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DAIICT PC503 Python Project Spam Detection Toolbox

In the following, we will design a SPAM email detection toolbox. First we will compose the emails, then we will detect spam. Do not hardcode values/logic when not specified.

Part A

  1. Create a text file named student_name_list.txt of the names of 10 PC503 students manually, keeping one name in each line. Note: this is the only file that is to be created manually. Files in the subsequent questions must not be created manually.

  2. Write a Python code main_data.py to do the following tasks. Each task must be written in a function with a suitable function name. Depending on the task, the function may or may not return anything.

  3. Read the file About-DAIICT.txt and create four new files named History.txt, Environment.txt, Recognition.txt and Accreditation.txt by inserting the content of each section as given in About-DAIICT.txt.

  4. Create a list named student_name_list of the 10 names by reading the text file student_name_list.txt.

  5. Randomly generate 15 files with file names: email-1.txt to email-20.txt. So, the name of the 13th file need not be email-13.txt, it can also be email-19.txt or email-5.txt. Content of each file (10 lines) should be as follows:
    a) First line should have time, date, month, year, email Id in the following format:
    Received at 17:23 hrs on October 7, 2018 from [email protected]
    All entries must be randomly inserted and email Ids can be created from random elements of student_name_list. Obviously, there will be at least one or more files containing the same email Id since the number of students is 10 (<15).
    b) Second line should be blank.
    c) Third line to the tenth line must be the lines from the four above mentioned text files in random order such that (i) there is at least one line from each file and (ii) the lines taken from History.txt can include only one of the four sentences containing “first wave”, “second wave”, “third wave”, “fourth wave”. There is no restriction on other sentences in this text file.

  6. Randomly generate 4 more files using the above mentioned logic with two modifications:
    a) File name should be the unused numbers between 1 to 20.
    b) Email Ids must be taken from the following list: email_ids =[[email protected], [email protected], [email protected], [email protected]].

  7. Randomly generate 1 more file using the above mentioned logic with three modifications:
    a) File name should be the unused number between 1 to 20.
    b) Email Id is [email protected]
    c) The lines taken from History.txt cannot include any one of the four sentences containing “first wave”, “second wave”, “third wave”, “fourth wave”. There is no restriction on other sentences in this text file.

Part B

  1. Write a Python code main_test.py to do the following tasks. Each task must be written in a function with a suitable function name. Depending on the task, the function may or may not return anything.

  2. Print the email Ids whose files do not have any “wave” information. In the current exercise, there is only one such entry.

  3. The users not having daiict email Id or without any “wave” information must be spammed. Rename the corresponding files as spam1.txt, spam2.txt, … and update their contents by adding a line at the beginning: “This email has been categorized as spam”.

  4. Create ordered_names.txt containing the user names (non-spam type) in the descending order of date-time etc., i.e. most recent first. One name in each line. Keep duplicate names.

  5. Create ordered_names_wave1.txt containing the user names (non-spam type) belonging to the “first wave” group in the descending order of date-time etc., i.e. most recent first. No duplicate names and one name in each line. Similarly create files for the “second wave”, “third wave”, “fourth wave” groups.

  6. Plot the number of users in each group and number of spam emails using a bar chart.

Note: Randomize the codes so that every time they are executed, different output is expected.

How to Run?

  1. To generate random data run "main_data.py" using "python3 main_data.py"
  2. To get all spam email and graphs run "test_data.py"

Note: Before you run 2nd line you need to atleast run 1st line once.