Name-to-Gender Classification

Our gender checking tool can determine whether a name is most likely to belong to a male or a female. We offer the service across multiple technologies, from high-volume APIs , processing of spreadsheets and integration into third-party platforms. We offer global coverage and support for non-latin alphabets.

Understanding Name-to-Gender Checking

Gender classification is a process used to infer an individual's gender based on their first name. The method uses data analysis and statistical models to guess a person's likely gender, which helps understand demographic trends. The result shows how likely it is that a name is associated with a specific gender.

Since naming conventions differ across countries and cultures, the accuracy of the prediction can be further enhanced by scoping the classification to a specific country of origin.

Accuracy and Limitations

It is important to note that this classification does not claim absolute accuracy due to the nature of names being non-exclusive to a single gender. The results are probability-based and will be returned with an accuracy score.

Users should acknowledge the inherent limitations of name-based gender classification. The fluidity of gender identity and the cultural variations in names mean that there will always be a degree of uncertainty. Names that are unisex or less common may have closer probability scores, indicating a non-specific gender association.

The Technology Behind Our Services

At the heart of our gender inference is an expansive dataset of around a billion people, gathered from diverse sources worldwide. This dataset is continually updated and refined to reflect current naming trends and demographic shifts. By leveraging big data analytics, we can analyze patterns and predict gender associations with high accuracy. The technology is designed to be scalable, handling requests ranging from single-name queries to millions of names processed via bulk uploads. This robust infrastructure allows us to serve a wide array of users, from individual researchers to large corporations, providing them with reliable name-based gender classification.

See also: Our data

  • 22 Alphabets
  • ~1 billion Database Size
  • 220 Countries

Full name parsing

Besides, in a few cultures, surnames don't carry any association with gender, so lookups on our name-to-gender services are always done based on a first name. However, we support the input of full names, which we will then parse to extract the first names. We also implement a fallback to a romanised version of the inputted name, in case the original input is unknown to us.

This leads to a fallback mechanism, as shown here. We will first make a lookup directly on the inputted name. If it's not found, we'll attempt to parse it as a full name. If still not found, we'll repeat the two with romanised versions of the input.

  • Input

    Δημήτρης Φωτόπουλος

    Lookup

    Δημήτρης Φωτόπουλος

  • Input

    Δημήτρης Φωτόπουλος

    Lookup

    Δημήτρης

  • Input

    Δημήτρης Φωτόπουλος

    Lookup

    Dimitris Fotopoulos

  • Input

    Δημήτρης Φωτόπουλος

    Lookup

    Dimitris

Localization

Naming conventions and the association between names and genders sometimes differ from culture to culture. To provide more accurate classifications, we support the scoping of a prediction to a specific country. By using scoping, we will make our prediction only based on data from the specified country.

> curl https://api.genderize.io?name=kim

{
   name: "kim",
   gender: "female",
   probability: 0.7,
   count: 83361
}

> curl https://api.genderize.io?name=kim&country_id=DK

{
   name: "kim",
   gender: "male",
   probability: 0.96,
   count: 3626,
   country_id: "DK"
}

Applications of Name-to-Gender Classification

The applications of name to gender classification are vast and varied. Academically, it facilitates sociological research and studies related to gender trends and differences. In business, it is a powerful tool for audience segmentation, enabling companies to tailor products and marketing campaigns to specific demographic groups. Furthermore, in the realm of analytics, it assists in enriching customer databases, enhancing the accuracy of customer profiles, and improving user experience through personalization.

Beyond these, name to gender classification also supports health researchers in epidemiological studies, assists in voter and census data analysis. It can also be pivotal in humanitarian efforts, such as analyzing displacement patterns of populations in crises based on names in registries.

See also: Case studies

Tools for Using Our Services

We offer a range of tools to access our gender classification services, ensuring flexibility and ease of integration for all users. The API provides a seamless way for developers to integrate gender classification into their applications, websites, or systems. For non-developers, we offer a CSV upload feature, allowing users to upload and classify large datasets efficiently.

We also offer integration through official no-code applications , facilitating the use of this classification within various apps and workflows without the need for coding. Additionally, the platform supports various open-source libraries, making it easy for developers to implement gender prediction in almost every programming language.

API

Our API offers direct integration of our gender classification service and allows for high-volume processing.

API Docs

CSV Processing

With our CSV tool, you'll be able to upload a file and have it processed in minutes, finding the gender of each row.

Process CSV

No-Code Integrations

Our official no-code integrations let's you instantly integrate with more thousands of apps through the biggest no-code platforms.

Read More