I am a highly motivated Software Engineer with expertise in C++, Python, Java, and Machine Learning. My focus is on reducing losses by predicting consumer return rates and implementing effective strategies, such as a Gibberish Detector for identifying incorrect address inputs and developing machine learning models to prevent high-risk individuals from choosing cash on delivery options. I excel in creating comprehensive UML diagrams to communicate and align stakeholders' expectations with application architecture. Additionally, I have experience in RTO analysis using machine learning techniques, enhancing my data analysis, modeling, and prediction skills. With strong communication and problem-solving abilities, I have successfully collaborated with team members, secured sponsorships and funding for events, and represented university sports teams in state and district-level competitions.