Pursuing a Bachelor's Degree in Information Technology with a specialization in Information Technology and Data Management.
GPA : 3.63
Helping organizations make sense of data and turn it into clear, actionable insights. I am a Data Scientist with a strong interest in the full data lifecycle, from raw data preparation and analysis to modeling and insight delivery. My background is rooted in data science, with a solid foundation in machine learning and statistics. I work on transforming raw, unstructured data through data cleaning, exploratory data analysis (EDA), feature engineering, and predictive modeling, with a focus on practical and decision-oriented outcomes. In parallel, I am actively expanding my expertise in data engineering, developing a deeper understanding of data pipelines, data preparation, and the engineering foundations required to support scalable and reliable analytics and machine learning workflows. I primarily work with Python and SQL, leveraging tools such as Pandas, NumPy, and Scikit-learn to build, evaluate, and interpret models across classification, regression, and clustering problems. My work emphasizes clarity, accuracy, and real-world applicability rather than purely theoretical results. Driven by a genuine interest in the data field as a whole, I continuously seek to strengthen both my analytical and engineering skill sets, aiming to bridge the gap between data engineering and data science to deliver insights that are both technically sound and business-relevant.
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We developed a full-stack intelligent system that forecasts gold prices using deep learning and supports users with real-time market data, investment recommendations.
This project leverages machine learning to predict the likelihood of doctors prescribing specific medications. It addresses a critical challenge in the pharmaceutical industry.
I used the Decision Tree model to explore the Ames Housing dataset and accurately predict house prices based on key features.
I built a machine learning model to help charities identify potential donors by predicting whether individuals earn more than $50K annually.
Oceanfront houses are highly desirable and command higher prices, often owned by wealthier individuals seeking prime locations.
