@Rakesh_Kumar1 wrote:
Hi, I'm a newb to Data Science. I'm trying to improve my Mulitple regression model but running out of ideas.
Here's the structure of Blackfriday training data.
str(train)
'data.frame': 550068 obs. of 10 variables:
$ Gender : Factor w/ 2 levels "F","M": 1 1 1 1 2 2 2 2 2 2 ...
$ Age : Factor w/ 7 levels "0-17","18-25",..: 1 1 1 1 7 3 5 5 5 3 ...
$ Occupation : Factor w/ 21 levels "0","1","2","3",..: 11 11 11 11 17 16 8 8 8 21 ...
$ City_Category : Factor w/ 3 levels "A","B","C": 1 1 1 1 3 1 2 2 2 1 ...
$ Stay_In_Current_City_Years: Factor w/ 5 levels "0","1","2","3",..: 3 3 3 3 5 4 3 3 3 2 ...
$ Marital_Status : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 2 2 2 ...
$ Product_Category_1 : Factor w/ 20 levels "1","2","3","4",..: 3 1 12 12 8 1 1 1 1 8 ...
$ Product_Category_2 : Factor w/ 18 levels "0","2","3","4",..: 1 6 1 14 1 2 8 15 16 1 ...
$ Product_Category_3 : Factor w/ 16 levels "0","3","4","5",..: 1 12 1 1 1 1 15 1 1 1 ...
$ Purchase : int 8370 15200 1422 1057 7969 15227 19215 15854 15686 7871 ...Here's my simple model:
fit1 <- lm(Purchase ~.,data = train)
Since all independent variables are Categorical, not sure how to go about in improving this model, considering data set to be a little large and Lot of categorical levels.Please help!
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