2 Best APIs for Country-Specific Mode in Nigeria

Agify.io is a name-to-age prediction API that estimates the most likely age of a person based on their first name, using statistical analysis of a database of name-age associations derived from social profiles and public records across multiple countries. The API returns a single predicted age along with the number of data points used for the prediction, which serves as an indicator of prediction confidence. The prediction model works by calculating the average age of individuals with that name in the statistical database. Names that are predominantly given to people born in a specific era — either because the name was fashionable at a particular time or because it has generational cultural associations — will produce reasonably accurate age estimates. Names that span generations evenly will produce an average age close to the population mean with lower discriminatory power. Country-specific prediction allows the age estimation to be conditioned on a country code (ISO 3166 alpha-2), improving accuracy by using name-age data specific to that country rather than the global dataset. Different cultures have different naming traditions and generational naming trends, so country-specific mode can significantly improve prediction accuracy for regionally concentrated names. Batch lookup mode accepts multiple first names in a single API request, returning an age estimate for each — making it efficient for bulk enrichment of user databases or contact lists without the overhead of individual API calls per name. For Nigerian applications, Agify.io provides a practical approach to demographic estimation without requiring users to disclose their birth year or age. While the accuracy for Nigerian-specific names depends on how well-represented those names are in Agify's statistical database, many Nigerian names — particularly those with cross-cultural adoption or those used by the diaspora in countries with larger data samples — will produce useful estimates. Nigerian e-commerce platforms can use age predictions to refine product recommendation algorithms — different age groups have different shopping preferences, and even rough age-bracket estimates from names can improve recommendation relevance. Nigerian fintech apps with age-gated features can use Agify predictions as one signal in age verification supplementary checks. Nigerian marketing platforms and digital advertising tools can use estimated age distributions from user name databases to model audience demographics — informing campaign targeting and content strategy with data-driven age segmentation without requiring users to explicitly provide age information. Like Genderize.io and Nationalize.io, Agify.io offers a free tier of 100 predictions per day without requiring any API key, making it trivially easy to test for a specific use case. Paid tiers at $9/month (1 million predictions per day) and $29/month (10 million per day) support large-scale production use. REST API calls are simple GET requests with the name as a query parameter. Agify.io's country-specific mode uses localized datasets that account for naming trend generational patterns in specific countries. Nigerian names present a complex case — Yoruba, Igbo, and Hausa names each have their own generational patterns that may not be well-represented in Agify's global dataset. However, Nigerian English names and names common in the Nigerian diaspora may have better coverage. The sample count field in Agify responses is critical for calibrating confidence. A prediction based on 50,000 samples should be weighted much more heavily than one based on 50 samples. Nigerian developers integrating Agify for decision-making should set minimum sample count thresholds below which predictions are treated as low-confidence and flagged accordingly rather than acted upon automatically. Agify, Genderize.io, and Nationalize.io form a complementary trio that together provide age, gender, and nationality predictions from names. Nigerian developers building comprehensive user profiling features can call all three APIs for a single name to build a probabilistic demographic profile without requiring users to self-report demographic information at registration.

Genderize.io is a name-to-gender prediction API that infers gender from first names based on statistical analysis of a large database of name-gender associations collected from social network profiles worldwide. The API takes a first name as input and returns the predicted gender along with a probability score and the sample count used for the prediction. The prediction model works by frequency analysis: if a name in the database is associated predominantly with male or female profiles, that name is predicted to be of that gender, with the probability reflecting the degree of dominance. A name like "Peter" would have a very high probability of male prediction, while a name like "Alex" might have a lower probability as it is used by both genders. Names with insufficient data in the database return a null gender with zero probability. Country-specific prediction mode allows the gender prediction to be conditioned on a country code (ISO 3166 alpha-2), which is important because some names have different gender associations across cultures. A name that is predominantly female in one country might be predominantly male in another. For Nigerian use cases, specifying the country code "NG" where the prediction dataset has Nigerian name data can improve accuracy for Nigerian names. Batch lookup mode accepts multiple names in a single API call, making it efficient for processing lists of users rather than making one API call per user. This is essential for data enrichment pipelines that process large databases of existing user records. For Nigerian applications, genderize.io provides a practical approach to adding personalization without requiring explicit gender data collection from users. Nigerian e-commerce platforms can personalize product recommendations and email greetings. Nigerian HR platforms can generate gender diversity analytics from employee name databases without requiring employees to fill in gender fields. Nigerian fintech apps can address customers by appropriate titles in automated communications. It is important to acknowledge the limitations for Nigerian use cases: the statistical database is weighted toward globally-common Western names. Nigerian first names across Yoruba (Adewale, Funmilayo, Chike, Adaeze), Igbo (Emeka, Chinwe, Obiora, Amaka), and Hausa (Abdullahi, Fatima, Sani, Hauwa) traditions may have lower sample counts in the database, potentially resulting in lower confidence predictions or null results for less internationally common Nigerian names. Despite this limitation, Genderize.io remains useful for Nigerian applications where a portion of the user base uses internationally common English or Arabic names, and where a best-effort gender inference (rather than guaranteed accuracy) is acceptable. The free tier of 100 lookups per day requires no API key — making it trivially easy to evaluate for a specific use case before committing to a paid subscription. Integration is a simple HTTP GET request with the name as a query parameter. No client library is required, and the JSON response is minimal and easy to parse. Genderize.io provides a transparent view into prediction uncertainty through its sample_count field. For Nigerian names with lower database coverage — less internationally common Yoruba, Igbo, or Hausa names — the sample count may be very low (single digits or zero), clearly indicating that the prediction is unreliable. Building confidence-aware logic that defers to other signals or prompts for user confirmation when sample counts are below a threshold produces better outcomes than treating all predictions equally regardless of evidence quality. The API's country-specific mode can also help with Nigerian applications by specifying country code "NG" to use any Nigeria-specific name data in the Genderize database, potentially improving predictions for names with clear gender associations within Nigeria even if they are less known internationally.