• 🔭 Currently exploring advanced techniques in Natural Language Processing (NLP) for text data analysis.
• 🌱 I’m constantly learning and experimenting with new tools and techniques in Data Science and Machine Learning.
• 💬 Ask me about anything related to Data Science, Machine Learning, or Python!

• Programming Languages: C, C++, Python, R, JavaScript, Java.
• Machine Learning & Deep Learning: TensorFlow, Scikit-Learn, PyTorch, YOLOv8.
• Natural Language Processing (NLP): NLTK, SpaCy.
• Big Data Technologies: Spark, Hadoop, DataBricks.
• Data Visualization: Matplotlib, Seaborn, Plotly, PowerBI, Adobe Illustrator, Adobe InDesign.
• Database Management: SQL (MySQL, PostgreSQL, SQL Server, SQLite), ETL.
• Version Control & Collaboration: GitHub, GitLab.
• Operating Systems: UNIX, MacOS, Windows.
• Cloud Platforms: Azure, AWS, GCP.
• Web Development: HTML/CSS, ReactJS.
• Microsoft Office Suite: Excel, PowerPoint, Word.
• Miscellaneous: PowerShell.

Here is a sneak peak of some of my personal Data Science projects:

• This NLP model categorizes UK political parties on a scale from left-wing to right-wing using speeches from MPs spanning 1970 to present.
• Technologies used: Python, NLP, Sentiment Analysis, TF-IDF, Random Forest.

• This project uses the YOLOv8 model to classify LEGO minifigure images, showcasing the power of deep learning in image recognition.
• Technologies used: Python, YOLOv8, Computer Vision.

• This project leverages satellite data and machine learning to predict rice crop locations in Vietnam's An Giang province, showcasing the fusion of remote sensing and data science.
• Technologies used: Python, Satellite Data, Random Forest, Deep Learning.

• Utilizing machine learning to classify RFID tags' movement, aiding in real-world asset tracking and theft prevention.
• Technologies used: Python, Random Forest, Feature Selection, Deep Learning.