Prepare the data
Use consented or synthetic messages, strip identifiers and split complete threads together.
01 / THE IDEA
Email Categorisation in practice.
Email categorisation predicts user-defined folders from message content. Thread duplication, private text and incorrectly hidden messages are practical concerns from the start.
Real-world use case · Proposed extension
A possible application for shared-inbox teams: Help small teams organise a shared inbox while retaining a complete all-mail view. This extension is not delivered by the supplied starter.
02 / THE BUILD
How to build a email auto-categorization project
step by step.
Build the experiment
Fit a text-classification pipeline with an uncertain category and user-defined labels.
Evaluate the result
Measure per-folder recall and inspect important messages placed in the wrong folder.
Define a reviewable output
Suggest reversible labels without deleting messages or changing access permissions.
03 / SOURCE SNAPSHOT
What the email auto-categorization starter actually does.
The supplied starter creates 800 synthetic binary-classification rows with ten numeric features. It makes a stratified train/test split, fits a scaled RandomForestClassifier pipeline and prints a classification report.
SOURCE EXCERPTPython
model = Pipeline(
steps=[
("scale", StandardScaler()),
("classifier", RandomForestClassifier(
n_estimators=250, random_state=RANDOM_STATE)),
]
)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print(classification_report(y_test, predictions))Important limitation
The supplied starter has no email text, vectoriser or folder labels. Synthetic binary classification does not establish performance on the proposed application. The source report uses one split; it supplies no deployment or real-world validation.
04 / BEYOND THE CLASSROOM
Where a email auto-categorization project could go next.
PROPOSED BUSINESS CONCEPT
Email Categorisation: a pilot concept
Proposed extension: Help small teams organise a shared inbox while retaining a complete all-mail view.
WHO IT COULD SERVEShared-inbox teams
FIRST MVPSuggest reversible labels without deleting messages or changing access permissions.
KEY RISKPrivate-message exposure and missed important email
This is a potential application pathway, not a delivered product. The supplied starter does not implement this service or validate it with real users.
05 / WHAT YOU TAKE AWAY
What a email auto-categorization project puts on your resume.
- Fit a text-classification pipeline with an uncertain category and user-defined labels.
- Measure per-folder recall and inspect important messages placed in the wrong folder.
- Suggest reversible labels without deleting messages or changing access permissions.
- Explain this limitation: The supplied starter has no email text, vectoriser or folder labels.
Want to build this with mentors and a lab?
AI Engineer Course Networkers Home runs this topic inside its AI Engineer Course, with placement support and hands-on labs.
06 / QUESTIONS STUDENTS ASK
Email Auto-Categorization project FAQs.
- Is Email Auto-Categorization suitable for a final year project?
- Yes — it fits a final year project scope: design dataset/labels, implement text processing and classifiers, and evaluate performance on real or synthetic emails.
- What should I learn or prepare before starting?
- Familiarise with Python NLP basics, vectorisers (TF-IDF), scikit-learn classifiers, evaluation metrics, and data anonymisation for texts; plan folder taxonomies and threaded message handling.
- How do I evaluate progress and how long will it take?
- Evaluate via precision/recall, confusion matrices, and per-folder F1; expect 6–12 weeks for dataset creation, modelling and analysis depending on scope and dataset availability.