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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Applications
Leverage your business and customer data using machine learning.
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Recommendation engines
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Customer segmentation
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Production & resource planning
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Interpretability
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Outlier or anomaly detection
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Fraud detection
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Forecasting models & time series analysis
Success stories
Stanford
Stanford
Stanford University’s Emergency Medicine Department from the School of Medicine leads the advancement of emergency medicine through innovation and scientific discovery. The department benefits from collaboration with other disciplines at Stanford, within local Silicon Valley, and across the globe.
The goal
The client wanted to create predictive models capable of using a patient’s Electronic Health Records (EHR) to anticipate the reason and timing of the patient’s next visit to the emergency room. Specifically, we were focused on surmising visits caused by domestic violence.
The data
The data consisted of over 300 million patient visits to the emergency room from different hospitals in the US. As it is extremely sensitive data, it was anonymized, deleting all patient identificatory traits and keeping only relevant demographic information.
Pedidos Ya
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.
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
We have experience in using different tools, such as Amazon SageMaker, to accelerate the development process.
Neo4j
Shapash
Sage Maker
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