I am a highly skilled and experienced software developer with a passion for programming in Python and Java. With a Master's degree in Data Science, I possess a strong understanding of data analysis, machine learning, and statistical modeling, which I have leveraged to deliver innovative solutions addressing complex business problems. With over three years of experience as a Java developer, I have honed my programming skills and am well-versed in developing efficient, scalable, and high-performance applications. I possess a deep understanding of the software development life cycle (SDLC) and have experience in agile methodologies, making me an asset to any team.
Learn moreMy projects showcase my skills and expertise in various domains, including data science, machine learning, and full-stack development, demonstrating my ability to deliver innovative solutions and tackle complex problems. Each project represents a unique challenge I've embraced, highlighting my dedication to continuous learning and pushing boundaries in pursuit of excellence.
In this project, three different classifiers, including Random Forest, Decision Tree,and Multinomial logistic regression was applied in a distributed fashion to predict the genre of music based on various features. The models accuracy and F1 score are calculated and compared their effectiveness, alongside data processing techniques, to identify the effectiveness of these techniques for music genre classification.
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The Auto-ML project automates machine learning tasks for regression and classification using Python, Pandas, pyCaret, and Streamlit. It provides functionalities for data uploading, profiling, model training, and prediction, making it easy for users to analyze and make predictions on their datasets without extensive manual intervention.
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A book recommendation matrix file (CSV), generated using MapReduce HPCI and employing user-based collaborative filtering technique, containing the likeability scores of the top 10 books for over 9000 books. Streamlit was utilized to develop a user interface (UI) for the recommendation system.
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The project aims to provide the construction team with data-driven insights using Tableau data visualization to optimize room renovation and construction decisions for a Hotel. The project tend to identify rooms needing renovation, suggested high-demand room types with optimal rates, and determined parking slot requirements.
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