I am Sunita Dhruw, a passionate Software and Data enthusiast with a solid background in
mathematics and computer science. I have developed strong proficiency in Python, Java, SQL,
and various data analysis tools, gaining practical experience in data cleaning, exploration, software development,
and the implementation of machine learning algorithms.
Through internships at YBI Foundation and Accenture North America, I have contributed
to a variety of projects, including web scraping, predictive modeling, and AI-driven
business solutions. I have also led the development of end-to-end projects such as a
Movie Recommender System and Credit Card Fraud Detection, showcasing my expertise in libraries
like NumPy, Pandas, and Scikit-Learn. My commitment to continuous growth is reflected in my
pursuit of certifications and participation in advanced programs.
This is a content based movie recommender system which is trained on a dataset containing
5000 movies, This is an end to end machine learning project, which recommends movies using
cosine similarity.
Library used : Numpy, Pandas, Sk-Learn, NLTK, Streamlit.
This project is a machine learning-based solution for detecting credit card fraud. It
utilizes Logistic Regression, a powerful algorithm for binary classification, to identify
potentially fraudulent transactions.
Library used : Numpy, Pandas, Sk-Learn, Python, Streamlit.
The primary purpose of this application is to facilitate student registration for
debate events. It also maintains accurate records of all students course-relayed
information.. It utilizes the Spring
MVC design pattern and Thyme leaf serves as its
front end.
Library used : Java, Spring Boot, Data JPA, HTML, CSS.
* Automated process of data extraction using SQL which reduced time by 25%.
* Processed, cleaned, and integrated large data sets from multiple sources into a
comprehensive database to support robust data analysis.
* Enhanced accuracy of predictive models by 20% using machine learning techniques.
* Engineered a secure payment system for an e-commerce client, integrated with Trustly using
Spring Boot.
* Implemented a validation framework and error handling system, enhancing security by
20%.
* Utilized agile methodologies and Jira, boosting project delivery efficiency by 25%.
* Supported students, improving students academic performance by 30% with personalized
tutoring sessions.
* Applied innovative teaching strategies to teach students, resulting in 25% increase in
student engagement.
* Encouraged confidence and consistent learning growth.
Developed a comprehensive skill set in data science and analytics, covering various aspects of data processing and analysis. Proficiency in Python and SQL enables efficient data manipulation and querying, while experience with libraries like Pandas, NumPy, and Scikit-Learn supports robust data analysis and model development. Expertise in data visualization tools such as Matplotlib and Seaborn ensures clear and insightful data presentations. Additionally, my knowledge of machine learning techniques and statistical analysis allows for the development of predictive models and data-driven solutions. Together, these skills form a versatile toolkit that empowers me to extract valuable insights, drive data-driven decision-making, and enhance business outcomes.
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