Languages: Python, SQL
Libraries/Frameworks: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Plotly
Tools: Jupyter Notebook, Git, Docker, VS Code, Anaconda
Databases: MySQL, PostgreSQL
Other: SAP, MS-Office
Concepts: Advanced Statistical Learning, Statistical Learning for Big Data Analysis, Applied Bayesian Data Analysis, Monte Carlo Simulation, Generalized Linear Model.
M.Sc. in Data Science, TU Dortmund(Expected 2027)
Masters in Computer Application (2012)
Bachelor in Computer Application (2009)
Description: The goal of this project is to build a predictive model for gold prices using market and economic indicators. Random Forest and Gradient Boosting algorithms were used to forecast future gold prices based on historical data and financial indicators. The model achieved an R-squared accuracy of 80%, indicating a high level of predictive performance.
Conclusion: Developed a Random Forest Regressor achieving 81.4% accuracy (R2 = 0.814) to predict gold prices. Identified Silver (SLV) as the strongest predictor (r = 0.87) and validated model generalization using Gradient Boosting as a benchmark.
Tech Stack: Python, Pandas, Numpy, Scikit-learn, Matplotlib, Streamlit, Jupyter Notebook, VSCode
Description: This dataset provides a comprehensive view of how German economic activity has evolved over nearly two decades and reflects the underlying strengths and adaptations of the German business landscape. As a student with a passion for business and economics studying in Germany, I am eager to explore how market forces, strategic decisions, and structural characteristics shape these economic trends and contribute to Germany’s continued economic success.
Conclusion: Analyzed 15 years of German trade data, uncovering an average profitability of 28.69%, a productivity gap of€268 (large firms) vs. €116 (micro firms) per person, and a 27% net market growth since 2005 with a steady investment intensity increase of +0.026% per year.
Tech Stack: Python 3.8+, pandas, numpy, matplotlib, seaborn, openpyxl
Description: Contributed to a real-world Omdena project analyzing healthcare accessibility in Sudan. Involved in data collection, cleaning, and exploratory data analysis to uncover insights and support data-driven decision-making.
Tech Stack: Python , pandas, numpy, matplotlib, seaborn.
AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents – Udemy
AWS Certified Machine Learning Specialty 2025 - Hands On! – Udemy
SQL & Database Design A-Z™: Learn MS SQL Server + PostgreSQL – Udemy
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