I extracted some other features from my dataset regarding punctuation, capital letters, upper case words. I got these value:
looking at the correlation with my target variable (1=spam, 0=not spam), using .corr() in python. BT stands for binary text, e.g., and BS stands for binary summary, where I assign 1 or 0 based on the presence of a capital letter in the text/summary, or upper case word, or...
Do you think that features like these can be useful in model building? I cannot see very strong correlations, but I would like to determine if an email can be spam or not based also on features like these (number of character/text length; presence of !, upper case words,....).
I have around 1000 emails, but only 50 are spam (maybe too small to extract useful information). However, I had to extract these information, so it is a new dataset, built on my own, so I could not get many more spam emails (and I would like to not use datasets from kaggle, for instance).
What do you think?