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👋 Hi there

Thank you for visiting my profile. My name is George and I am a Data Scientist currently working and living in London, UK.

💻 📚 Interests

My passion is around topics related to statistics, computer science and the intersection of these two fields. I enjoy working with different types of algorithms, from classification & regression, to clustering and graph/network models, and find ways to use my data analytics experience to investigate and solve real world problems.

Motto: "Life is short, use Python"

🔧 Tech Stack

Python R SQL PostgreSQL C++ C Azure Flask Shell cript Jupyter Notebook Amazon Web Services

George Spyrou's Projects

binary_classification_of_bank_marketing_campaigns icon binary_classification_of_bank_marketing_campaigns

Exploratory data analysis (EDA) and development of classification algorithms (Logistic Regression, Random Forest) to predict clients that are most likely to subscribe to a bank's product, as a result of marketing campaigns.

categorization_consumer_complaints icon categorization_consumer_complaints

Use of XGboost and Multinomial Naive Bayes, along with AWS SageMaker, to perform automatic text classification of consumer complaints to their respective categories

deep-learning-a-z icon deep-learning-a-z

Deep Learning course from Udemy covering the topics of ANN, CNN, RNN, Boltzmann Machines, SOMs and Autoencoders

online_courses icon online_courses

Notes and assignment solutions for online courses and MOOCs that I have completed or are currently in progress.

text_analysis_of_consumer_reviews icon text_analysis_of_consumer_reviews

Natural Language Processing (NLP) and analysis on reviews about delivery companies in the UK based on reviews extracted from the Trustpilot website

tube-virality icon tube-virality

Develop an API to retrieve statistics and information around Youtube trending videos. Perform descriptive statistics analysis, and build models able to project the likelihood of a trending video to become viral.

vaccines_trade_network icon vaccines_trade_network

Implementation of ARIMA and Holt-Winters Exponential Smoothing models for the analysis of the global network of human vaccines for the period 2010-2019

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