Defining the Problem: How to process over 37,000 unread emails with AI and modern data science techniques. | by Christopher Tavolazzi | Jun, 2024

Welcome once more, fellow information adventurers and e-mail procrastinators! In case you occur to’re merely turning into a member of us, I’m the person with 37,000 unread emails who’s decided to check information science as a way to procrastinate even extra on actually learning them. Wise plan, correct?

In our closing thrilling episode, we laid out our grand scheme to point out my embarrassing inbox proper right into a treasure trove of information science learning. Proper now, we’re going to stipulate our draw back additional precisely and uncover how AI and classy information science methods may assist us cope with this digital monster.

Let’s start by breaking down what 37,000 unread emails truly means:

1. Amount: If each e-mail takes merely 1 minute to study, it’s going to take over 600 hours (that’s 25 days straight) to get through all of them. Yikes.

2. Choice: These emails range from important work communications to the 5 hundredth publication about cat motion pictures I in a roundabout way subscribed to.

3. Velocity: New emails maintain coming in prior to I can say “unsubscribe.”

4. Price: Hidden on this digital haystack are most certainly some needles of significant information… and an entire lot of spam.

5. Traditional: A number of of those emails are so earlier, they might qualify for classic standing. (Does anyone nonetheless use “Talk about to you on AIM later” as a sign-off?)

Now, how can AI and information science help us slay this e-mail dragon? Let’s break it down:

1. Pure Language Processing (NLP)

NLP is like instructing a laptop to study and understand human language. We are going to use it to:

• Routinely categorize emails by matter or significance
• Extract key information from e-mail our our bodies
• Summarize prolonged e-mail threads (because of who has time to study a 50-email chain concerning the place to go for lunch?)

Proper right here’s a sneak peek at what this might seem like in code:

from sklearn.feature_extraction.textual content material import TfidfVectorizer
from sklearn.cluster import KMeans
# Convert e-mail our our bodies to TF-IDF vectors
vectorizer = TfidfVectorizer(stop_words="english")
X = vectorizer.fit_transform(emails['body'])
# Cluster emails into issues
kmeans = KMeans(n_clusters=10)
kmeans.match(X)
# Add cluster labels to our dataframe
emails['topic_cluster'] = kmeans.labels_

Don’t worry if this seems to be like like alphabet soup correct now. We’ll break it down in future articles.

2. Machine Finding out Classification

We’ll observe fashions to routinely sort emails into lessons like:

• Urgent vs. Can Wait
• Work vs. Personal
• “Why am I subscribed to this?” vs. “Oh, that’s actually attention-grabbing”
• Deal or low price (we get a lot of promoting and advertising emails!)

3. Time Assortment Analysis

By analyzing e-mail patterns over time, we’re in a position to:

• Predict busy intervals (like when my boss is extra more likely to ship that urgent 11 PM e-mail)
• Decide the easiest events to cope with my inbox (most certainly not at 2 AM after a Netflix binge)

4. Neighborhood Analysis

We’ll map out my e-mail connections to:

• Decide key contacts (Who do I e-mail most? Who on a regular basis BCCs me?)
• Visualize communication patterns (Appears, I ghost a lot of folks. Sorry, everyone.)

5. Anomaly Detection

We’ll use AI to flag unusual emails, like:

• That one time a prince truly did want to give me his fortune (nonetheless prepared on that wire swap…)
• When my usually calm colleague sends an all-caps e-mail (URGENT: PRINTER OUT OF INK!!!)

Proper right here’s how we’ll methodology this monumental course of:

1. Data Extraction: We’ll pull all 37,000 emails proper right into a format we’re in a position to work with. Pray for my onerous drive.

2. Exploratory Data Analysis: We’ll dive into the data to know what we’re dealing with. Put collectively for some stunning revelations about my e-mail habits.

3. Preprocessing: We’ll clear the data, because of let’s face it, it’s most certainly messier than my exact inbox.

4. Attribute Engineering: We’ll create important choices from our e-mail information that our AI fashions can understand.

5. Model Developing: We’ll assemble and observe quite a few fashions to help categorize, summarize, and prioritize emails.

6. Evaluation and Iteration: We’ll examine our fashions and maintain refining them. Maybe by mannequin 37,000, they’ll be good.

7. Deployment: Lastly, we’ll create a system that will course of recent emails as they arrive in. The dream of Inbox Zero lives on!

This mission isn’t almost clearing my embarrassingly full inbox. It’s about tackling a difficulty that many individuals face inside the digital age: information overload. The methods we’ll uncover have capabilities far previous e-mail administration:

• Corporations use associated methods to course of purchaser options at scale.
• Researchers analyze large volumes of textual content material information to find out traits and patterns.
• Social media platforms detect and categorize content material materials routinely.

All of these are job experience that will help me land invaluable options. We are going to’t stop this stuff and it’s very important to study to make use of the model new devices to your profit.

Plus, let’s be honest, it’s a tremendous excuse for me to check information science with out having to admit I’m avoiding my emails.

In our subsequent thrilling installment, we’ll roll up our sleeves and start with information extraction. We’ll uncover the way in which to entry our e-mail information, the ethical considerations of working with personal communications, and the enjoyment of realizing merely what variety of “Remaining Remaining FINAL_v2” doc variations you’ve been emailed by the years.

Until then, may your inboxes be ever in your favor, and take into account: every unread e-mail is barely a information degree able to be analyzed!

Maintain tuned, and cozy procrastina — I suggest, information science-ing!