Arjjun S

Worked on AI focused projects including model development, data preprocessing, and automation tasks • Collaborating with the team to build and test real world AI solutions • Learning industry workflows, version control, and best practices • Improving skills in Python, machine learning, and problem solving

Successfully completed a 10 week virtual internship focused on Artificial Intelligence and Machine Learning, supported by India Edu Program and Google for Developers. • Gained practical experience in designing, training, and evaluating AI/ML models • Explored essential topics such as supervised and unsupervised learning, model optimization, and data preprocessing • Worked on real world datasets using Python based tools and frameworks • Enhanced understanding of AI workflows, cloud based deployment, and model performance analysis Institution: SRM Institute of Science and Technology, Kattankulathur

Successfully completed a 10-week virtual internship focused on Artificial Intelligence and Machine Learning, powered by AWS Academy. • Gained hands-on experience in building, training, and deploying ML models • Explored core AI/ML concepts including supervised learning, unsupervised learning, and model evaluation techniques • Worked on real-time datasets and AWS cloud tools for scalable model deployment • Strengthened skills in Python, Jupyter Notebooks, and data visualization Institution: SRM Institute of Science and Technology, Kattankulathur

Completed an internship focused on developing predictive models for healthcare, specifically targeting inpatient readmission prediction within 30 days. Handled the end-to-end machine learning pipeline including data preprocessing, exploratory data analysis, feature engineering, and model development using XGBoost (achieved 86% Accuracy and 91% ROC AUC). Worked on improving model interpretability for clinical deployment and proposed integration into electronic health record (EHR) systems for real-time decision support.

Completed two hands-on project: 1. Stock Prediction-Developed a KNN-based model to predict NIFTY stock price movements using historical market data. 2. Heart Attack Prediction - Built a logistic regression model to predict heart attack risk based on patient health metrics.

Completed two hands-on projects: • Song Predict – Built a music recommendation model using clustering techniques. • Cardiovascular Disease Prediction – Trained and evaluated multiple machine learning models (SVM, KNN, Decision Tree, Logistic Regression, Random Forest). • Achieved highest accuracy (72%) using Support Vector Machine (SVM). • Programmatically identified the best-performing model based on evaluation metrics.