World’s FirstAutomated ML

Platform with In-built Feature Store

Build no-code ML models in seconds leveraging thousands of features from GeoIQ’s data repository.

Core Features

Draw valuable insights and learnings with no-code ML models. Replicate the results to identify look-alikes of success scenarios

Augment extensive real world datasets

Enrich your data with over thousands of ML ready attributes. Predict the target variable precisely with no anomalies. Granular data available for locations across the country

One Click Build and Deploy

Build models with the click of a button. Upload a .csv file with just the addresses and the target variable. Model is analysis ready in couple hours reduced from days.

Unlimited models at no cost

Build as many models as it takes to get to the most accurate solution and run them parallelly. Pay only when you see results.

Why NoCode ML?

Try for Free

Build an unlimited number of models and run them parallelly

Competitive Pricing

Pay only when you see results

Real Time APIs

Sub second response time with 99.9% uptime *

Security

Data Security

GeoIQ is committed to securing and encrypting all its account information.

We're ISO-27001, HIPAA, SOC-2 (Type 2) compliant and have multiple security measures in place. You have complete ownership of your data at all times.

Frequently asked questions

Input or train dataset would include location addresses or lat/longs, and corresponding values of the target variable that you want to predict.

The models created for your dataset could be found under “Model Summary” on the top navbar.

Depending on the size of dataset, it could take 30 minutes to 2 hours to train the model. GeoIQ builds features on-demand for the data you have uploaded, and that needs some time. However, the process happens at the back-end and you’ll be notified when the model is ready.

Your train dataset should have at-least 1000 data points.

Once you upload the dataset, GeoIQ attributes are created for the entire dataset. Next, exploratory data analysis maps performance of each attribute against the target variable. It also create a machine learning model to predict the target variable.

Yes, you can input your own variables/attributes by adding them in columns to include while creating datasets. These attributes will used along with GeoIQ attributes while training the model.

For information on data related questions please refer to the FAQ section on thedata page.

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