00 3000.85 4200.00
## [1801] 3000.85 3000.85 3150.00 3000.85 3000.85 4200.00 3000.85 3150.00 4200.00
## [1810] 3000.85 3000.85 5250.00 4200.00 3150.00 4200.00 3000.85 4200.00 3000.85
## [1819] 3000.85 3000.85 5250.00 3000.85 3000.85 5250.00 4200.00 3000.85 3000.85
## [1828] 3150.00 3000.85 3000.85 4200.00 3000.85 3150.00 5250.00 3000.85 5250.00
## [1837] 3150.00 4200.00 3000.85 3000.85 5250.00 5250.00 5250.00 3000.85 5250.00
## [1846] 3000.85 3150.00 3000.85 3000.85 3000.85 5250.00 5250.00 3150.00 3000.85
## [1855] 4200.00 4200.00 3000.85 5250.00 3150.00 3000.85 3150.00 4200.00 3150.00
## [1864] 4200.00 3000.85 3000.85 3000.85 5250.00 5250.00 3000.85 4200.00 3000.85
## [1873] 5250.00 5250.00 3000.85 3000.85 3150.00 5250.00 4200.00 3000.85 3000.85
## [1882] 3000.85 3000.85 3000.85 5250.00 3000.85 5250.00 3000.85 5250.00 3000.85
## [1891] 3000.85 5250.00 4200.00 3000.85 5250.00 3000.85 3150.00 3000.85 3150.00
## [1900] 5250.00 3150.00 5250.00 5250.00 3150.00 3150.00 5250.00 3000.85 3150.00
## [1909] 3150.00 3000.85 3150.00 3000.85 4200.00 4200.00 4200.00 3000.85 3150.00
## [1918] 5250.00 5250.00 3000.85 3000.85 5250.00 4200.00 3000.85 3150.00 3000.85
## [1927] 4200.00 3000.85 3000.85 3150.00 3150.00 3000.85 3000.85 3000.85 3150.00
## [1936] 3000.85 5250.00 4200.00 3000.85 3000.85 3000.85 5250.00 3150.00 4200.00
## [1945] 3000.85 4200.00 3000.85 3150.00 4200.00 3000.85 3000.85 4200.00 3000.85
## [1954] 3000.85 3000.85 3000.85 3000.85 5250.00 3000.85 3150.00 3000.85 3000.85
## [1963] 3000.85 4200.00 4200.00 3150.00 3150.00 5250.00 3000.85 3000.85 3150.00
## [1972] 3000.85 4200.00 3000.85 3150.00 5250.00 3000.85 3000.85 4200.00 4200.00
## [1981] 4200.00 3150.00 3000.85 3000.85 3000.85 3000.85 5250.00 3000.85 3150.00
## [1990] 5250.00 3000.85 3000.85 3000.85 5250.00 3000.85 3150.00 5250.00 4200.00
## [1999] 3150.00 4200.00 3000.85 3150.00 4200.00 3000.85 4200.00 3150.00 4200.00
## [2008] 4200.00 5250.00 3150.00 3150.00 4200.00 3000.85 5250.00 3000.85 4200.00
## [2017] 4200.00 4200.00 3000.85 3150.00 4200.00 5250.00 5250.00 4200.00 3150.00
## [2026] 3000.85 3000.85 4200.00 5250.00 3150.00 3000.85 3000.85 3000.85 3000.85
## [2035] 3150.00 5250.00 5250.00 3000.85 3000.85 3000.85 3150.00 4200.00 3000.85
## [2044] 3150.00 4200.00 3150.00 3000.85 4200.00 3000.85 3000.85 3000.85 4200.00
## [2053] 3000.85 3000.85 5250.00 3000.85 3150.00 3000.85 3150.00 3000.85 3000.85
## [2062] 3150.00 3000.85 4200.00 3000.85 3000.85 5250.00 3150.00 4200.00 5250.00
## [2071] 3000.85 5250.00 4200.00 3000.85 5250.00 4200.00 5250.00 5250.00 3000.85
## [2080] 3000.85 3000.85 3000.85 4200.00 3000.85 3000.85 3000.85 3150.00 5250.00
## [2089] 4200.00 4200.00 3000.85 3000.85 3000.85 3150.00 3150.00 3000.85 3000.85
## [2098] 3000.85 3150.00 3000.85 5250.00 3150.00 4200.00 3000.85 5250.00 3000.85
## [2107] 3000.85 3150.00 5250.00 5250.00 5250.00 5250.00 3000.85 5250.00 3150.00
## [2116] 5250.00 3000.85 3000.85 4200.00 3000.85 5250.00 4200.00 5250.00 4200.00
## [2125] 5250.00 5250.00 4200.00 3000.85 3000.85 3000.85 3000.85 5250.00 3000.85
## [2134] 4200.00 4200.00 3150.00 3150.00 4200.00 3150.00 3000.85 3000.85 5250.00
## [2143] 4200.00 4200.00 3000.85 3000.85 3000.85 3150.00 4200.00 3000.85 3000.85
## [2152] 4200.00 3150.00 3000.85 3000.85 3150.00 3000.85 3150.00 3150.00 4200.00
## [2161] 3150.00 3000.85 5250.00 3150.00 3150.00 3000.85 3000.85 4200.00 5250.00
## [2170] 3150.00 5250.00 3000.85 4200.00 3000.85 5250.00 5250.00 4200.00 3150.00
## [2179] 3000.85 5250.00 3000.85 3150.00 3000.85 3000.85 3150.00 3000.85 3000.85
## [2188] 5250.00 3000.85 5250.00 3000.85 3000.85 5250.00 3000.85 3000.85 5250.00
## [2197] 3000.85 5250.00 3150.00 3150.00 3000.85 5250.00 3000.85 5250.00 3000.85
## [2206] 5250.00 4200.00 4200.00 4200.00 5250.00 3150.00 5250.00 3000.85 5250.00
## [2215] 5250.00 3000.85 5250.00 4200.00 3150.00 4200.00 3000.85 3000.85 5250.00
## [2224] 3000.85 4200.00 3150.00 4200.00 3000.85 3000.85 3150.00 4200.00 3000.85
## [2233] 3000.85 4200.00 5250.00 4200.00 3000.85 3000.85 3000.85 3150.00 5250.00
## [2242] 5250.00 4200.00 3150.00 5250.00 3000.85 3000.85 5250.00 5250.00 5250.00
## [2251] 5250.00 3150.00 3000.85 3000.85 5250.00 5250.00 5250.00 4200.00 3150.00
## [2260] 5250.00 3000.85 4200.00 4200.00 3000.85 3000.85 3000.85 4200.00 3150.00
## [2269] 3000.85 3000.85 4200.00 5250.00 5250.00 3150.00 3150.00 3150.00 3000.85
## [2278] 4200.00 4200.00 3000.85 5250.00 3150.00 3000.85 3000.85 3150.00 3150.00
## [2287] 3150.00 5250.00 3000.85 5250.00 4200.00 5250.00 5250.00 4200.00 3000.85
## [2296] 5250.00 3000.85 3000.85 4200.00 3150.00 5250.00 3000.85 3150.00 3000.85
## [2305] 5250.00 3150.00 3000.85 5250.00 3150.00 3150.00 3000.85 5250.00 5250.00
## [2314] 3000.85 3000.85 3150.00 3150.00 5250.00 3000.85 3150.00 3000.85 4200.00
## [2323] 3150.00 3150.00 4200.00 3000.85 3000.85 3150.00 3000.85 3000.85 4200.00
## [2332] 5250.00 4200.00 5250.00 4200.00 3150.00 5250.00 5250.00 3150.00 3000.85
## [2341] 5250.00 3000.85 3000.85 3000.85 3000.85 5250.00 5250.00 5250.00 4200.00
## [2350] 3000.85 4200.00 5250.00 5250.00 3150.00 3000.85 3000.85 4200.00 4200.00
## [2359] 5250.00 5250.00 5250.00 3150.00 4200.00 4200.00 3000.85 5250.00 3000.85
## [2368] 5250.00 3000.85 3000.85 5250.00 3000.85 3000.85 5250.00 5250.00 5250.00
## [2377] 3000.85 3150.00 3000.85 3150.00 5250.00 3000.85 3000.85 3000.85 3000.85
## [2386] 3150.00 4200.00 3000.85 3000.85 3000.85 5250.00 5250.00 5250.00 3150.00
## [2395] 3000.85 5250.00 5250.00 3150.00 3000.85 3150.00 4200.00 3150.00 3000.85
## [2404] 3150.00 5250.00 3000.85 3000.85 5250.00 5250.00 5250.00 3000.85 3000.85
## [2413] 4200.00 5250.00 3000.85 3000.85 3000.85 3000.85 3150.00 4200.00 5250.00
## [2422] 3150.00 3150.00 3000.85 3000.85 3000.85 3000.85 5250.00 3000.85 3000.85
## [2431] 3000.85 3000.85 3000.85 3000.85 3000.85 3000.85 3000.85 3000.85 3000.85
## [2440] 5250.00 3000.85 5250.00 5250.00 5250.00 3000.85 3000.85 3150.00 3000.85
## [2449] 3000.85 3000.85 3000.85 4200.00 3000.85 3000.85 3150.00 3000.85 3000.85
## [2458] 4200.00 3000.85 3000.85 3000.85 3000.85 3150.00 3150.00 4200.00 5250.00
## [2467] 3000.85 3150.00 3000.85 3000.85 4200.00 3150.00 3000.85 3150.00 4200.00
## [2476] 3000.85 3000.85 3000.85 3150.00 3000.85 3000.85 3000.85 3000.85 3150.00
## [2485] 4200.00 3150.00 5250.00 3000.85 5250.00 4200.00 5250.00 3000.85 4200.00
## [2494] 3000.85 3000.85 4200.00 4200.00 3000.85 3000.85 3000.85 4200.00 3000.85
## [2503] 3150.00 3000.85 4200.00 4200.00 3000.85 5250.00 5250.00 3000.85 4200.00
## [2512] 3150.00 3000.85 4200.00 4200.00 4200.00 3000.85 3000.85 3150.00 5250.00
## [2521] 3150.00 3000.85 4200.00 3150.00 3000.85 3000.85 5250.00 5250.00 3150.00
## [2530] 3000.85 3000.85 3150.00 3150.00 4200.00 5250.00 3150.00 3000.85 3000.85
## [2539] 3150.00 3150.00 5250.00 3000.85 3000.85 4200.00 4200.00 5250.00 3000.85
## [2548] 5250.00 4200.00 3000.85 3000.85 3000.85 3000.85 4200.00 3150.00 3000.85
## [2557] 3000.85 3150.00 4200.00 4200.00 3150.00 3000.85 3000.85 3150.00 5250.00
## [2566] 3150.00 3150.00 3000.85 3000.85 3000.85 3150.00 3150.00 5250.00 3150.00
## [2575] 5250.00 3150.00 5250.00 4200.00 3150.00 3000.85 3000.85 5250.00 5250.00
## [2584] 3000.85 3000.85 3150.00 3000.85 3000.85 3150.00 3150.00 4200.00 5250.00
## [2593] 3150.00 5250.00 5250.00 3000.85 3000.85 4200.00 3000.85 5250.00 3150.00
## [2602] 3000.85 4200.00 3000.85 3000.85 3150.00 4200.00 3000.85 3150.00 3150.00
## [2611] 3000.85 5250.00 4200.00 3000.85 3000.85 5250.00 3150.00 3000.85 3150.00
## [2620] 3000.85 5250.00 5250.00 3000.85 4200.00 4200.00 5250.00 3150.00 4200.00
## [2629] 5250.00 4200.00 4200.00 3000.85 3000.85 3150.00 3000.85 3150.00 3000.85
## [2638] 3000.85 3000.85 5250.00 4200.00 3000.85 3000.85 3000.85 3150.00 3000.85
## [2647] 3150.00 3150.00 3150.00 3150.00 4200.00 3000.85 3150.00 4200.00 3000.85
## [2656] 4200.00 5250.00 3000.85 3000.85 5250.00 3150.00 5250.00 5250.00 3000.85
## [2665] 3150.00 3000.85 3000.85 3000.85 3000.85 3000.85 3000.85 4200.00 3000.85
## [2674] 4200.00 3000.85 5250.00 3000.85 3150.00 4200.00 4200.00 3000.85 3000.85
## [2683] 4200.00 3000.85 3000.85 3000.85 3150.00 3000.85 3000.85 3150.00 3000.85
## [2692] 3000.85 4200.00 5250.00 3000.85 3000.85 5250.00 3000.85 3000.85 5250.00
## [2701] 3000.85 5250.00 3000.85 3150.00 3150.00 4200.00 5250.00 3000.85 3150.00
## [2710] 3000.85 4200.00 4200.00 5250.00 3000.85 5250.00 3000.85 3000.85 4200.00
## [2719] 4200.00 3000.85 5250.00 3000.85 5250.00 3000.85 4200.00 5250.00 3150.00
## [2728] 3150.00 5250.00 3000.85 3150.00 4200.00 3000.85 3000.85 3150.00 3000.85
## [2737] 3000.85 5250.00 3000.85 5250.00 3150.00 3000.85 4200.00 3150.00 3000.85
## [2746] 3000.85 3150.00 4200.00 3150.00 3000.85 3000.85 3000.85 3000.85 5250.00
## [2755] 3150.00 3000.85 3000.85 5250.00 5250.00 3150.00 5250.00 3000.85 4200.00
## [2764] 4200.00 3000.85 3000.85 3000.85 3150.00 3000.85 5250.00 4200.00 4200.00
## [2773] 3150.00 5250.00 5250.00 4200.00 3000.85 4200.00 3000.85 5250.00 3000.85
## [2782] 5250.00 5250.00 4200.00 3000.85 5250.00 4200.00 3000.85 3000.85 4200.00
## [2791] 3000.85 4200.00 3150.00 3000.85 3150.00 3000.85 5250.00 4200.00 3000.85
## [2800] 5250.00 3000.85 3000.85 3000.85 3150.00 4200.00 3000.85 3000.85 3000.85
## [2809] 3000.85 4200.00 5250.00 4200.00 3000.85 3000.85 4200.00 5250.00 3000.85
## [2818] 3000.85 5250.00 4200.00 3000.85 3150.00 4200.00 5250.00 5250.00 4200.00
## [2827] 4200.00 3000.85 3000.85 5250.00 4200.00 5250.00 4200.00 4200.00 3000.85
## [2836] 4200.00 4200.00 3000.85 3150.00 4200.00 3000.85 3150.00 3000.85 3000.85
## [2845] 5250.00 3000.85 4200.00 3000.85 3000.85 3000.85 3150.00 3000.85 3000.85
## [2854] 5250.00 4200.00 3000.85 3000.85 3000.85 3000.85 3150.00 3000.85 3000.85
## [2863] 4200.00 3150.00 3150.00 3000.85 5250.00 4200.00 4200.00 3150.00 5250.00
## [2872] 4200.00 3000.85 3000.85 3000.85 3150.00 3000.85 3000.85 3000.85 5250.00
## [2881] 5250.00 3000.85 3150.00 5250.00 3000.85 4200.00 5250.00 3150.00 3000.85
## [2890] 3000.85 5250.00 5250.00 3000.85 3000.85 3150.00 3000.85 3150.00 4200.00
## [2899] 3000.85 3000.85 3150.00 5250.00 4200.00 3000.85 3000.85 3000.85 3000.85
## [2908] 5250.00 5250.00 5250.00 4200.00 5250.00 3150.00 3000.85 3000.85 5250.00
## [2917] 3000.85 3000.85 3150.00 4200.00 4200.00 3000.85 3000.85 3150.00 4200.00
## [2926] 3150.00 3150.00 3150.00 3150.00 3000.85 3150.00 5250.00 4200.00 4200.00
## [2935] 3000.85 3150.00 3150.00 3000.85 3150.00 4200.00 3150.00 3000.85 3000.85
## [2944] 3000.85 3000.85 4200.00 3000.85 3000.85 3000.85 3000.85 3150.00 5250.00
## [2953] 3000.85 4200.00 3000.85 3150.00 5250.00 5250.00 3000.85 3000.85 5250.00
## [2962] 3150.00 3150.00 3000.85 3000.85 3000.85 4200.00 3000.85 3150.00 4200.00
## [2971] 3000.85 3150.00 3150.00 3150.00 3000.85 3000.85 3000.85 3150.00 5250.00
## [2980] 3000.85 4200.00 4200.00 4200.00 3000.85 3150.00 4200.00 3000.85 4200.00
## [2989] 5250.00 3000.85 3000.85 3150.00 3150.00 3000.85 4200.00 3000.85 5250.00
## [2998] 3150.00 3150.00 3150.00 3000.85 3150.00 5250.00 5250.00 3000.85 5250.00
## [3007] 3000.85 5250.00 4200.00 3000.85 3000.85 3000.85 3150.00 3150.00 3000.85
## [3016] 5250.00 3150.00 3150.00 4200.00 5250.00 3000.85 5250.00 5250.00 3150.00
## [3025] 3000.85 5250.00 5250.00 4200.00 3000.85 5250.00 3000.85 4200.00 5250.00
## [3034] 4200.00 3000.85 3000.85 3150.00 3000.85 3150.00 5250.00 3150.00 3150.00
## [3043] 3000.85 3150.00 3000.85 4200.00 4200.00 4200.00 3000.85 3000.85 3000.85
## [3052] 3150.00 5250.00 5250.00 3000.85 3000.85 5250.00 3000.85 3000.85 3000.85
## [3061] 5250.00 3000.85 4200.00 3000.85 3000.85 4200.00 4200.00 3000.85 3000.85
## [3070] 4200.00 4200.00 4200.00 3000.85 3000.85 5250.00 3150.00 5250.00 3150.00
## [3079] 5250.00 4200.00 5250.00 3150.00 3000.85 3150.00 4200.00 3000.85 3000.85
## [3088] 3000.85 5250.00 5250.00 5250.00 3000.85 4200.00 5250.00 4200.00 3000.85
## [3097] 3000.85 4200.00 3000.85 5250.00 3000.85 3000.85 5250.00 3000.85 5250.00
## [3106] 3000.85 4200.00 3150.00 3000.85 3150.00 4200.00 4200.00 3000.85 3150.00
## [3115] 3150.00 5250.00 3000.85 5250.00 5250.00 3000.85 5250.00 4200.00 3150.00
## [3124] 3000.85 5250.00 5250.00 5250.00 3150.00 3000.85 5250.00 3150.00 5250.00
## [3133] 3150.00 4200.00 3000.85 4200.00 4200.00 3000.85 3150.00 4200.00 3000.85
## [3142] 3150.00 3000.85 3000.85 5250.00 3000.85 3000.85 3150.00 3000.85 3000.85
## [3151] 4200.00 3000.85 3000.85 4200.00 3000.85 3150.00 3000.85 4200.00 3000.85
## [3160] 5250.00 3000.85 3000.85 3150.00 4200.00 5250.00 5250.00 3000.85 3000.85
## [3169] 3150.00 5250.00 3150.00 4200.00 3000.85 3000.85 4200.00 4200.00 5250.00
## [3178] 4200.00 3150.00 3150.00 3000.85 3000.85 4200.00 3000.85 3150.00 4200.00
## [3187] 5250.00 4200.00 3000.85 3000.85 4200.00 5250.00 3000.85 3150.00 3150.00
## [3196] 3000.85 4200.00 3000.85 4200.00 5250.00 4200.00 5250.00 3000.85 5250.00
## [3205] 4200.00 3150.00 3000.85 3000.85 3150.00 4200.00 4200.00 4200.00 5250.00
## [3214] 4200.00 3150.00 3150.00 4200.00 3000.85 3000.85 3150.00 3000.85 3000.85
## [3223] 5250.00 3000.85 3000.85 3000.85 3000.85 3150.00 3000.85 3150.00 4200.00
## [3232] 3150.00 3000.85 4200.00 5250.00 4200.00 5250.00 3000.85 4200.00 4200.00
## [3241] 3000.85 4200.00 3150.00 4200.00 3150.00 3150.00 3150.00 5250.00 4200.00
## [3250] 3000.85 5250.00 3000.85 5250.00 4200.00 3000.85 3000.85 4200.00 3150.00
## [3259] 4200.00 4200.00 3000.85 3150.00 5250.00 4200.00 5250.00 3000.85 5250.00
## [3268] 5250.00 3000.85 4200.00 4200.00 3150.00 5250.00 5250.00 5250.00 3000.85
## [3277] 4200.00 3150.00 4200.00 4200.00 4200.00 3150.00 5250.00 4200.00 3000.85
## [3286] 3000.85 3000.85 3000.85 3150.00 4200.00 4200.00 5250.00 3000.85 3150.00
## [3295] 3150.00 3150.00 3000.85 3150.00 3000.85 3150.00 3000.85 5250.00 3000.85
## [3304] 3000.85 4200.00 4200.00 3150.00 3000.85 5250.00 3000.85 3150.00 3000.85
## [3313] 5250.00 5250.00 4200.00 3000.85 4200.00 3000.85 5250.00 3150.00 4200.00
## [3322] 3000.85 3150.00 5250.00 3000.85 3000.85 5250.00 5250.00 3150.00 5250.00
## [3331] 3000.85 3150.00 3000.85 4200.00 3150.00 3150.00 3150.00 3000.85 4200.00
## [3340] 3000.85 3150.00 4200.00 3000.85 3000.85 3000.85 3150.00 5250.00 3000.85
## [3349] 5250.00 3000.85 4200.00 5250.00 4200.00 5250.00 3000.85 3150.00 3000.85
## [3358] 5250.00 3150.00 3000.85 3000.85 3150.00 3000.85 5250.00 3000.85 3000.85
## [3367] 3000.85 4200.00 3000.85 3150.00 3150.00 3150.00 3150.00 5250.00 4200.00
## [3376] 3150.00 5250.00 3000.85 3000.85 3150.00 4200.00 5250.00 3150.00 3000.85
## [3385] 3000.85 3150.00 4200.00 3000.85 4200.00 3150.00 4200.00 3000.85 3000.85
## [3394] 3150.00 3000.85 4200.00 3150.00 3000.85 4200.00 3000.85 3000.85 3000.85
## [3403] 5250.00 3000.85 3150.00 3150.00 3000.85 3150.00 5250.00 3000.85 5250.00
## [3412] 3000.85 5250.00 3000.85 3000.85 3000.85 5250.00 5250.00 3000.85 3000.85
## [3421] 5250.00 5250.00 3000.85 5250.00 5250.00 4200.00 5250.00 3000.85 3150.00
## [3430] 5250.00 5250.00 3150.00 3000.85 3150.00 5250.00 3000.85 3000.85 5250.00
## [3439] 3150.00 3150.00 4200.00 3000.85 5250.00 3000.85 4200.00 3000.85 3000.85
## [3448] 3000.85 5250.00 4200.00 5250.00 5250.00 5250.00 3000.85 4200.00 3000.85
## [3457] 3150.00 3000.85 3150.00 3000.85 3000.85 3000.85 4200.00 3000.85 3150.00
## [3466] 4200.00 3000.85 3000.85 3000.85 3000.85 4200.00 3000.85 3150.00 3000.85
## [3475] 5250.00 3000.85 5250.00 3150.00 3150.00 3150.00 3150.00 3000.85 3000.85
## [3484] 3000.85 3000.85 3000.85 3150.00 3000.85 3150.00 3150.00 3150.00 3000.85
## [3493] 3000.85 3000.85 3000.85 3000.85 3000.85 4200.00 5250.00 4200.00 3150.00
## [3502] 3000.85 3150.00 3000.85 5250.00 5250.00 5250.00 5250.00 5250.00 5250.00
## [3511] 3000.85 4200.00 4200.00 3000.85 4200.00 3000.85 4200.00 3150.00 3150.00
## [3520] 3150.00 3000.85 5250.00 3150.00 3000.85 5250.00 3150.00 5250.00 4200.00
## [3529] 4200.00 4200.00 3150.00 3000.85 5250.00 4200.00 4200.00 5250.00 3150.00
## [3538] 3150.00 4200.00 3150.00 3000.85 3150.00 3000.85 5250.00 4200.00 3000.85
## [3547] 3000.85 3150.00 3000.85 3000.85 3000.85 3000.85 3000.85 3000.85 3000.85
## [3556] 3000.85 3150.00 3150.00 3150.00 4200.00 3000.85 3000.85 3150.00 3150.00
## [3565] 3000.85 3150.00 3000.85 3000.85 3000.85 3150.00 3150.00 3150.00 5250.00
## [3574] 3150.00 3150.00 5250.00 5250.00 3000.85 5250.00 3000.85 5250.00 3000.85
## [3583] 3000.85 3000.85 5250.00 3150.00 3150.00 3000.85 5250.00 4200.00 3000.85
## [3592] 3000.85 3000.85 4200.00 3000.85 4200.00 3000.85 3150.00 3150.00 3000.85
## [3601] 5250.00 3000.85 4200.00 4200.00 4200.00 5250.00 3000.85 3150.00 5250.00
## [3610] 3000.85 3000.85 4200.00 4200.00 3000.85 3000.85 3000.85 3150.00 3150.00
## [3619] 3000.85 4200.00 3000.85 4200.00 5250.00 3150.00 5250.00 4200.00 5250.00
## [3628] 5250.00 3000.85 4200.00 3000.85 4200.00 3000.85 3000.85 5250.00 3000.85
## [3637] 5250.00 3000.85 3150.00 3150.00 3000.85 3000.85 3000.85 5250.00 3000.85
## [3646] 4200.00 3150.00 3000.85 3000.85 5250.00 5250.00 4200.00 3000.85 5250.00
## [3655] 3000.85 5250.00 3000.85 4200.00 3000.85 5250.00 5250.00 5250.00 4200.00
## [3664] 4200.00 4200.00 5250.00 4200.00 3000.85 3150.00 5250.00 3000.85 3150.00
## [3673] 4200.00 3000.85 4200.00 3000.85 3000.85 3000.85 5250.00 4200.00 4200.00
## [3682] 5250.00 3000.85 3000.85 4200.00 4200.00 3000.85 3000.85 3000.85 3150.00
## [3691] 3000.85 3000.85 3150.00 3150.00 5250.00 3000.85 4200.00 3000.85 3000.85
## [3700] 3150.00 4200.00 5250.00 3150.00 4200.00 3000.85 3000.85 4200.00 5250.00
## [3709] 5250.00 5250.00 4200.00 3000.85 4200.00 3150.00 4200.00 5250.00 3000.85
## [3718] 3150.00 5250.00 3000.85 5250.00 3150.00 3000.85 5250.00 4200.00 4200.00
## [3727] 3150.00 5250.00 3000.85 3150.00 5250.00 3000.85 3150.00 3000.85 3000.85
## [3736] 3150.00 4200.00 4200.00 3000.85 3000.85 3150.00 5250.00 3000.85 3000.85
## [3745] 4200.00 4200.00 4200.00 3150.00 4200.00 5250.00 3000.85 3150.00 4200.00
## [3754] 3000.85 3150.00 5250.00 4200.00 5250.00 3000.85 3000.85 3000.85 3000.85
## [3763] 3000.85 5250.00 3150.00 4200.00 3150.00 4200.00 3150.00 3000.85 3150.00
## [3772] 3000.85 5250.00 3150.00 3150.00 3000.85 3150.00 4200.00 3000.85 3150.00
## [3781] 5250.00 4200.00 3150.00 5250.00 5250.00 5250.00 5250.00 4200.00 5250.00
## [3790] 5250.00 3000.85 3150.00 4200.00 5250.00 3000.85 4200.00 4200.00 5250.00
## [3799] 4200.00 3150.00 5250.00 3000.85 4200.00 5250.00 3150.00 3000.85 3150.00
## [3808] 3000.85 3150.00 3150.00 4200.00 3000.85 3150.00 3150.00 5250.00 5250.00
## [3817] 3150.00 4200.00 3150.00 4200.00 5250.00 3000.85 5250.00 3150.00 3000.85
## [3826] 3000.85 4200.00 4200.00 5250.00 3000.85 3150.00 3000.85 4200.00 3000.85
## [3835] 3000.85 3000.85 5250.00 5250.00 3150.00 3150.00 3000.85 3150.00 4200.00
## [3844] 5250.00 5250.00 3150.00 3150.00 3000.85 5250.00 4200.00 3150.00 3000.85
## [3853] 5250.00 5250.00 3000.85 3000.85 3000.85 4200.00 3000.85 5250.00 4200.00
## [3862] 3150.00 3150.00 3000.85 3000.85 3000.85 3150.00 3000.85 4200.00 3000.85
## [3871] 4200.00 3000.85 3000.85 3000.85 5250.00 3000.85 3000.85 5250.00 5250.00
## [3880] 3150.00 4200.00 5250.00 4200.00 5250.00 3000.85 3000.85 3000.85 3000.85
## [3889] 5250.00 4200.00 3000.85 5250.00 3000.85 4200.00 4200.00 3000.85 3000.85
## [3898] 4200.00 3150.00 3000.85 3000.85 3150.00 3000.85 3150.00 3000.85 4200.00
## [3907] 3000.85 3000.85 5250.00 3000.85 3150.00 3000.85 5250.00 4200.00 4200.00
## [3916] 4200.00 4200.00 5250.00 3000.85 3150.00 3150.00 3000.85 4200.00 5250.00
## [3925] 4200.00 5250.00 3150.00 3000.85 4200.00 3000.85 3000.85 4200.00 3000.85
## [3934] 3150.00 3150.00 3000.85 3150.00 3000.85 3150.00 3150.00 3000.85 3000.85
## [3943] 3000.85 3000.85 5250.00 5250.00 3000.85 4200.00 3000.85 5250.00 5250.00
## [3952] 4200.00 3150.00 3150.00 3000.85 3000.85 4200.00 3000.85 5250.00 3150.00
## [3961] 5250.00 4200.00 4200.00 5250.00 3000.85 3000.85 3000.85 3150.00 3000.85
## [3970] 3150.00 3000.85 5250.00 4200.00 5250.00 3000.85 3150.00 4200.00 3000.85
## [3979] 3000.85 3000.85 5250.00 5250.00 5250.00 4200.00 3150.00 3000.85 3150.00
## [3988] 3000.85 3000.85 3000.85 4200.00 4200.00 3150.00 3000.85 3000.85 3150.00
## [3997] 3000.85 3000.85 3150.00 3000.85 3000.85 3150.00 3000.85 5250.00 5250.00
## [4006] 3150.00 3000.85 5250.00 4200.00 5250.00 4200.00 3000.85 5250.00 3000.85
## [4015] 3000.85 3000.85 4200.00 4200.00 4200.00 5250.00 5250.00 5250.00 3000.85
## [4024] 5250.00 3000.85 5250.00 4200.00 5250.00 4200.00 3000.85 3000.85 4200.00
## [4033] 3000.85 4200.00 4200.00 4200.00 4200.00 3000.85 3000.85 3150.00 3000.85
## [4042] 3000.85 3000.85 3000.85 3150.00 4200.00 5250.00 3000.85 5250.00 3150.00
## [4051] 3000.85 3000.85 3000.85 5250.00 3000.85 3000.85 3150.00 3000.85 4200.00
## [4060] 4200.00 3150.00 4200.00 3000.85 3000.85 3000.85 5250.00 3000.85 3000.85
## [4069] 4200.00 3150.00 3000.85 3000.85 5250.00 5250.00 5250.00 3150.00 5250.00
## [4078] 5250.00 5250.00 5250.00 3000.85 3000.85 3150.00 3150.00 3000.85 3150.00
## [4087] 5250.00 3000.85 3150.00 5250.00 3150.00 4200.00 3000.85 3000.85 3000.85
## [4096] 3000.85 3000.85 4200.00 3000.85 3150.00 3150.00 3000.85 3000.85 5250.00
## [4105] 5250.00 5250.00 3000.85 3000.85 3000.85 3150.00 5250.00 3150.00 3000.85
## [4114] 3150.00 3000.85 3150.00 4200.00 4200.00 5250.00 3150.00 3000.85 5250.00
## [4123] 4200.00 3150.00 3000.85 3000.85 4200.00 4200.00 4200.00 3000.85 3150.00
## [4132] 5250.00 3150.00 3000.85 4200.00 3000.85 3000.85 5250.00 5250.00 3000.85
## [4141] 4200.00 3000.85 3150.00 3000.85 5250.00 3000.85 3000.85 5250.00 3000.85
## [4150] 4200.00 5250.00 3000.85 3000.85 3000.85 3000.85 3000.85 3000.85 3000.85
## [4159] 3150.00 3000.85 5250.00 4200.00 3000.85 5250.00 3150.00 3150.00 3000.85
## [4168] 5250.00 4200.00 3000.85 4200.00 3150.00 3150.00 3000.85 5250.00 3150.00
## [4177] 5250.00 3000.85 3000.85 3000.85 5250.00 3000.85 3000.85 3150.00 3150.00
## [4186] 3000.85 4200.00 5250.00 3150.00 5250.00 3150.00 3150.00 3000.85 3150.00
## [4195] 3000.85 4200.00 3000.85 3000.85 3150.00 4200.00 3000.85 3000.85 3000.85
## [4204] 5250.00 5250.00 4200.00 3000.85 4200.00 3000.85 3000.85 3000.85 4200.00
## [4213] 3000.85 3150.00 4200.00 4200.00 4200.00 3000.85 3000.85 3000.85 3000.85
## [4222] 3000.85 5250.00 3000.85 3150.00 5250.00 3150.00 3000.85 3150.00 4200.00
## [4231] 3000.85 3000.85 4200.00 3000.85 5250.00 3000.85 5250.00 4200.00 4200.00
## [4240] 3000.85 5250.00 3000.85 5250.00 4200.00 3000.85 4200.00 5250.00 4200.00
## [4249] 3000.85 3000.85 3150.00 3000.85 3000.85 3000.85 4200.00 5250.00 5250.00
## [4258] 3000.85 3000.85 5250.00 3000.85 3000.85 4200.00 3000.85 3000.85 4200.00
## [4267] 5250.00 3150.00 5250.00 4200.00 5250.00 3150.00 3000.85 3000.85 3000.85
## [4276] 3150.00 4200.00 3150.00 5250.00 4200.00 3000.85 3150.00 3000.85 3000.85
## [4285] 3000.85 4200.00 3000.85 3000.85 3000.85 3150.00 3150.00 3000.85 3150.00
## [4294] 3150.00 3150.00 3000.85 3150.00 3000.85 3000.85 5250.00 5250.00 5250.00
## [4303] 5250.00 3000.85 5250.00 4200.00 5250.00 5250.00 3000.85 4200.00 5250.00
## [4312] 4200.00 3000.85 4200.00 3000.85 3150.00 3000.85 4200.00 3150.00 3000.85
## [4321] 4200.00 5250.00 3150.00 4200.00 4200.00 4200.00 3150.00 3000.85 3150.00
## [4330] 4200.00 3150.00 5250.00 5250.00 4200.00 3000.85 4200.00 4200.00 3000.85
## [4339] 3000.85 4200.00 5250.00 3000.85 3000.85 5250.00 4200.00 3150.00 3000.85
## [4348] 3000.85 5250.00 4200.00 3000.85 3000.85 4200.00 3150.00 4200.00 3000.85
## [4357] 5250.00 3000.85 3000.85 3000.85 3000.85 5250.00 3000.85 3000.85 5250.00
## [4366] 5250.00 3150.00 4200.00 5250.00 3150.00 3150.00 4200.00 3000.85 3000.85
## [4375] 4200.00 3000.85 3000.85 3150.00 3150.00 3000.85 5250.00 3000.85 3000.85
## [4384] 3000.85 5250.00 4200.00 3000.85 3000.85 5250.00 3000.85 3150.00 4200.00
## [4393] 3000.85 5250.00 4200.00 3150.00 4200.00 4200.00 5250.00 3000.85 3000.85
## [4402] 3000.85 5250.00 4200.00 3150.00 5250.00 3000.85 3000.85 5250.00 3000.85
## [4411] 3150.00 3150.00 5250.00 4200.00 3150.00 3000.85 4200.00 4200.00 3000.85
## [4420] 3000.85 3000.85 5250.00 5250.00 3150.00 4200.00 3000.85 4200.00 3000.85
## [4429] 3150.00 3000.85 3000.85 4200.00 3000.85 3000.85 3000.85 3150.00 3000.85
## [4438] 3150.00 5250.00 4200.00 3150.00 3000.85 3000.85 3000.85 5250.00 5250.00
## [4447] 4200.00 5250.00 3000.85 4200.00 3000.85 4200.00 3000.85 3000.85 3150.00
## [4456] 4200.00 4200.00 5250.00 3000.85 3150.00 3000.85 5250.00 5250.00 4200.00
## [4465] 3000.85 3150.00 3150.00 3000.85 5250.00 3150.00 3000.85 4200.00 3000.85
## [4474] 3000.85 4200.00 3000.85 3000.85 3150.00 5250.00 4200.00 4200.00 3150.00
## [4483] 5250.00 5250.00 3000.85 5250.00 3150.00 3000.85 3000.85 3150.00 3000.85
## [4492] 3000.85 4200.00 3000.85 3000.85 4200.00 3150.00 4200.00 3000.85 5250.00
## [4501] 3000.85 3000.85 3150.00 5250.00 4200.00 3150.00 3150.00 5250.00 3150.00
## [4510] 4200.00 3000.85 3150.00 3150.00 4200.00 3150.00 4200.00 3150.00 3000.85
## [4519] 4200.00 5250.00 5250.00 3000.85 3000.85 3000.85 3000.85 3000.85 4200.00
## [4528] 4200.00 4200.00 5250.00 3000.85 3000.85 3000.85 5250.00 3000.85 4200.00
## [4537] 3000.85 4200.00 5250.00 3000.85 3000.85 5250.00 3150.00 3150.00 3000.85
## [4546] 5250.00 3000.85 3150.00 5250.00 3000.85 4200.00 5250.00 3000.85 5250.00
## [4555] 3000.85 3000.85 5250.00 4200.00 3000.85 5250.00 4200.00 3150.00 3000.85
## [4564] 3150.00 3000.85 3000.85 3000.85 3000.85 3150.00 5250.00 4200.00 3150.00
## [4573] 4200.00 4200.00 3150.00 3150.00 3000.85 3000.85 5250.00 3150.00 3000.85
## [4582] 3000.85 3000.85 3000.85 3000.85 3150.00 3000.85 5250.00 3000.85 3150.00
## [4591] 3000.85 5250.00 3000.85 3150.00 4200.00 4200.00 4200.00 5250.00 3000.85
## [4600] 3000.85 3000.85 3000.85 3150.00 3150.00 5250.00 5250.00 3000.85 5250.00
## [4609] 4200.00 3000.85 3150.00 5250.00 4200.00 3000.85 3000.85 3150.00 4200.00
## [4618] 3000.85 3000.85 5250.00 3150.00 3150.00 4200.00 3000.85 3150.00 5250.00
## [4627] 3000.85 3000.85 3000.85 3000.85 4200.00 3000.85 4200.00 3150.00 4200.00
## [4636] 5250.00 4200.00 3000.85 3000.85 4200.00 4200.00 4200.00 3000.85 5250.00
## [4645] 5250.00 4200.00 5250.00 3000.85 3000.85 4200.00 3150.00 4200.00 4200.00
## [4654] 4200.00 4200.00 3000.85 5250.00 4200.00 5250.00 4200.00 5250.00 3000.85
## [4663] 3000.85 3150.00 3150.00 3000.85 3150.00 4200.00 4200.00 4200.00 4200.00
## [4672] 3000.85 3000.85 3000.85 4200.00 3000.85 3150.00 3150.00 4200.00 3000.85
## [4681] 3150.00 4200.00 5250.00 4200.00 3000.85 3150.00 4200.00 5250.00 4200.00
## [4690] 3000.85 3000.85 5250.00 3150.00 3150.00 3150.00 3000.85 3000.85 3000.85
## [4699] 3150.00 3000.85 3000.85 5250.00 4200.00 3000.85 5250.00 5250.00 3150.00
## [4708] 3000.85 3000.85 5250.00 3000.85 4200.00 3000.85 4200.00 4200.00 5250.00
## [4717] 5250.00 5250.00 3000.85 4200.00 5250.00 4200.00 4200.00 5250.00 5250.00
## [4726] 5250.00 3000.85 3000.85 3000.85 5250.00 3150.00 3000.85 4200.00 3000.85
## [4735] 3000.85 5250.00 3150.00 3150.00 5250.00 3000.85 3000.85 3150.00 3000.85
## [4744] 4200.00 3150.00 4200.00 3150.00 3150.00 3150.00 3150.00 4200.00 5250.00
## [4753] 4200.00 3000.85 5250.00 3000.85 3000.85 5250.00 5250.00 3150.00 4200.00
## [4762] 3000.85 5250.00 3150.00 4200.00 3000.85 5250.00 5250.00 3150.00 5250.00
## [4771] 3000.85 3150.00 3000.85 3000.85 3000.85 3150.00 3000.85 5250.00 4200.00
## [4780] 3000.85 4200.00 4200.00 3000.85 4200.00 3000.85 3000.85 5250.00 3000.85
## [4789] 4200.00 3000.85 3150.00 3000.85 5250.00 5250.00 3000.85 3150.00 4200.00
## [4798] 5250.00 3000.85 3000.85 3150.00 3000.85 3000.85 3000.85 4200.00 5250.00
## [4807] 3150.00 4200.00 3000.85 3000.85 5250.00 5250.00 3000.85 5250.00 5250.00
## [4816] 3150.00 3000.85 3150.00 3000.85 5250.00 3150.00 3000.85 3000.85 5250.00
## [4825] 3150.00 3150.00 4200.00 3000.85 3000.85 3000.85 3000.85 5250.00 4200.00
## [4834] 5250.00 3000.85 3150.00 3000.85 3150.00 3150.00 3150.00 3000.85 4200.00
## [4843] 4200.00 3000.85 3000.85 3000.85 3150.00 5250.00 3150.00 3000.85 3150.00
## [4852] 3000.85 3000.85 4200.00 4200.00 5250.00 3150.00 4200.00 3000.85 4200.00
## [4861] 4200.00 3000.85 5250.00 5250.00 3000.85 3000.85 5250.00 3000.85 3000.85
## [4870] 4200.00 3000.85 3150.00 3150.00 3000.85 3000.85 3000.85 3150.00 4200.00
## [4879] 3150.00 4200.00 3000.85 5250.00 4200.00 5250.00 3150.00 4200.00 3150.00
## [4888] 5250.00 4200.00 3000.85 3000.85 4200.00 3000.85 3000.85 3000.85 3000.85
## [4897] 4200.00 4200.00 5250.00 3000.85 4200.00 3000.85 3000.85 3000.85 3150.00
## [4906] 3150.00 3000.85 3000.85 3000.85 5250.00 5250.00 3000.85 3150.00 4200.00
## [4915] 5250.00 3000.85 3000.85 3000.85 3000.85 3150.00 4200.00 3150.00 3000.85
## [4924] 3150.00 3000.85 3000.85 4200.00 4200.00 3150.00 3000.85 3000.85 3000.85
## [4933] 5250.00 3000.85 3150.00 3150.00 4200.00 3150.00 3000.85 5250.00 3150.00
## [4942] 5250.00 3000.85 3150.00 5250.00 3000.85 3150.00 5250.00 3000.85 3150.00
## [4951] 3150.00 3000.85 4200.00 3000.85 3150.00 3000.85 3150.00 5250.00 4200.00
## [4960] 3000.85 3000.85 5250.00 3000.85 3000.85 3150.00 5250.00 3150.00 3000.85
## [4969] 3000.85 3000.85 3000.85 4200.00 3000.85 3000.85 3150.00 5250.00 3000.85
## [4978] 4200.00 4200.00 3000.85 3000.85 5250.00 4200.00 4200.00 3150.00 3000.85
## [4987] 3000.85 5250.00 3150.00 3000.85 5250.00 3150.00 3000.85 4200.00 4200.00
## [4996] 3000.85 3000.85 5250.00 3150.00 3000.85 3000.85 4200.00 3000.85 4200.00
## [5005] 3000.85 4200.00 4200.00 3000.85 3150.00 4200.00 5250.00 4200.00 4200.00
## [5014] 3150.00 3150.00 3000.85 3000.85 3000.85 3000.85 3000.85 3150.00 3150.00
## [5023] 3150.00 4200.00#Handling Outliers using Capping
cap_outliers <- function(x) {
Q1 <- quantile(x, 0.25, na.rm = TRUE)
Q3 <- quantile(x, 0.75, na.rm = TRUE)
IQR <- Q3 - Q1
lower <- Q1 - 1.5 * IQR
upper <- Q3 + 1.5 * IQR
x <- ifelse(x < lower, lower, x)
x <- ifelse(x > upper, upper, x)
return(x)
}
data$Price_capped <- NULL
# Apply capping
data$Price_capped <- cap_outliers(data$price)
price_capped_outliers <- boxplot.stats(data$Price_capped)$out
price_capped_outliers## numeric(0)Q4 — Are there any duplicate records in the dataset? Remove them.
# Q4: Detect duplicate records
num_duplicates <- sum(duplicated(data))
num_duplicates## [1] 0##Q5 — Apply Min–Max Normalization to All Numeric Variables
# Function for Min–Max
minmax <- function(x) {
return((x - min(x)) / (max(x) - min(x)))
}
# Identify numeric columns
num_cols <- sapply(data, is.numeric)
# Create a new normalized dataset
normalized_data <- data
normalized_data[num_cols] <- lapply(data[num_cols], minmax)# Show before and after for comparison
head(data[num_cols])## age quantity price Price_capped
## 1 28 5 1500.40 1500.400
## 2 21 3 1800.51 1800.510
## 3 20 1 300.08 300.080
## 4 66 5 3000.85 2932.625
## 5 53 4 60.60 60.600
## 6 28 5 1500.40 1500.400colnames(data)## [1] "invoice_no" "customer_id" "gender" "age"
## [5] "category" "quantity" "price" "payment_method"
## [9] "invoice_date" "shopping_mall" "Price_capped"head(normalized_data[num_cols])## age quantity price Price_capped
## 1 0.19607843 1.00 0.28507828 0.51075103
## 2 0.05882353 0.50 0.34229909 0.61326879
## 3 0.03921569 0.00 0.05621791 0.10072095
## 4 0.94117647 1.00 0.57116327 1.00000000
## 5 0.68627451 0.75 0.01055718 0.01891443
## 6 0.19607843 1.00 0.28507828 0.51075103Q6. Compute mean, median, mode, range, variance, and standard deviation for:
Quantity Price
# Function to compute mode
get_mode <- function(x) {
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]
}
# Quantity stats
cat("Mean:", mean(data$quantity), "\n")## Mean: 3.003429cat("Median:", median(data$quantity), "\n")## Median: 3cat("Mode:", get_mode(data$quantity), "\n")## Mode: 3cat("Variance:", var(data$quantity), "\n")## Variance: 1.99664cat("Standard Deviation:", sd(data$quantity), "\n\n")## Standard Deviation: 1.413025# Price stats
cat("Mean:", mean(data$price), "\n")## Mean: 689.2563cat("Median:", median(data$price), "\n")## Median: 203.3cat("Mode:", get_mode(data$price), "\n")## Mode: 600.16cat("Variance:", var(data$price), "\n")## Variance: 885828.4cat("Standard Deviation:", sd(data$price), "\n")## Standard Deviation: 941.1846Q7 — What is the distribution of Quantity purchased?
library(ggplot2)
ggplot(data, aes(x = quantity)) +
geom_histogram(bins = 5, fill = "skyblue", color = "black") +
theme_minimal() +
labs(
title = "Distribution of Quantity Purchased",
x = "Quantity",
y = "Frequency"
)
The distribution appears relatively uniform because each quantity range
has a similar count. There are no extreme values or large spikes,
suggesting consistent purchasing behavior in terms of quantity.
Q8 — Are there outliers in Quantity or Price?
ggplot(data, aes(y = quantity)) +
geom_boxplot(fill = "orange", alpha = 0.7, color = "black") +
labs(title = "Boxplot of Quantity",
y = "Quantity") +
theme_minimal()
library(ggplot2)
ggplot(data, aes(y = Price_capped)) +
geom_boxplot(fill = "skyblue", alpha = 0.7, color = "black") +
labs(title = "Boxplot of Price",
y = "Price") +
theme_minimal()
Q9. Does Quantity increase with Price?
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondata_summary <- data %>%
group_by(quantity) %>%
summarise(mean_price = mean(Price_capped))
ggplot(data_summary, aes(x = quantity, y = mean_price)) +
geom_line(size = 1.2) +
geom_point(size = 2) +
labs(title = "Mean Price per Quantity",
x = "Quantity",
y = "Average Price") +
theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
The chart shows that the average price increases steadily as quantity
increases. This indicates a strong, linear relationship: higher
quantities result in higher total prices.
cor(data$quantity, data$Price_capped, use = "complete.obs")## [1] 0.3521133Correlation Value Interpretation 0 to ±0.2 Very weak / no relationship ±0.21 to ±0.4 Weak ±0.41 to ±0.6 Moderate > ±0.6 Strong
Quantity increases slightly with price, but the relationship is weak. This means price is not a major factor driving the quantity purchased, and customers do not significantly change the quantity they buy based on price.
10. Create a correlation heatmap of numerical variables and interpret any strong relationships
library(dplyr)
library(reshape2)
numeric_df <- data %>% select(quantity, Price_capped)
numeric_df$quantity <- as.numeric(numeric_df$quantity)
numeric_df$Price_capped<- as.numeric(numeric_df$Price_capped)
#correlation matrix
corr_matrix <- cor(numeric_df, use = "complete.obs")
# Melt matrix for heatmap
corr_melt <- melt(corr_matrix)
# Plot heatmap
ggplot(corr_melt, aes(x = Var1, y = Var2, fill = value)) +
geom_tile(color = "black") +
geom_text(aes(label = round(value, 2)), size = 4) +
scale_fill_gradient2(low = "red", mid = "white", high = "green",
midpoint = 0, limit = c(-1,1)) +
labs(title = "Correlation Heatmap of Quantity and Price",
x = "Variables", y = "Variables", fill = "Correlation") +
theme_minimal()
A value close to +1 = strong positive correlation A value close to -1 =
strong negative correlation A value near 0 = no strong relationship
Expected result: near 0, meaning Quantity does not depend on Price.
PHASE 3 — STATISTICAL MODELING & MACHINE LEARNING
Q11. Build a simple linear regression model to predict Price from Quantity.
# Build linear regression model
model <- lm(Price_capped ~ quantity, data = data)
# model summary
summary(model)##
## Call:
## lm(formula = Price_capped ~ quantity, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1020.1 -603.5 -210.9 352.8 2283.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 52.960 5.558 9.529 <2e-16 ***
## quantity 198.650 1.674 118.642 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 746.1 on 99455 degrees of freedom
## Multiple R-squared: 0.124, Adjusted R-squared: 0.124
## F-statistic: 1.408e+04 on 1 and 99455 DF, p-value: < 2.2e-16Price increases by about ₹198 for every additional unit purchased, and the relationship is statistically significant. But the model only explains 12% of the variation in price, so quantity is not a strong predictor of price.
# Scatter plot
ggplot(data, aes(x = quantity, y = Price_capped)) +
geom_point(alpha = 0.7) +
geom_smooth(method = "lm", se = FALSE, color = "red") +
labs(title = "Quantity vs Price (Capped)",
x = "Quantity",
y = "Price (Capped)") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
The linear regression model shows a positive but weak relationship
between Quantity and Price. This means customers buying more items tend
to spend slightly more, but the increase is not very strong. The
R-squared value is low, indicating that Quantity alone is not a good
predictor of price, and other factors (e.g., product type, discount,
brand, store section) may influence pricing decisions. Therefore, the
model is not strong and is not suitable for accurate price
prediction.
ANOVA
Q12. Use ANOVA to test whether average spending (Price) differs between genders.
data$gender <- as.factor(data$gender)
data$Price_capped <- as.numeric(data$Price_capped)
# Perform ANOVA
anova_model <- aov(Price_capped ~ gender, data = data)
summary(anova_model)## Df Sum Sq Mean Sq F value Pr(>F)
## gender 1 2.926e+05 292561 0.46 0.497
## Residuals 99455 6.320e+10 635498# Group means
aggregate(Price_capped ~ gender, data = data, mean)## gender Price_capped
## 1 Female 648.1849
## 2 Male 651.6830The ANOVA results show that gender does not significantly affect the outcome. The p-value is 0.497, so there is no meaningful difference between male and female customers.
# Calculate group mean
gender_mean <- aggregate(Price_capped ~ gender, data, mean)
# Plot means
ggplot(gender_mean, aes(x = gender, y = Price_capped, fill = gender)) +
geom_bar(stat = "identity", alpha = 0.7, color = "black") +
labs(title = "Average Spending by Gender (Mean Comparison)",
x = "Gender",
y = "Average Price (Capped)") +
theme_minimal()
The ANOVA test compares mean Price_capped between Male and Female
customers. The resulting p-value is 0.497, which is greater than 0.05,
indicating that the difference in spending is not statistically
significant. Therefore, gender does not play a significant role in
predicting purchase spending.
Male and female customers spend nearly similar amounts on average. Marketing decisions should not be based purely on gender for pricing or promotions.
K-Means Clustering
Q13. Using Quantity and Price (normalized), perform K-Means clustering.
df_cluster <- data[, c("quantity", "Price_capped")]
# Normalize using scale()
df_scaled <- scale(df_cluster)set.seed(123)
k <- 3
# Replace if elbow suggests 2 or 4
final_model <- kmeans(df_scaled, centers = k, nstart = 25)
# Add cluster labels to original data
data$Cluster <- as.factor(final_model$cluster)library(ggplot2)
ggplot(data, aes(x = quantity, y = Price_capped, color = Cluster)) +
geom_point(alpha = 0.7, size = 3) +
labs(title = "K-Means Clustering Result (Quantity vs Price)",
x = "Quantity",
y = "Price (Capped)") +
theme_minimal()
aggregate(df_cluster, by = list(Cluster = data$Cluster), mean)## Cluster quantity Price_capped
## 1 1 1.487835 300.0806
## 2 2 4.206962 1842.7543
## 3 3 3.808358 245.4082Cluster 1 These customers purchase items at lower price ranges, typically below 1000. They represent low-spending customers, even if they buy different quantities.
Cluster 2 These customers consistently fall in the higher price range, often above 2000. They represent high-value transactions—either expensive products or bulk purchases.
Cluster 3 This group lies in the mid-price segment, usually between 200 and 800. They represent medium-spending customers with moderate price levels.
KNN Classification
Q14. Use KNN to predict customer Gender based on numeric variables.
## Load libraries
library(class)
library(caret)## Loading required package: latticelibrary(e1071)##
## Attaching package: 'e1071'## The following object is masked from 'package:ggplot2':
##
## element# STEP 1: Prepare dataset (only needed columns)
df_knn <- data[, c("gender", "quantity", "Price_capped")]
# STEP 2: Remove rows with missing values
df_knn <- na.omit(df_knn)
# STEP 3: Convert target variable to factor
df_knn$gender <- as.factor(df_knn$gender)
# STEP 4: Train-Test Split (80%-20%)
set.seed(123)
index <- createDataPartition(df_knn$gender, p = 0.8, list = FALSE)
train <- df_knn[index, ]
test <- df_knn[-index, ]
# STEP 5: Scale only numeric columns inside train/test separately
#train_scaled <- scale(train[, c("quantity", "Price_capped")])
#test_scaled <- scale(test[, c("quantity", "Price_capped")])
train_scaled <- scale(train[, c("quantity", "Price_capped")]) + jitter(rep(0, nrow(train)), amount = 0.0001)
test_scaled <- scale(test[, c("quantity", "Price_capped")]) + jitter(rep(0, nrow(test)), amount = 0.0001)
# STEP 6: Extract labels
train_labels <- train$gender
test_labels <- test$gender
# STEP 7: Select k (sqrt rule)
k <- 5
# STEP 8: Run KNN
pred <- knn(train = train_scaled,
test = test_scaled,
cl = train_labels,
k = k)
# STEP 9: Model evaluation
confusionMatrix(pred, test_labels)## Confusion Matrix and Statistics
##
## Reference
## Prediction Female Male
## Female 10304 6983
## Male 1592 1012
##
## Accuracy : 0.5689
## 95% CI : (0.562, 0.5758)
## No Information Rate : 0.5981
## P-Value [Acc > NIR] : 1
##
## Kappa : -0.0081
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.8662
## Specificity : 0.1266
## Pos Pred Value : 0.5961
## Neg Pred Value : 0.3886
## Prevalence : 0.5981
## Detection Rate : 0.5180
## Detection Prevalence : 0.8691
## Balanced Accuracy : 0.4964
##
## 'Positive' Class : Female
## install.packages("kknn")## renv was unable to query available packages from the following repositories:
## - # https://packagemanager.posit.co/cran/latest/src/contrib --------------------
## error downloading 'https://packagemanager.posit.co/cran/latest/src/contrib/PACKAGES.rds' [error code 6]
## error downloading 'https://packagemanager.posit.co/cran/latest/src/contrib/PACKAGES.gz' [error code 6]
## error downloading 'https://packagemanager.posit.co/cran/latest/src/contrib/PACKAGES' [error code 6]
##
##
## The following package(s) will be installed:
## - kknn [1.4.1]
## These packages will be installed into "C:/Users/VICTUS/OneDrive/Desktop/Data science/R33/renv/library/windows/R-4.4/x86_64-w64-mingw32".
##
## # Installing packages --------------------------------------------------------
## - Installing kknn ... OK [linked from cache]
## Successfully installed 1 package in 0.11 seconds.library(kknn)##
## Attaching package: 'kknn'## The following object is masked from 'package:caret':
##
## contr.dummymodel <- kknn(gender ~ quantity + Price_capped, train, test, k = 5)
pred <- fitted(model)
confusionMatrix(pred, test_labels)## Confusion Matrix and Statistics
##
## Reference
## Prediction Female Male
## Female 7305 4852
## Male 4591 3143
##
## Accuracy : 0.5253
## 95% CI : (0.5183, 0.5322)
## No Information Rate : 0.5981
## P-Value [Acc > NIR] : 1.00000
##
## Kappa : 0.0072
##
## Mcnemar's Test P-Value : 0.00746
##
## Sensitivity : 0.6141
## Specificity : 0.3931
## Pos Pred Value : 0.6009
## Neg Pred Value : 0.4064
## Prevalence : 0.5981
## Detection Rate : 0.3673
## Detection Prevalence : 0.6112
## Balanced Accuracy : 0.5036
##
## 'Positive' Class : Female
## A K-Nearest Neighbors (KNN) classification model was built using Quantity and Price_capped as independent variables to predict customer Gender. The dataset was split into 80% training and 20% testing, and feature scaling was applied before model training. The model performance was evaluated using confusion matrix and accuracy score.
Accuracy = 52.53% This means the model correctly predicts gender only a little better than random guessing (50%).
PHASE 4 — ASSOCIATION RULE MINING & INSIGHTS
Q15. Convert transaction data into a transaction format (CustomerID → items).
library(arules)## Loading required package: Matrix##
## Attaching package: 'arules'## The following object is masked from 'package:dplyr':
##
## recode## The following objects are masked from 'package:base':
##
## abbreviate, write# Convert to list: Each CustomerID → vector of items purchased
transaction_list <- split(data$category, data$customer_id)
# Convert list to transactions object
transactions <- as(transaction_list, "transactions")
# Summary of transactions
summary(transactions)## transactions as itemMatrix in sparse format with
## 99457 rows (elements/itemsets/transactions) and
## 8 columns (items) and a density of 0.125
##
## most frequent items:
## Clothing Cosmetics Food & Beverage Toys Shoes
## 34487 15097 14776 10087 10034
## (Other)
## 14976
##
## element (itemset/transaction) length distribution:
## sizes
## 1
## 99457
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 1 1 1 1 1
##
## includes extended item information - examples:
## labels
## 1 Books
## 2 Clothing
## 3 Cosmetics
##
## includes extended transaction information - examples:
## transactionID
## 1 C100004
## 2 C100005
## 3 C100006# View first few transactions
inspect(head(transactions, 5))## items transactionID
## [1] {Clothing} C100004
## [2] {Shoes} C100005
## [3] {Toys} C100006
## [4] {Food & Beverage} C100012
## [5] {Toys} C100019The dataset was transformed into a transaction format by grouping items purchased by each customer. This structure is essential for performing market basket analysis using the Apriori algorithm.
Q16 — Generate Association Rules Using the Apriori Algorithm
library(arules)
# Run Apriori Algorithm
rules <- apriori(
transactions,
parameter = list(
supp = 0.05, # minimum support = 1%
conf = 0.3 # minimum confidence = 50%
)
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.3 0.1 1 none FALSE TRUE 5 0.05 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 4972
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[8 item(s), 99457 transaction(s)] done [0.01s].
## sorting and recoding items ... [8 item(s)] done [0.00s].
## creating transaction tree ... done [0.03s].
## checking subsets of size 1 done [0.00s].
## writing ... [1 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].# View summary of rules
summary(rules)## set of 1 rules
##
## rule length distribution (lhs + rhs):sizes
## 1
## 1
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 1 1 1 1 1
##
## summary of quality measures:
## support confidence coverage lift count
## Min. :0.3468 Min. :0.3468 Min. :1 Min. :1 Min. :34487
## 1st Qu.:0.3468 1st Qu.:0.3468 1st Qu.:1 1st Qu.:1 1st Qu.:34487
## Median :0.3468 Median :0.3468 Median :1 Median :1 Median :34487
## Mean :0.3468 Mean :0.3468 Mean :1 Mean :1 Mean :34487
## 3rd Qu.:0.3468 3rd Qu.:0.3468 3rd Qu.:1 3rd Qu.:1 3rd Qu.:34487
## Max. :0.3468 Max. :0.3468 Max. :1 Max. :1 Max. :34487
##
## mining info:
## data ntransactions support confidence
## transactions 99457 0.05 0.3
## call
## apriori(data = transactions, parameter = list(supp = 0.05, conf = 0.3))# Inspect first few rules
inspect(head(rules, 10))## lhs rhs support confidence coverage lift count
## [1] {} => {Clothing} 0.3467529 0.3467529 1 1 34487length(rules)## [1] 1“Association Rule Mining was performed using the Apriori algorithm. After converting the dataset into transaction format, the algorithm produced only one rule:
{} → {Clothing}
This rule is not a meaningful association; it simply indicates that Clothing appears in 34.67% of all transactions. The main reason for the lack of valid rules is that the dataset contains single-item transactions, meaning that customers usually purchase only one product category at a time. Since Apriori requires multiple items within a single basket to form associations, the algorithm could not generate any useful product–product relationships.
Therefore, association rule mining is not suitable for this dataset without restructuring or combining transactions.”
Q17. What are the strongest rules?
Only one rule was generated by the Apriori algorithm, but it is not a true association rule. It simply indicates that the “Clothing” category is the most frequent product category in the dataset.
Because the dataset contains single-item purchases, the algorithm cannot detect any meaningful relationships between products. As a result, there are no strong or actionable association rules in this dataset.
# Select numeric features for clustering
X <- data %>% select(quantity,Price_capped )
# Scale the data (important for k-means)
X_scaled <- scale(X)
# Run K-means with 3 clusters
set.seed(123)
kmeans_model <- kmeans(X_scaled, centers = 3, nstart = 25)
# Add cluster labels to dataframe
data$cluster <- kmeans_model$cluster
# Compute average spending per cluster
cluster_summary <- data %>%
group_by(cluster) %>%
summarise(
avg_total_sales = mean(Price_capped),
count = n()
) %>%
arrange(desc(avg_total_sales))
print(cluster_summary)## # A tibble: 3 × 3
## cluster avg_total_sales count
## <int> <dbl> <int>
## 1 2 1843. 23845
## 2 1 300. 38595
## 3 3 245. 37017Q18. Which customer segment (via clustering) spends the most?
library(dplyr)
library(ggplot2)
library(ggrepel)
data$Cluster <- as.factor(data$Cluster)
hulls <- data %>%
group_by(Cluster) %>%
slice(chull(quantity, Price_capped))
center <- data %>%
group_by(Cluster) %>%
summarise(
quantity = mean(quantity),
Price_capped = mean(Price_capped)
)
ggplot(data, aes(x = quantity, y = Price_capped, color = Cluster)) +
geom_point(size = 3) +
# convex hull boundaries
geom_polygon(
data = hulls,
aes(x = quantity, y = Price_capped, fill = Cluster, group = Cluster),
alpha = 0.2,
color = "black"
) +
# centroids
geom_point(
data = center,
aes(x = quantity, y = Price_capped),
color = "black",
size = 7,
shape = 8
) +
# centroid labels
geom_text_repel(
data = center,
aes(x = quantity, y = Price_capped,
label = paste0("cluster ", Cluster, "\n(",
round(quantity,1), ", ", round(Price_capped,1), ")")),
color = "black",
fontface = "bold",
size = 4
) +
labs(
title = "K-Means Clustering with Boundaries and Centroids",
x = "Quantity",
y = "Price (Capped)"
) +
theme_minimal()
Q19. Which age group buys the highest-priced products?
library(dplyr)
library(ggplot2)
# ---- Step 1: Create age groups ----
data <- data %>%
mutate(age_group = case_when(
age < 20 ~ "Under 20",
age >= 20 & age < 30 ~ "20-29",
age >= 30 & age < 40 ~ "30-39",
age >= 40 & age < 50 ~ "40-49",
age >= 50 & age < 60 ~ "50-59",
age >= 60 ~ "60+",
TRUE ~ "Unknown"
))
# ---- Step 2: Calculate average price per age group ----
age_price_summary <- data %>%
group_by(age_group) %>%
summarise(
avg_price = mean(Price_capped, na.rm = TRUE),
count = n()
) %>%
arrange(desc(avg_price))
print(age_price_summary)## # A tibble: 6 × 3
## age_group avg_price count
## <chr> <dbl> <int>
## 1 40-49 658. 19153
## 2 20-29 653. 19263
## 3 60+ 650. 19043
## 4 30-39 646. 19287
## 5 50-59 645. 18931
## 6 Under 20 633. 3780ggplot(age_price_summary, aes(x = reorder(age_group, avg_price), y = avg_price)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(
title = "Average Product Price by Age Group",
x = "Age Group",
y = "Average Price (Capped)"
) +
theme_minimal()
## Q20. What time of the year/month/week shows the highest purchase
activity?
library(dplyr)
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:arules':
##
## intersect, setdiff, union## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(ggplot2)
# Convert invoice_date to date format
data$invoice_date <- as.Date(data$invoice_date)
# Extract components
data <- data %>%
mutate(
year = year(invoice_date),
month = month(invoice_date, label = TRUE, abbr = TRUE),
week = week(invoice_date),
weekday = weekdays(invoice_date)
)
# ---- Monthly purchase activity ----
monthly_activity <- data %>%
group_by(year,month) %>%
summarise(transactions = n()) %>%
arrange(desc(transactions))## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.print(monthly_activity)## # A tibble: 365 × 3
## # Groups: year [31]
## year month transactions
## <dbl> <ord> <int>
## 1 7 Jan 426
## 2 6 Jan 419
## 3 18 Feb 409
## 4 5 Feb 408
## 5 13 Feb 405
## 6 4 Mar 401
## 7 6 Mar 401
## 8 24 Feb 400
## 9 28 Jan 400
## 10 26 Feb 399
## # ℹ 355 more rows# ---- Weekly purchase activity ----
weekly_activity <- data %>%
group_by(year, week) %>%
summarise(transactions = n()) %>%
arrange(desc(transactions))## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.print(weekly_activity)## # A tibble: 365 × 3
## # Groups: year [31]
## year week transactions
## <dbl> <dbl> <int>
## 1 7 3 426
## 2 6 3 419
## 3 18 8 409
## 4 5 8 408
## 5 13 8 405
## 6 4 12 401
## 7 6 12 401
## 8 24 8 400
## 9 28 3 400
## 10 26 8 399
## # ℹ 355 more rows# ---- Day of week activity ----
weekday_activity <- data %>%
group_by(weekday) %>%
summarise(transactions = n()) %>%
arrange(desc(transactions))
print(weekday_activity)## # A tibble: 7 × 2
## weekday transactions
## <chr> <int>
## 1 Saturday 14616
## 2 Sunday 14246
## 3 Tuesday 14232
## 4 Monday 14184
## 5 Thursday 14130
## 6 Wednesday 14046
## 7 Friday 14003#purchase by month
ggplot(monthly_activity, aes(x = month, y = transactions, group = year, fill = month)) +
geom_col() +
labs(title = "Monthly Purchase Activity",
x = "Month", y = "Number of Transactions") +
theme_minimal()
#perchage by day
ggplot(weekday_activity, aes(x = reorder(weekday, transactions), y = transactions)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(title = "Purchase Activity by Day of Week",
x = "Weekday", y = "Transactions") +
theme_minimal()
Conclusion
The analysis shows that middle-aged customers (40–59), especially those in Cluster 3, are the highest spenders and represent the most valuable customer segment. Gender has no major effect on spending, and quantity is not strongly related to price. Clear monthly and weekly purchase patterns exist, providing opportunities for better forecasting, inventory planning, and targeted promotions. Although limited by single-item transactions, the insights offer strong guidance for improving marketing and operational decisions