load_data.R

bf <- read.csv("dataset.csv", header=T)

bf$Occupation <- factor(bf$Occupation, levels = c('0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20'))
bf$Marital_Status <- factor(bf$Marital_Status, levels = c('0', '1'))
bf$Product_Category_1 <- factor(bf$Product_Category_1, levels = c('1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18'))
bf$Product_Category_2 <- factor(bf$Product_Category_2, levels = c('2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18'))
bf$Product_Category_3 <- factor(bf$Product_Category_3, levels = c('3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18'))
bf$Age <- factor(bf$Age, levels = c('0-17', '18-25', '26-35', '36-45', '46-50', '51-55', '55+'), ordered = T)
bf$Stay_In_Current_City_Years <- factor(bf$Stay_In_Current_City_Years, levels = c('0', '1', '2', '3', '4+'), ordered = T)

purchase_sum <- aggregate(
  bf$Purchase,
  list(User_ID = bf$User_ID, Gender = bf$Gender, City_Category = bf$City_Category, Age = bf$Age, Occupation = bf$Occupation, Stay_In_Current_City_Years = bf$Stay_In_Current_City_Years, Marital_Status = bf$Marital_Status),
  sum
)