I'm `SANJANA HOMBAL` a recent graduate with an MSc in Data Science and Analytics from the University of Hertfordshire. Skilled in using large datasets and advanced algorithms, I specialize in deriving actionable insights for real-world applications. My passion lies in leveraging technology to solve complex problems and I am actively seeking roles in data science and analytics.
If you're interested in discussing data science, machine learning, or the latest trends in analytics, or if you'd like to collaborate on projects, feel free to connect with me. I'm always eager to connect with fellow professionals and explore new opportunities in the tech and analytics fields.
If you're interested in discussing data science, machine learning, or the latest trends in analytics, or if you'd like to collaborate on projects, feel free to connect with me. I'm always eager to connect with fellow professionals and explore new opportunities in the tech and analytics fields.
My journey in computer science and data science has fueled my passion for analytics and machine learning. I am now eager to further immerse myself in the field, aiming to join a role where I can apply my skills in data-driven problem-solving. I thrive in challenging environments, using data to uncover insights and deliver solutions. I enjoy working collaboratively with cross-functional teams, leveraging diverse skill sets to achieve project goals. Committed to staying current with the latest trends in data science and analytics, I continuously seek new challenges to expand my knowledge and expertise.
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- Description: Analyzed "Heart Condition" dataset using LibSVM (RBF kernel) and J48 models in WEKA software. Optimized parameters via grid-search and parameter-search and performed 10-fold cross-validation to compare models.
- Description: Investigated differences in the mean of live births between upstate and downstate counties using R Programming. Conducted pairwise.t.test() and visualized results, achieving statistically significant insights.
- Description: Developed a webpage using Python and HTML to detect fraudulent health insurance claims with the XGBoost algorithm, achieving 98% accuracy in identifying fraud.
- Description: Created a real-time face mask detection system using CNN classifiers in Python, improving accuracy to 97% and reducing latency by 25%.
- Description: Investigated various algorithms and vectorization methods for sentiment analysis. Achieved distinction for demonstrating the effectiveness of different machine learning models and their performance on textual data.
