Final-year B.E. ECE student (CGPA: 8.78) from Velammal Institute of Technology, Chennai, with internship experience in Data
Science. Skilled in Machine Learning, Deep Learning, NLP, and Generative AI with hands-on exposu…
Final-year B.E. ECE student (CGPA: 8.78) from Velammal Institute of Technology, Chennai, with internship experience in Data
Science. Skilled in Machine Learning, Deep Learning, NLP, and Generative AI with hands-on exposure to LangChain-based
systems. Certified in AWS ML Foundations and IBM Data Fundamentals. Actively seeking a Data Scientist or AI/ML Engineer
role where I can contribute to building intelligent, data-driven solutions from day one.
Performed data preprocessing and EDA on a 7,000+ row real-world dataset using NumPy and Pandas to deliver data-driven insights. • Built end-to-end visualizations (confusion matrices, heatmaps, feature importance) using Matplotlib and Seaborn across 2 production-level projects. • Developed and evaluated machine learning recommendation system prototypes using Scikit-learn in a real-world R&D environment. • Leveraged Generative AI and automation to optimize data pipelines and workflows beyond core project scope.
Applied Python (NumPy, Pandas, scikit-learn) for data preprocessing, cleaning, and exploratory analysis
Conducted data visualization using Matplotlib/Seaborn to derive actionable insights
Collaborated on recommendation system prototypes and explored generative AI concepts
Demonstrated strong problem-solving, analytical reasoning, and commitment to learning emerging AI tools
Applied Python (NumPy, Pandas, scikit-learn) for data preprocessing, cleaning, and exploratory analysis
Conducted data visualization using Matplotlib/Seaborn to derive actionable insights
Collaborated on recommendation system prototypes and explored generative AI concepts
Demonstrated strong problem-solving, analytical reasoning, and commitment to learning emerging AI tools
Completed a one-month intensive internship focused on Full Stack Development under industry guidance
Worked with technologies like HTML, CSS, JavaScript, React.js, Node.js, and MongoDB to build scalable web applications
Built an end-to-end ML pipeline on real telecom data to predict customer churn using Random Forest classifier. • Performed EDA, data cleaning, label encoding, and feature engineering on 7,000+ customer records. • Achieved 79% accuracy and 69% ROC-AUC; saved 5 insight charts including confusion matrix and feature importance plots.
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