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SMOTE-MR: A distributed Synthetic Minority Oversampling Technique (SMOTE) for Big Data which applies a MapReduce based-approach. SMOTE-MR is categorized as an `approximated/ non exact` solution. Also, there is an `exact` solution called SMOTE-BD written by the author (See: https://github.com/majobasgall/smote-bd)

License: Apache License 2.0

Scala 100.00%
big-data imbalanced-data machile-learning scala smote spark

smote-mr's Introduction

Hi there, I'm María José!

I'm a PhD holder in Computer Science, specializing in Data Science, with expertise in Machine Learning, statistics, and data visualization on large-scale datasets. I have experience performing as a Data Science Lead and as a Data Engineer in Switzerland. Skilled in Apache Spark, Scala, Python, SQL, and DevOps. I'm looking for opportunities to further apply my proficiency in Machine Learning, Data Science or Data Engineering roles.

I thrive in collaborative environments, both sharing my skills and embracing new ones!

Languages and Tools:

Programming: Scala Python SQL Spark Hadoop Scikit-Learn Pandas Matplotlib NumPy

Cloud and BI tools: Microsoft Azure Google Cloud Platform Google BigQuery Tableau Power BI Apache Airflow

Others: Docker GitLab CI/CD GitHub Actions Bash JIRA Jupyter Notebook GNU/Linux Scrum Streamlit

Certifications

DataCamp Badge

DataCamp Badge

Google Cloud Badge

DeepLearning.AI Badge

EPFL Badge

EPFL Badge

Let's connect:

LinkedIn

And get to know about my projects, research papers, and even a glimpse into my personal interests by visiting my website:

Website

smote-mr's People

Contributors

majobasgall avatar mjbasgall avatar

Stargazers

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