I am a Junior Data Scientist with a passion for learning and applying data-driven solutions. I have a solid foundation in:

• Machine Learning
• Statistics
• Databases

I am continuously building my portfolio of projects, applying my skills to solve real-world problems. My long-term goal is to become a highly skilled Data Scientist, capable of delivering insights and solutions to complex challenges.

Feel free to explore my projects below to see my progress and the areas I'm focusing on. Please note that some projects are still in the process of being updated, but new additions are coming soon.

• 🧠 Continuously learning through hands-on projects in supervised and unsupervised learning, neural networks, and deep learning frameworks like TensorFlow and PyTorch.
• 📊 Applying statistical techniques such as hypothesis testing, regression analysis, and probability distributions in my work.
• 🤖 Expanding my knowledge in natural language processing (NLP) and exploring innovative algorithms.
• 📈 I also enjoy scraping sports data and creating visually appealing charts to tell data-driven stories.
• 💡 Always on the lookout for new challenges in data science, staying updated with the latest trends and research papers.
• ⚙️ Build my skills in Devops en Data Engineering to maintain clear and efficient pipelines

• Implemented state-of-the-art algorithms from the literature on Speech Emotion Recognition (SER) to analyze emotional patterns in speech data.
• Trained models using audio features extracted with a model that have been trained with emotion2vec+large features , leveraging advanced embeddings to improve emotion classification accuracy.

This was an individual project at Polytech Lille.

• Cleaned the dataset(10 000 lines) by removing null values to enhance data quality and ensure accurate analysis.
• Engineered new features using word embedding techniques to capture semantic relationships in the text data.
• Trained and optimized machine learning models to accurately classify toxic comments, improving the detection of harmful content.

This was a team project at Polytech Lille involving a group of 6 members.

• Conducted exploratory data analysis (EDA) on a dataset of 600 scooter users to uncover 4 distinct user segments.
• Performed hypothesis testing to validate key insights and user behaviors.
• Developed and proposed two targeted offers designed to meet the needs of these four user segments, enhancing overall customer satisfaction