Mathematical modeling

Problem: We all hate seeing spam in our inbox, but what we hate even more is when real emails wind up in the spam folder. Suppose that each time we see a spam email in our inbox, we get 1 happiness unit more sad each time a real email winds up in the spam folder, we get 5 happiness units more sad. Our happiness is unaffected by correct classifications.

##### How much happiness do we lose using the Additive Model? How much happiness do we lose using the Logistic model? Report your answers as happiness loss-per-email-received. (Use the test data for your calculations)

Data description
The test data has 58 columns 57 are independent variables (wordcount and character count for example) is.spam is the dependent variable. 1= is spam. 0= no spam there is a total of 1536 entries. 0= 941 and 1= 595

Column names are as follows ( I did not find any information relevant in the test data that can help with the problem)

[1] "word_freq_make" "word_freq_address" "word_freq_all" "word_freq_3d" [5] "word_freq_our" "word_freq_over" "word_freq_remove" "word_freq_internet" [9] "word_freq_order" "word_freq_mail" "word_freq_receive" "word_freq_will" [13] "word_freq_people" "word_freq_report" "word_freq_addresses" "word_freq_free" [17] "word_freq_business" "word_freq_email" "word_freq_you" "word_freq_credit" [21] "word_freq_your" "word_freq_font" "word_freq_000" "word_freq_money" [25] "word_freq_hp" "word_freq_hpl" "word_freq_george" "word_freq_650" [29] "word_freq_lab" "word_freq_labs" "word_freq_telnet" "word_freq_857" [33] "word_freq_data" "word_freq_415" "word_freq_85" "word_freq_technology" [37] "word_freq_1999" "word_freq_parts" "word_freq_pm" "word_freq_direct" [41] "word_freq_cs" "word_freq_meeting" "word_freq_original" "word_freq_project" [45] "word_freq_re" "word_freq_edu" "word_freq_table" "word_freq_conference" [49] "char_freq_semicolon" "char_freq_parens" "char_freq_bracket" "char_freq_exclamation" [53] "char_freq_dollar" "char_freq_pound" "capital_run_length_average" "capital_run_length_longest" [57] "capital_run_length_total" "is.spam"

The original data set if needed can be find here https://archive.ics.uci.edu/ml/datasets/spambase

I need to use functions gam() and glm() in R programming 

  • Bounty is too low

  • Sorry I can't increase it more..Please help me. it is the only question left out of 35. I have tried everything

  • I agree with Schwartstack, the bouty is low. This may take more than an hour to answer.

  • Could you rewrite this please: "Suppose that each time we see a spam email in our inbox, we get 1 happiness unit more sad each time a real email winds up in the spam folder, we get 5 happiness units more sad."

  • The original data set has 4601 entries.

  • Also, should we aim to train the 'best' possible models or just use the default ones? Should we do feature selection or cross validation or just train a simple model with all observations?

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