Hi I am YOUSSEF ZERBOUH

Master's Student in Data Science and AI Currently pursuing a Master's degree in Data Science and Artificial Intelligence.
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About

I am a technology enthusiast passionate about data science and artificial intelligence. Currently pursuing a Master's degree in Data Science and AI, I have a strong interest in machine learning and the practical application of algorithms in real-world projects. I am continuously seeking opportunities to learn and grow within the vast field of technology, always eager for challenges that allow me to expand my horizons and enhance my skills. Below are the technologies I am proficient in:
NumPy

NumPy

Pandas

Pandas

TensorFlow

TensorFlow

PyTorch

PyTorch

MySQL

MySQL

Django

Django

Python

Python

Google Cloud Platform

Google Cloud Platform

PostgreSQL

PostgreSQL

MongoDB

MongoDB

Git

Git

Docker

Docker

JavaScript

JavaScript

Redis

Redis

Apache Kafka

Apache Kafka

Apache Spark

Apache Spark

Apache Airflow

Apache Airflow

Apache NiFi

Apache NiFi

Java

Java

Amazon Web Services

AWS

Hadoop

Hadoop

Selenium

Selenium

React

React

OpenCV

OpenCV

PHP

PHP

Flask

Flask

Projects

Dive into the projects developed so far

Amazon Reviews Sentiment Analysis

This project provides a real-time sentiment analysis pipeline for Amazon product reviews using Apache Kafka, Apache Spark Streaming, and MongoDB. It streams Amazon review data to Kafka, processes and predicts sentiment with Spark, and stores the results in MongoDB for further analysis or visualization. Tech: Apache Kafka, Apache Spark, MongoDB, Python, Docker, Machine Learning.

Dog-Cat Classification

A Flask-based web application that utilizes a deep learning Convolutional Neural Network (CNN) to predict whether an uploaded image contains a dog or a cat. The model processes uploaded images and generates predictions with a user-friendly web interface and API endpoint for programmatic access. Tech: Python, Flask, TensorFlow/Keras, CNN, Deep Learning, HTML, CSS.

Google Play Store Apps Analysis

A comprehensive analysis of Android applications in the Google Play Store, comparing thousands of apps to derive insights about the mobile app market. The project analyzes app categories, ratings, pricing, and user reviews using statistical and interactive visualizations to understand market trends. Tech: Python, Pandas, Matplotlib, Seaborn, Plotly, Jupyter Notebook, Data Analysis.

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