Eliot Barril

Hong Kong SAR Contact Info
5K followers 500+ connections

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About

Data Science, AI, Data strategy, Retail Activity & Luxury Market

Activity

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Experience & Education

  • DFS Group Limited

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Licenses & Certifications

Projects

  • KAGGLE Competition: Expedia Hotel Recommendations

    Expedia provided logs of customer behavior. These included what customers searched for, how they interacted with search results (click/book), whether or not the search result was a travel package.
    The goal was to predict which hotel group a user is going to book.

    Final Ranking: 243/1974

  • Data Challenge - Predict missing links in citation network (Kaggle In Class)

    The goal was to predict links in a citation network. A citation network was defined as a graph where nodes are research papers. There was an edge between two nodes if one of the two papers cite the other.

    Accuracy score obtained : 0.97036

  • Data Challenge - Predict pollution in an asian megacity (Plume Labs)

    The goal was to predict the concentration of some pollutants for the next 24 hours at a particular point in an asian megacity given some pollution measurement and some meteo measurement at this particular points and at some other points in and around this megacity

    Ranking: 8/45

    Other creators

  • Opinion mining : sentiment analysis on the IMDB movie review dataset

    1 month project evaluated by a jury of researchers specialized in machine learning and computer science :
    -Implemented multiple preprocessing algorithms to extract meaningful features from raw text documents (in our case, movie reviews).
    -Tested different machine learning algorithms on the preprocessed data.
    -Grouped the learned models in an ensemble model.

    Other creators

  • KAGGLE : Porto Seguro's Safe Driver Prediction

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    The goal of this challenge was to build a model that predicts the probability that a driver will initiate an auto insurance claim in the next year.

    Final Rank : 59th/5170 (Top 1.1%)

    See project

  • KAGGLE : Sberbank Russian Housing Market

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    Housing costs demand a significant investment from both consumers and developers. In this competition, Sberbank is challenging Kagglers to develop algorithms which use a broad spectrum of features to predict realty prices. Competitors will rely on a rich dataset that includes housing data and macroeconomic patterns. An accurate forecasting model will allow Sberbank to provide more certainty to their customers in an uncertain economy.

    Ranking : 190th/3272
    Top 6%

    Other creators
    See project

  • KAGGLE Competition: Allstate Claims Severity

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    Final Ranking: 191st/3055
    Top 7%

    See project

  • Data Challenge : Estimation of car insurance premiums (MAIF) (Datascience.net)

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    The goal was to predict car insurance premiums from a database provided by MAIF.

    Final Ranking: 39th/941
    Top 4%

    See project

Honors & Awards

  • Kaggle Competitions Expert

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    Ranking : 1243rd/69810
    https://www.kaggle.com/eliotbarr

  • Kaggle Kernel Expert

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    Kaggle Scripts
    Ranking : 28th/73800
    https://www.kaggle.com/eliotbarr

Languages

  • English

    Professional working proficiency

  • French

    Native or bilingual proficiency

  • Spanish

    Professional working proficiency

  • German

    Elementary proficiency

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