I build end-to-end machine learning systems — from data preprocessing and model training to deployment using Streamlit and APIs. My focus is turning raw data into intelligent, explainable, and production-ready applications.

• Data Analyst / Machine Learning Engineer
• Passionate about AI, automation, and backend systems
• I build real-world ML projects and deploy them using Streamlit & APIs

• Languages: Python, PHP, JavaScript, SQL
• ML Tools: Pandas, Scikit-learn, TensorFlow, XGBoost, Scikit-learn, Numpy,
• Data Tools: Jupyter Notebook, Power BI, Excel, Git & GitHub
• Web: Streamlit, Flask, HTML/CSS, Docker
• Visualization: Matplotlib, Seaborn, Plotly

• Estes AI (An explainable AI platform for breast cancer risk prediction and clinical decision support)
• Credit Risk / Loan Default predictor
• Student Performance Prediction System (ML + Streamlit)

• Deploying ML models into real-world systems
• Building explainable AI applications
• Improving backend API performance for ML systems
• Expanding into deep learning and NLP

“A machine learning model is only valuable when real people can use and understand it.” I focus on building systems that don’t just predict — but explain, deploy, and scale.

• Email: teo@drauig.xyz
• LinkedIn: https://shorturl.at/O972I