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Free production-ready automated data enrichment
for machine learning:
only accuracy improving features from 100+ sources

Automatically searches through thousands of ready-to-use features from public and community data sources and enriches your ML pipeline with only the relevant features

Search in Colab
Search in Binder

Ready to use Jupyter IDE with search example

Trusted by data scientists and data engineers

Past, present, and future data

100+ connected sources
239 countries
40 years of data history

🌐 Public data

Historical weather & Climate normals for postal/ZIP code

68 countries
22 years history
Monthly update

Air temperature
Precipitation
Wind
Air pressure
Normals
Sun hours
Moon phase

Location/Places/POI/Area info
from OpenStreetMap
for postal/ZIP code
221 countries
2 years history
Monthly update

POI Categories:
Schools, restaurants, hotels, supermarkets, etc
Houses:
Living buldings, business centers, etc
Transport infrustructure:
Roads, public transport stops, etc
Public facilities:
Gov. offices, post office, police, etc
Natural features:
Public parks, green areas, etc
Stats for different distances (1 km / 3 km / 5 km)

International holidays & events, Workweek calendar

232 countries
22 years history
Monthly update

Workweek calendars by countries
Public holidays / Observed holidays
Religious holidays
Sporting events
Political events

Consumer Confidence index


44 countries
22 years history
Monthly update

Monthly Consumer confidence index

World economic
indicators

191 countries
41 years history
Monthly update

Consumer Price index
GDP
Сentral Bank Rates
Сommodities prices

Markets
data

17 years
history
Monthly update

Stock prices
Stock volumes
Currencies and exchange rates
Market indexes

👩🏻‍💻 Community shared data

World demographic data
for postal/ZIP code

90 countries
Annual update

Residential population
Income
Home value
Home ownership
Employment
Industries
Occupations
Population mobility

Public social media profile data
for email & phone

600+ mln phones
350+ mln emails
104 countries
Monthly update

Estimated age
Gender, nationality
Residence & zip/postal code
Maritial status
Employer, job title
Duration of employment
Interests

World mobile network coverage
for postal/ZIP code

167 countries
Monthly update

Mobile network coverage statistics

Mobile phones activity index

Statistics for different distances
(1 km / 3 km / 5 km)

Car ownership data and
Parking statistics
for postal/ZIP code
email & phone
3 countries
Annual update

Car Brand
Car Model
Year statistics
Parking statistics by:
Brand, Model

Geolocation profile
for IPv4 & phone

2^32 IP
600+ mln phones
239 countries
Monthly update

Country
Region
City
Postal/ZIP code
Time zone
Proxy/VPN/Datacenter flag for IP

World house prices
for postal/ZIP code


44 countries
Annual update

House price index for countries
House price index for zip/postal code

Don’t see the data source you need?
Let us know, we’ll add that!

Add new data source

🔎 Search and enrichment for 6 entity types

Dateor DateTime
CountryISO 3166 codes
Postal/ZIP code900 000+ unique codes
Phone number600 mln+ phone numbers
Hashed email (HEM)350 mln+ emails
IP-address2^32 ip-addresses

🏁 Get started in under a minute

Step by step guide for Jupyter

#1

Install Upgini library

... from PyPI and check out our documentation on GitHub (it's open-source)

#2

Select data enrichment keys
and initiate feature search

You can reuse your existing labeled training dataset
Only relevant features that give metric improvement (ROC AUC, RMSE, etc.) returned, not just correlated with the target variable.
Without API Key With Free API Key

#3

Enrich ML model with new features and retrain

10-25% accuracy improvement to baseline results from mainstream AutoML frameworks

#4

Add external features into production ML pipeline

Enrich production datasets with actual features/data for the present time

Tutorials

Data Enrichment for Absolute Beginners in Machine learning

Data Enrichment for Absolute Beginners in Machine learning

5 Cool Ways to Enrich ML Models with Open Data for Free

5 Cool Ways to Enrich ML Models with Open Data for Free: An In-depth Review of Python Libraries

Data Enrichment for Email response Prediction

Data Enrichment for Email response Prediction

Find new features & data for your ML model

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Search in Binder
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