<jupyter_start><jupyter_text>Tip: This is R notebook<jupyter_code># Set plot size
library("repr")
options(repr.plot.width=8, repr.plot.height=3)<jupyter_output><empty_output><jupyter_text>Problem StatementA retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summary of various customers for selected high volume products from last month.Now, they want to build a model to predict the purchase amount of customer against various products which will help them to create personalized offer for customers against different products.For more detail: https://datahack.analyticsvidhya.com/contest/black-friday/<jupyter_code>train <- read.csv("train.csv")
test <- read.csv("test.csv")
summary(train)
str(train)
summary(test)
head(train)
colnames(train)
cat("\n", "Age", "\n", table(train$Age), "\n")
cat("\n","Occupation","\n", table(train$Occupation), "\n")
cat("\n","City_Category","\n", table(train$City_Category), "\n")<jupyter_output>Age 
 15102 99660 219587 110013 45701 38501 21504 

 Occupation 
 69638 47426 26588 17650 72308 12177 20355 59133 1546 6291 12930 11586 31179 7728 27309 12165 25371 40043 6622 8461 33562 

 City_Category 
 147720 231173 171175
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