プロフィール詳細

プロジェクトを作成

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DIMITRIOS P.に依頼

Greece

Data Scientist | Ba MSc | Statistician | 5years exp in Insurance Maritime | Expert in Python, R, ML,Streamlit

プロフィール概要

ML projects & analysis: I have deployed a research project on the maritime industry about the idling periods of a vessel the main interest is to see the increase of the energy demand that is an outcome of idle periods and what is the effect in terms of consumption. The idle period is the time that a vessel/ship remains at the same spot without moving. In this case, the vessel has released the anchor and has zero speed. As this is not a money-making situation for the company (because vessels make money when they travel) the project aims to identify the additional power that the vessel needs to sail. In essence, the goal is to realize how less power the vessel would need to move if there was no idle period, and how harmful are the idle periods for fuel consumption. A machine learning project was held to identify those periods and reflect the extra consumption of the vessels. The accuracy of the algorithm was very good and the results were tested using the real fuel consumption of a vessel. Finally, the algorithm predicted the additional needed power with high accuracy. One other project was about the draught that a vessel should have before starting sailing. After analyzing vessels’ data, I when a vessel has the optimal fuel consumption. This analysis involves multiple variables that are linked to the vessel’s performance. The ML model advises the optimal draught that a vessel should have before its arrival. The user’s input of this model (that changes according to particular voyages) is speed, wind speed, wave speed, and some other characteristics of a voyage. I designed and implemented a machine learning model for insurance claims/ accidents in the maritime industry. More specifically, the goal is to predict the cost of an accident that happens on a vessel that is on a voyage. There are numerous variables that can characterize a shipping voyage, for example, trading area, weather, vessel type/age/cargo, port of arrival and destination, and seafarer’s year of experience/rank/medical health/previous conditions etc. The accidents that are happening all these years are labeled and linked to the above characteristics, and so the company has a historical background for all cases. So, for example, the shipping company knows the circumstances of a maritime accident and the total cost of treatment/repair. With this knowledge, I created an algorithm that can classify a new unseen accident and predict the cost of it. This assists the operational department and the management in the decision-making process. I have also implemented several big data analyses about various subjects that help the company make decisions. • I have analyzed the data we receive from vessels and made clusters to identify weather, wind, and current patterns for specific voyages that the vessels are making very frequently. I have also studied a lot, of engineering problems that are coming from the mechanical part of machines or engines. • Predictive maintenance analysis for several mechanical parts Moreover, I have analyzed healthcare data that are linked to an insurance company (previous company/occupation). Those analyses were about: • Sales linked to the productivity of salesforce teams to set annual and quarter goals • Quantitative analysis of insurance portfolio and premium forecasting based on claims records • Loss ratio calculations & premium checking Technical Skills - Languages: Python, R, SQL -Web Applications: Streamlit, Dash - DS & ML Tools: Weka, rapidminner - Statistical Tools: SPSS, MatLab - BI tools: Tableau, SAP Data Management Concepts: - Statistical Modeling - Machine Learning algorithms - Forecasting models - Business Intelligence - Data Mining /Cleaning - Feature engineering - Data Clustering/Classification - Visualization storytelling techniques - Collaborative filtering method - Time series analysis

専門分野

• ★ Scikit-learn
• ☆ SPSS
• ☆ Matlab
• ☆ SQL
• ☆ Excel
• ☆ R
• ☆ Statistical Finance
• ☆ Insurance

サービス

• Research
• Data & AI
• Product Development

Research Market Research, User Research, Meta-Research, Gray Literature Search

Data & AI Predictive Modeling, Statistical Analysis, Data Visualization, Big Data Analytics, Data Processing, Data Insights

Product Development Product Evaluation

職務経験

Data Scientist / Statistical Analyst

Angelicoussis group

1月 2018 - 現在

学歴

M.Sc (Master of Science)

University of Piraeus, Greece

1月 2017 - 1月 2019

Bachelor of Applied Science

University of Piraeus, Greece

1月 2008 - 1月 2013

認定資格

• Investment License - Certificate Level D - Bank of Greece

  Bank of Greece

  1月 2015 - 現在

• First Certificate in English - University of Cambridge

  University of Cambridge

  1月 2005 - 現在