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Data Science Portfolio

Throughout my data science career, I was fortunate to work in different industries, working on diverse business models to solve real-world problems. Some of the models I built are presented below.

For more information on the projects or potential use cases, I invite you to Contact Me using the button below.


Architecture Complexity

Construction is complex, and it depends on various factors. Understanding the different pillars of complexity enables the Company to plan ahead, meet deadlines and adhere to compliance regulations with maximum profit.

Categorical & Numerical Prediction

Classifiers and Regressors are models that can predict the outcome before it is observed. The former predicts the Class or Category while the latter attempts to predict the actual value expected. 


Topic Modelling (NLP)

Natural Language Processing techniques are used to extract information from data provided in the form of text. Such data can be a document or a part of a form, amongst other things. These texts can shed light on important aspects that the Company need to focus on to meet their customers' and audiences' demands.

Behavioural Scoring

TimeSeries models can find patterns over time on the consistency of various behaviours. From customers and product performances to Marine insurance, Time Series is a technique that can be used to build a score and automate processes based on your metrics.


Weather Anomalies Score

Weather anomalies are becoming more common as the effects of Climate Change acclimate. These anomalies impact different businesses from Nautical, Aeronautical, Construction, Transport and more. 

Explainable AI Research

Companies are using AI to support their business decisions. Governments are issuing policies, Acts, Executive Orders and laws to ensure that Data Privacy, Copyrights, etc are respected. 

Explainable AI has many benefits. It increases confidence in your model, thus improving communication with Stakeholders and ensures the ethical use of its output.

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