Prepare the data
Prepare licensed product descriptions with editor-verified categories and group variants before splitting.
01 / THE IDEA
Product Categorisation in practice.
Product categorisation maps catalogue descriptions to a controlled taxonomy. Duplicate variants and changing categories require careful splitting and an explicit unknown-category path.
Real-world use case · Proposed extension
A possible application for catalogue editors: Help small merchants reduce repetitive catalogue labelling while preserving editorial control. This extension is not delivered by the supplied starter.
02 / THE BUILD
How to build a product categorization system project
step by step.
Build the experiment
Train a TF-IDF text classifier with a fallback for low-confidence or unseen categories.
Evaluate the result
Measure macro F1 and confusion between neighbouring taxonomy branches on held-out products.
Define a reviewable output
Provide top candidate categories and an editor correction workflow.
03 / SOURCE SNAPSHOT
What the product categorization system 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 source contains synthetic numeric features, not product descriptions or a taxonomy. 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 product categorization system project could go next.
PROPOSED BUSINESS CONCEPT
Product Categorisation: a pilot concept
Proposed extension: Help small merchants reduce repetitive catalogue labelling while preserving editorial control.
WHO IT COULD SERVECatalogue editors
FIRST MVPProvide top candidate categories and an editor correction workflow.
KEY RISKTaxonomy drift and incorrect product placement
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 product categorization system project puts on your resume.
- Train a TF-IDF text classifier with a fallback for low-confidence or unseen categories.
- Measure macro F1 and confusion between neighbouring taxonomy branches on held-out products.
- Provide top candidate categories and an editor correction workflow.
- Explain this limitation: The source contains synthetic numeric features, not product descriptions or a taxonomy.
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
Product Categorization System project FAQs.
- Is this suitable for a final year project?
- Yes — it’s a solid intermediate machine learning idea for final year students, focusing on taxonomy mapping, handling duplicates and unknown categories; the starter is partial and needs extension.
- What should I learn or prepare before starting?
- Familiarity with Python and scikit-learn, text preprocessing, feature engineering for product descriptions, and taxonomy design; plan to replace synthetic numeric features with real textual data.
- How do I evaluate progress and scope the work?
- Start by building a dataset and baseline classifier, add duplicate handling and an unknown-category path, then measure accuracy, F1 and rejection rates; expect several weeks to prototype and more to refine.