Thank you so much @technocrat, you have been so helpful. I have rewritten the code and I think it makes sense now. My goal is to see those customers whose status are either 1 or 0. Since my RAM wasn't enough to perform the assigned task, I had to resort to removing some columns that slowed down my machine.

**"Then, if **`pred_value`

is supposed to be a predicted value of `status`

in a logistic model, you would need to dig out the log likelihood. (See my post here , which is based on the standard text.) If, on the other hand, it's supposed to be the *estimates* of the independent variables, those need to be extracted from the model output."

can you buttress further on the log-likelihood part and the estimates?

Below is my new codebase:

```
#remove unwanted columns
model_input_df <- ml[, c(-1,-2,-3,-4,-5,-6,-7,-9)]
glimpse(model_input_df)
#Preliminary casting to the appropriate data type.
model_input_df$Status <- as.factor(model_input_df$Status)
model_input_df$Feeder <- as.character(model_input_df$Feeder)
model_input_df$group_cons <- as.factor(model_input_df$group_cons)
#...........................................................................
#...........................................................................
#BUILDING THE MACHINE LEARNING MODEL/partitioning the data
intrain<- createDataPartition(model_input_df$Status,p=0.75,list=FALSE)
set.seed(2017)
training<- model_input_df[intrain,]
testing<- model_input_df[-intrain,]
#memory.limit(size = 56000)
#............................................................................
#Confirm the splitting is correct:
dim(training); dim(testing)
#Fitting the Logistic Regression Model:
LogModel <- glm(Status ~ .,data=training,family=binomial, maxit=100)
print(summary(LogModel))
#...............................................................................
#colnames(model_input_df)
#LogModel <- c(1, 2, 3, 4, 5,6,7,8,9)
# binding them together using rbind function of Base R
#final_df <- rbind(ml[, c(-1, -2,-3,-4,-5,-6,-7)], "pred_values" = LogModel)
#head(final_df)
#saveRDS(LogModel, "logmodel.rds")
#..............................................Adding Acc No back.....................
#Feature Analysis:
anova(LogModel, test="Chisq")
head(testing)
#Assessing the predictive ability of the Logistic Regression model
#testing$Status <- as.character(testing$Status)
#testing$Status[testing$Status=="0"] <- "0"
#testing$Status[testing$Status=="1"] <- "1"
fitted.results <- predict(LogModel,newdata=testing,type='response')
fitted.results <- ifelse(fitted.results > 0.5,1,0)
misClasificError <- mean(fitted.results != testing$Status)
print(paste('Logistic Regression Accuracy',1-misClasificError))
#class(testing$Average.Consumption)
final_df <- rbind(ml[, c(1,2,3,4,5,6,7,9)],"Pred_values"=fitted.results)
```

But it throws in a warning message

Warning messages:

```
1: In `[<-.factor`(`*tmp*`, ri, value = 0) :
invalid factor level, NA generated
2: In `[<-.factor`(`*tmp*`, ri, value = 0) :
invalid factor level, NA generated
3: In `[<-.factor`(`*tmp*`, ri, value = 0) :
invalid factor level, NA generated
4: In `[<-.factor`(`*tmp*`, ri, value = 0) :
invalid factor level, NA generated
5: In `[<-.factor`(`*tmp*`, ri, value = 0) :
invalid factor level, NA generated
6: In `[<-.factor`(`*tmp*`, ri, value = 0) :
invalid factor level, NA generated
7: In `[<-.factor`(`*tmp*`, ri, value = 0) :
invalid factor level, NA generated
```

I just want to merge the predicted outcome with the list of customers in this case.