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Hello World 👋

A passionate Data Scientist from Morocco

About Me

I'm Youssra Abouelmawahib, a data science enthusiast driven by a love for solving complex problems and uncovering insights from data. My journey in data science is fueled by my curiosity and desire to make a positive impact through innovative solutions.

  • 🌱 I’m currently learning advanced Data Science techniques and exploring the fields of NLP, Generative AI, and Prompt Engineering.
  • 💬 Ask me about NLP, AI, Generative models, and Prompt Engineering.
  • 📫 How to reach me: [email protected]

Technologies and Tools

python javascript java PyTorch tensorflow scikit-learn PowerBI tableau mysql postgresql mongodb AWS GCP docker firebase miro canva figma git ssis networkx google colab

GitHub Stats

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GitHub Streak

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Youssra Abouelmawahib's Projects

automatic-review-analyzer icon automatic-review-analyzer

The goal of this project is to design a classifier to use for sentiment analysis of product reviews. Our training set consists of reviews written by Amazon customers for various food products. The reviews, originally given on a 5 point scale, have been adjusted to a +1 or -1 scale, representing a positive or negative review, respectively.

collaborative-filtering-via-gaussian-mixtures icon collaborative-filtering-via-gaussian-mixtures

This project aims to build a mixture model for collaborative filtering using Gaussian mixtures. The dataset used consists of movie ratings provided by users, extracted from a subset of the Netflix database. Since users have rated only a small fraction of the movies, the dataset is partially filled. The objective is to predict the missing entries .

covid-19-tableau-dashboard icon covid-19-tableau-dashboard

Explore COVID-19 trends interactively. This Tableau dashboard visualizes cases, deaths, recoveries, and vaccination data, offering insights into the global pandemic's impact. Stay informed through dynamic data analysis. #COVID19 #DataVisualization

deciphering-long-term-co2-trends-a-time-series-analysis-of-mauna-loa-data icon deciphering-long-term-co2-trends-a-time-series-analysis-of-mauna-loa-data

Using sophisticated time series analysis techniques on Mauna Loa Observatory data, we unveil insightful patterns and trends crucial for understanding climate change dynamics. This project showcases our commitment to rigorous scientific inquiry, essential for advancing environmental research and informing policy decisions.

digit-recognition icon digit-recognition

The MNIST dataset comprises 60,000 training and 10,000 testing handwritten digits, aiding various image processing systems. Each image, sized 28x28 pixels, was collected from Census Bureau employees and high school students, offering a rich resource for testing diverse methods.

estimating-ocean-flows-with-gaussian-processes icon estimating-ocean-flows-with-gaussian-processes

The Philippine Archipelago is a fascinating multiscale ocean region. Its geometry is complex, with multiple straits, islands, steep shelf-breaks, and coastal features, leading to partially interconnected seas and basins. In this part, we will be studying, understanding, and navigating through the ocean current flows.

genomics-and-high-dimensional-data icon genomics-and-high-dimensional-data

In this project, we will analyze a single-cell RNA-seq dataset, with the goal of unveiling hierarchical structure and discovering important genes. The datasets provided are all different subsets of a larger single-cell RNA-seq dataset, compiled by the Allen Institute.

monitoring-inland-surface-water-area-using-google-earth-engine-python icon monitoring-inland-surface-water-area-using-google-earth-engine-python

In this project, we’ll explore the process of developing a powerful tool to monitor inland surface water areas using the Google Earth Engine (GEE) platform and Python. By leveraging geospatial data and advanced analysis techniques, we’ll unlock insights into water dynamics, contributing to environmental research and sustainable resource management.

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