Zipfian Final Project Spring 2014

Jeeves is an email natural language classifier that finds messages that need a meeting location defined.

I built this as my Zipfian final project because I want my computer to do more things for me. Why not when it has all this data on me. I made a long wish list of items and then focused on getting my computer to read emails and classify the ones that meeting location defined. Then it sends me a text if an email is classified as true.

If there was enough time I wanted the program to take the next step of finding a couple recommendations on locations. Just getting the classifier working was plenty for the two weeks we had for the project.

This project is similar to spam filters where a false positive (getting texts on email incorrectly classified as true) is more acceptible than a false negative (missing an email that needs a location)

• Handle_email.py, common.py & twilio_views.py in the app folder are the main files that run the application.

  • Handle_email:

    • Main file that runs the full project pipeline from getting the email to sending a text
    • Call check_email function to get the program to check for new gmails
    • There is a pickle file of the last time that the email was checked that will be upated after new email is found
    • If there are new emails then the data is parsed out and cleaned
    • The modified tf-idf vectorizer is unpickled and applied to the email message to generate features
    • The modified logistic regression model is unpickled and the email features are run through the logistic regression for a prediction
    • If the prediction is true then a message is created
    • The message is sent through the jeeves_notifications function under the Twilio views
    • Also the message is passed to the unix say command so my computer says that a new email needs a meeting location defined
      • See a screen capture of the project in action

  • Common:

    • Holds common functions that are used for the project pipeline and for building out and testing the vectorizer and classifier
    • Pickle functions
    • API call to get new gmails
    • Function to clean data (strip whitespace and characters that are not UTF8, etc.)
    • Store data in Postgres
    • Put data into Pandas dataframe
    • Generate dataset splits for X, y and cross validation

  • Twilio_views:

    • Passes the message through the Twilio API to turn it into a text message on my phone

Check out nyghtowl.io for my blog posts on my progress with developing Jeeves:

• Starting the project
• Completing the project pipeline
• Fine tuning

• Try Porter for stemming

• Continue to code for producing the model:

  • Grid Search again to improve parameters
  • plot error rate and learning curves (regularization sprint)
  • explore other ways to apply k-fold

• Other feature ideas / customization

  • Make date in body of text more informatitve
  • Python NLP - Regex library
  • length of thread (3+)

  • email from an email address / new or not

  • email address in contacts or on Linkedin...

• Closing loop with a cronjob to automate email checking

• Use Ec2, picloud(install package - pass function and arguments and it goes up to EC2) to run models / esp grid search

• Continue to look at dimensionality reduction approaches

• Turn pipeline sections into classes/objects?

• Take model to another level

  • use partial predict so can take in feedback on new results and if they are correct (Adam gave this idea)
  • make the vectorizer/classifier result one feature and then add in other engineered features and feed through another classifer

Raw data stored in Postgres DB

~1000+ emails total ~120 emails meet target conditions (may be smaller based on thread emails)

Raw Data:

• Message ID (message_id) = string / primary key
• Thread ID (thread_id) = string
• To (to_email) = string
• From (from_email) = string
• CC (cc) = string
• Date Sent (date) = timestamp
• Subject (subject) = string
• Starred Email (starred) = boolean (true meanss its starred)
• Message Body (body) = text
• Message Subject & Body (sub_body) = text
• Email Owner (email_owner) = string (email source)
• Box (email box) = string
• Needs Location (target) = boolean (true needs a meeting location and based on labels 'Jeeves' & 'Starred')

• Any bugs about Markdown Preview please feel free to report here
• And you are welcome to fork and submit pullrequests

Copyright (c) 2014 Nyghtowl