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10 years in ML/AI.

Kaggle Competitions Grandmaster (Top 0.1% globally) and Ex-Lead…

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  • Mario Filho - Data Science

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Reconocimientos y premios

  • 9th of 5170 at Porto Seguro’s Safe Driver Prediction

    Kaggle | Porto Seguro

    In this competition, you’re challenged to build a model that predicts the probability that a driver will initiate an auto insurance claim in the next year.

  • 2nd of 579 at Growing Instability: Classifying Crisis Reports

    DSTL | UK Gov. Office for Science | SIS | MI5

    Analysing data in documents such as media reports can provide a better understanding of a potential crisis situation, growing instability in a particular region or, specific theme such as terrorism. Using news material, this challenge asks participants to predict topic tags for classifying unseen reports so that they can be used to improve awareness and understanding

  • 6th of 3307 at Quora Question Pairs

    Kaggle | Quora

    In this competition, Kagglers are challenged to tackle this natural language processing problem by applying advanced techniques to classify whether question pairs are duplicates or not. Doing so will make it easier to find high quality answers to questions resulting in an improved experience for Quora writers, seekers, and readers.

  • 5th of 305 at Bank Fears Loanliness

    HackerEarth

    The Bank Indessa has not done well in last 3 quarters. Their NPAs (Non Performing Assets) have reached all time high. It is starting to lose confidence of its investors. As a result, it’s stock has fallen by 20% in the previous quarter alone.

    After careful analysis, it was found that the majority of NPA was contributed by loan defaulters. With the messy data collected over all the years, this bank has decided to use machine learning to figure out a way to find these defaulters and devise…

    The Bank Indessa has not done well in last 3 quarters. Their NPAs (Non Performing Assets) have reached all time high. It is starting to lose confidence of its investors. As a result, it’s stock has fallen by 20% in the previous quarter alone.

    After careful analysis, it was found that the majority of NPA was contributed by loan defaulters. With the messy data collected over all the years, this bank has decided to use machine learning to figure out a way to find these defaulters and devise a plan to reduce them.

    This bank uses a pool of investors to sanction their loans. For example: If any customer has applied for a loan of $20000, along with bank, the investors perform a due diligence on the requested loan application. Keep this in mind while understanding data.

    In this challenge, you will help this bank by predicting the probability that a member will default.

    https://www.hackerearth.com/challenge/competitive/machine-learning-challenge-one/leaderboard/

  • 3rd of 548 at Avito Duplicate Ads Detection

    Kaggle / Avito.RU

    To ensure that buyers can easily find what they're looking for without sifting through dozens of deceptively identical ads, Avito is asking Kagglers to develop a model that can automatically spot duplicate ads. With more accurate duplicate ad detection, Avito will make it much easier for buyers to find and make their next purchase with an honest seller.

  • 13th of 2125 at Home Depot Product Search Relevance

    Kaggle / Home Depot

    In this competition, Home Depot is asking data scientists to help them improve their customers' shopping experience by developing a model that can accurately predict the relevance of search results.

  • 1st (Winner) of 974 at Telstra Network Disruptions

    Kaggle / Telstra

    In their first recruiting competition, Telstra is challenging Kagglers to predict the severity of service disruptions on their network. Using a dataset of features from their service logs, you're tasked with predicting if a disruption is a momentary glitch or a total interruption of connectivity.

  • 1st (Winner) of 1323 at Caterpillar Tube Pricing

    Kaggle / Caterpillar

    Caterpillar relies on a variety of suppliers to manufacture tube assemblies, each having their own unique pricing model. This competition provides detailed tube, component, and annual volume datasets, and challenges you to predict the price a supplier will quote for a given tube assembly.

  • 6th of 985 at Facebook Recruiting IV: Human or Robot?

    Kaggle / Facebook

    Ever wonder what it's like to work at Facebook? Facebook and Kaggle are launching an Engineering competition for 2015. Trail blaze your way to the top of the leader board to earn an opportunity at interviewing for a role as a software engineer, working on world class Machine Learning problems.

    In this competition, you'll be chasing down robots for an online auction site. Human bidders on the site are becoming increasingly frustrated with their inability to win auctions vs. their…

    Ever wonder what it's like to work at Facebook? Facebook and Kaggle are launching an Engineering competition for 2015. Trail blaze your way to the top of the leader board to earn an opportunity at interviewing for a role as a software engineer, working on world class Machine Learning problems.

    In this competition, you'll be chasing down robots for an online auction site. Human bidders on the site are becoming increasingly frustrated with their inability to win auctions vs. their software-controlled counterparts. As a result, usage from the site's core customer base is plummeting.

  • 12th of 46,000+ Data Scientists - Highest Global Kaggle Ranking

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