Data-driven growth through smart decision-making

Predictive Analytics

Get the most out of your data: identify underlying patterns not visible to the human eye by leveraging neural networks to solve prediction and classification problems. Design, build, train and deploy machine learning and deep learning models in order to predict or classify key metrics of your business.

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Use cases

Leverage your business and customer data using machine learning.

Recommendation engines

Production & resource planning

Outlier or anomaly detection

Forecasting models & time series analysis

Customer segmentation

Interpretability

Fraud detection

Related work

Pedidos Ya

PedidosYa is part of Delivery Hero and they are the market leader for food delivery in LATAM. They are located in multiple countries in the region and are expanding by buying competitors like Glovo.

The goal

The client has a vast and diverse food catalog of all restaurants in the region that offer their services through PedidosYa application.

We were given the task to improve existing categorization and to extract further structured information that could allow PedidosYa improve their search results, recommendations and decisions. It was a typical natural language processing problem.

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The data

In this case data was vast but it was not labeled. Labelling the entire dataset was not a possibility because of time constraints. Natural language complexities were abundant as data was inputted by small restaurant owners following, and language variations from country to country only made things worse.

What was a sandwich in some locations was an emparedado in others and when some ice cream shops sell by kilogram, others sell by litre. Although french fries are usually a side dish, if sold alone they can be a plate you share with others and what is called Peruvian cuisine in Argentina is just a typical plate in Peru.

Technologies

SageMaker

Neo4j

Shapash

We have experience in using different tools, such as Amazon SageMaker, to accelerate the development process.

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Training, developing and delivering machine learning models into production