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Machine Learning · Mini Project
Name Origin Classifier
Character-level model that guesses the linguistic origin of a name
1-2 weeks build
Overview
A character-level recurrent network, trained on the public names dataset used in the PyTorch tutorials, that predicts which of eighteen languages a surname most likely comes from. It is compared with a character n-gram baseline, and a Flask page shows the top guesses with probabilities. The report is explicit that a name says nothing reliable about a person's nationality.
What makes it stand up
- 01Character-level GRU built and trained from scratch in PyTorch
- 02Character n-gram logistic regression as a baseline
- 03Per-language confusion matrix showing which origins get mixed up
- 04Top-three guesses with probabilities rather than a single answer
- 05Guesses language origin only, never nationality or ethnicity, with bias limits documented
Module breakdown
- 01
Dataset
Loads and normalises names across eighteen languages.
- 02
Baseline
Character n-gram features with logistic regression.
- 03
RNN model
GRU over one-hot characters with class weighting.
- 04
Evaluation
Per-class F1 and confusion matrix.
- 05
Web demo
Flask page returning the top three origins.
After this, you will be able to
- Explain how an RNN reads a sequence one character at a time
- Defend the ethical limits of inferring origin from a name
- Demo predictions on names the examiner suggests
Technology stack
- Python
- PyTorch
- Pandas
- Flask
- Level
- Mini Project
- Domain
- Machine Learning
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support