Data Scientist
MSc Astrophysics & BSc Physics with Data Science at Queen Mary University of London.
Engineering intelligent systems that transform complex data into actionable insights
Passionate about leveraging cutting-edge AI and machine learning techniques to solve real-world problems. Experience with neural networks, deep learning, and predictive modeling.
Expert in extracting insights from complex datasets, statistical analysis, and data visualization. Specialized in building scalable data pipelines and predictive models.
Full-stack development experience with modern frameworks and technologies. Building robust applications that bridge the gap between data science and user experience.
Data Science Intern at Infosys and AI Trainer at Outlier with proven track record in building high-performance machine learning models and delivering actionable insights.
Discover my latest work in machine learning, data science, and AI applications
Built Random Forest and MLP models achieving 96.5% and 98.8% accuracy respectively using the UCI Car Evaluation dataset.
Engineered Decision Tree classifier achieving 96.7% accuracy and 0.89 AUC on the HTRU2 dataset for pulsar identification.
Engineered fraud detection system achieving 95% precision rate, significantly reducing financial losses.
Ready to collaborate on exciting data science and AI projects? Let's build something amazing together.
Currently open to new opportunities in data science, machine learning, and AI development.
Explore my latest articles on data science, AI, and technology trends
A deep dive into the architecture and functioning of neural networks, exploring their applications in AI.
Exploring various data visualization techniques to effectively communicate insights and findings.
Discussing the ethical implications of AI technologies and the importance of responsible AI development.