I’ve worked on 20+ Data Science projects, helping the organisations I collaborated with to better understand their data, generate actionable insights, and build dashboards to monitor and improve their operations. Below is an overview of some of these projects.

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No private or sensitive data, only general information of my work.

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Real Use Cases

Python ∙ SQL ∙ Pytesseract ∙ OpenCV  ⎹  Lead to +€50,000 annual recovery potential.

Python ∙ SQL ∙ Pytesseract ∙ OpenCV ⎹ Lead to +€50,000 annual recovery potential.

Python ∙ Statistic tests ⎹ Delivered 20 strategic actions to increase conversion, ensure compliance and optimise the digital insurance journey.

Python ∙ Statistic tests ⎹ Delivered 20 strategic actions to increase conversion, ensure compliance and optimise the digital insurance journey.

Python ∙ Geopandas ∙ Clustering Models ⎹ Identified 4 optimal agency locations for tele-consultation booths, improving healthcare access in underserved areas.

Python ∙ Geopandas ∙ Clustering Models ⎹ Identified 4 optimal agency locations for tele-consultation booths, improving healthcare access in underserved areas.

Python ∙ Missing Data Imputation ∙ Bivariate Analysis ∙ Statistic tests ⎹  Invalidated assumptions about policyholders’ income, enabling sharper targeting and smarter strategic decisions.

Python ∙ Missing Data Imputation ∙ Bivariate Analysis ∙ Statistic testsInvalidated assumptions about policyholders’ income, enabling sharper targeting and smarter strategic decisions.

Python ∙ Neural Network ∙ Probabilist Models ⎹ Proved synthetic data’s reliability and changed internal standards for handling sensitive healthcare data in consumption studies.

Python ∙ Neural Network ∙ Probabilist Models ⎹ Proved synthetic data’s reliability and changed internal standards for handling sensitive healthcare data in consumption studies.

Python ∙ NetworkX ∙ TimeSeries ⎹  Enhanced a commercial tool by integrating real-time HR data, consolidating KPIs at group level and initiated a predictive module.

Python ∙ NetworkX ∙ TimeSeries ⎹ Enhanced a commercial tool by integrating real-time HR data, consolidating KPIs at group level and initiated a predictive module.

Python ∙ OpenCV ∙ CNN  ⎹ Improved identifier recognition Recall to 65%, enabling automation of cases previously handled manually.

Python ∙ OpenCV ∙ CNNImproved identifier recognition Recall to 65%, enabling automation of cases previously handled manually.

Python ∙ Geopandas ∙ Time Travel Estimation ∙ ArcGIS ⎹ Consolidated four agencies and closed two, leading to significant operational cost savings.

Python ∙ Geopandas ∙ Time Travel Estimation ∙ ArcGIS ⎹ Consolidated four agencies and closed two, leading to significant operational cost savings.

Python ∙ Geopandas ∙ Time Travel Estimation ∙ ArcGIS ⎹ Identified a low-impact synergie, leading to a strategic no-go decision.

Python ∙ Geopandas ∙ Time Travel Estimation ∙ ArcGIS ⎹ Identified a low-impact synergie, leading to a strategic no-go decision.

Undergraduate Use Cases

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