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Nationality Guesser by Name

Type a first name to instantly see the countries it is most associated with, each ranked by probability so you can judge how strong the signal is.

Updated

Data provided by Nationalize.io

No name guessed yet

Enter a first name to see the countries most statistically associated with it.

How to use Nationality Guesser by Name

The Nationality Guesser by Name predicts which countries a first name is most associated with, using aggregated demographic data. Enter any first name and it returns a ranked list of countries, each with a probability that reflects how strongly the name signals that origin. It works because first names carry cultural and linguistic fingerprints: spelling patterns, common suffixes, and historical popularity differ from country to country. The prediction comes live from the open Nationalize.io API and is a probabilistic estimate of likely origin — not a statement about any individual’s actual citizenship, ethnicity, or heritage.

  1. Type a single first name into the input box (for example, Sofia or Akira).
  2. Press Guess Nationality (or hit Enter) to send the name to the Nationalize.io API.
  3. Review the ranked list of countries, ordered from most to least likely.
  4. Read each probability percentage to see how confident the prediction is.
  5. Try alternative spellings to see how the predicted countries change.

Your query is sent to Nationalize.ioto fetch results. We don't store it.

How nationality prediction from a name works

Nationality prediction analyses the statistical association between a first name and the countries where it appears, drawn from large volumes of public data. The provider returns the top candidate countries and assigns each a probability — its estimated likelihood relative to the other candidates, so the listed probabilities sum to roughly 100%. A name like "Wei" leans strongly toward one region, while a widely shared name like "Maria" spreads its probability across many Spanish- and Portuguese-speaking countries. The tool maps each ISO 3166-1 alpha-2 country code (such as US or DE) to a readable country name, falling back to the raw code for less common codes. The output describes where a name is statistically common, not where any specific person is from.

Reading the probability output
ProbabilitySignal strengthInterpretation
60%+StrongThe name is closely tied to one country
30–60%ModerateA leading country, but real spread across others
Under 30%Weak / sharedThe name is common in several countries
No countriesNoneNot enough data to predict an origin

Accuracy, bias, and responsible use

Predicting nationality from a name is inherently uncertain and must be treated with care. Names migrate with people: immigration, diaspora communities, and global pop culture mean a name common in one country is regularly used in many others. The training data also over-represents some regions and platforms, so predictions are most reliable for widely documented names and far weaker for rare or newly coined ones. Critically, a name reveals likely linguistic or cultural origin at best — it says nothing definitive about a person’s citizenship, ethnicity, religion, or identity. Using these guesses to make assumptions about real individuals risks stereotyping and discrimination, so keep this tool to curiosity, education, and synthetic test data rather than any consequential decision.

Worked examples

Guessing nationality for "Akira"

Inputs: Akira

Result: Japan ranked as the most likely country with high probability

Guessing nationality for "Maria"

Inputs: Maria

Result: Probability spread across several Spanish/Portuguese-speaking countries

Glossary

Probability
The estimated likelihood that a name belongs to a given country, relative to the other candidates returned.
ISO 3166-1 alpha-2
The two-letter country code standard (e.g. US, GB, DE) used to identify each predicted country.
Country of origin
The country a name is statistically most associated with — an estimate of likely origin, not confirmed nationality.
Diaspora
A population that has spread from its original homeland, carrying names into many other countries.
Cultural bias
Skew in predictions caused by some cultures and regions being better represented in the underlying data than others.

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