Case Studies
Our data scientists have delivered projects across sectors such as finance, e-commerce, healthcare, agriculture, and industrial engineering, for companies from around the world including the US, Australia, Switzerland, and the UK.
Case Studies
Our data scientists have delivered projects across sectors such as finance, e-commerce, healthcare, agriculture, and industrial engineering, for companies from around the world including the US, Australia, Switzerland, and the UK.
Project: To reduce the time Phastar analysts spend manually coding verbatim medical terms by developing an automated Natural Language Processing (NLP) solution to identify and recommend the most similar Low-Level Terms (LLTs) for every verbatim term.
Outcome: Our team developed an NLP model that could identify and recommend the most similar LLTs with up to 90% accuracy
Project: To identify inefficiencies in production lines and extract actionable insights from the data provided.
Outcome: Development of a non-linear supervised machine learning approach (random forest) to better explain variables affecting productions and detect abnormalities at large scale
Project: To develop a python-based model to estimate the size of pineapples from phone-generated imaging directly taken from plants in the field and to apply this model to internally developed correlations to predict pictures from pineapples into sizes.
Outcome: Hundreds of pineapple images were annotated to train the deep-learning model, which achieved up to 90% accuracy. Reasons for the remaining errors as well as potential solutions were identified.
Project: To better understand the impact of demographics on mental illness by understanding the relationship between the demographic variables, the disease, and the connectomic data.
Outcome: Our data scientists were able to build a model which successfully identified key features, such as age and sex which contribute to the predictions of their model.
Project: To analyse how Simprint’s fingerprint technology was being used and its effectiveness as a biometric tool for identification.
Outcome: The analysis that our data scientist delivered enabled Simprints to evaluate how their technology was performing, to make improvements to project operations on the ground, and also to influence the design of future projects.
Project: To develop an unsupervised machine learning model to predict when parts are likely to fail within a hydroelectric power plant.
Outcome: The Ishango.ai team developed a model that could identify anomalies at certain times and also identify abnormal patterns that are likely to be the cause of the anomalies detected.
Project: To classify patients’ depression levels and understand sleep subgroups by analysing public data sets using machine learning and signal processing techniques.
Outcome: The Ishango.ai team developed a supervised machine-learning model that predicts the severity of depression from actigraphy data (movement/sleep).
Project: To design a data-driven system that could create targeted recommendations of suitable products to tails.com customers, aiming to introduce new products to customers.
Outcome: The Ishango.ai team developed a recommender system targeting products to pre-existing tails.com customers.
Project: To develop a recommendation engine to support Analysts by suggesting high-relevance articles as a source of insights for their clients.
Outcome: The team developed an NLP model to extract and group topics using Latent Dirichlet Allocation (LDA) topic modelling.
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