Here my dataset is pd and i have split it into training and testing data as pd_train1 and pd_train2
sku national_inv lead_time in_transit_qty forecast_3_month forecast_6_month
1 3921548 8 12 0 0 0
2 3191009 83 2 33 157 377
3 2935810 8 4 0 0 0
4 2205847 31 4 63 70 160
5 4953497 3 12 0 0 0
6 2286884 0 8 0 0 0
forecast_9_month sales_1_month sales_3_month sales_6_month sales_9_month min_bank
1 0 1 1 2 5 2
2 603 44 98 148 156 53
3 0 0 0 1 1 0
4 223 27 90 164 219 0
5 0 0 0 0 0 0
6 0 0 0 0 0 0
potential_issue pieces_past_due perf_6_month_avg perf_12_month_avg local_bo_qty
1 0 0 0.63 0.75 0
2 0 0 0.68 0.66 0
3 0 0 0.73 0.78 0
4 0 0 0.73 0.78 0
5 0 0 0.81 0.74 0
6 0 0 0.91 0.96 0
deck_risk oe_constraint ppap_risk stop_auto_buy rev_stop went_on_backorder data
1 0 0 0 1 0 No train
2 0 0 0 1 0 No train
3 0 0 0 1 0 No train
4 0 0 1 1 0 No train
5 0 0 0 1 0 No train
6 0 0 0 1 0 No train
I wanted to create a lm model for my training data pd_train1
But i am getting this error as below:
> fit=lm(went_on_backorder~.,data=pd_train1)
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
NA/NaN/Inf in 'y'
In addition: Warning message:
In storage.mode(v) <- "double" : NAs introduced by coercion
I tried searching for infinite values:
sapply(pd_train1, function(x) sum(is.infinite(x)))
sku national_inv lead_time in_transit_qty forecast_3_month
0 0 0 0 0
forecast_6_month forecast_9_month sales_1_month sales_3_month sales_6_month
0 0 0 0 0
sales_9_month min_bank potential_issue pieces_past_due perf_6_month_avg
0 0 0 0 0
perf_12_month_avg local_bo_qty deck_risk oe_constraint ppap_risk
0 0 0 0 0
stop_auto_buy rev_stop went_on_backorder data
0 0 0 0
And also for NA/NaN values in my training data on which i want to make linear model
sku national_inv lead_time in_transit_qty forecast_3_month
0 0 0 0 0
forecast_6_month forecast_9_month sales_1_month sales_3_month sales_6_month
0 0 0 0 0
sales_9_month min_bank potential_issue pieces_past_due perf_6_month_avg
0 0 0 0 0
perf_12_month_avg local_bo_qty deck_risk oe_constraint ppap_risk
0 0 0 0 0
stop_auto_buy rev_stop went_on_backorder
0 0 0
Inf %in% pd_train1$went_on_backorder
1] FALSE
NaN %in% pd_test$went_on_backorder
1] FALSE
Henceforth I am not able to get the NA/NaN/Inf values in my dataset
Can someone help me understand why is this throwing an error, please?
Here went_on_backorder is my target variable.
The column went_on_backorder is a factor. Linear regression requires a numeric response variable.
To use logistic regression, use glm in base R or a package such as vgam. Here's a brief example:
pd_train1 <- data.frame('went_on_backorder' = c('No','Yes','Yes'), 'lead_time' = 1:3)
model <- glm(went_on_backorder ~ ., data = pd_train1, family = 'binomial')
And you can predict your classes:
predict(model, newdata = data.frame('lead_time' = c(0,1,2.5,3.5)), type = "response")