I am a Data Science and Machine Learning student with hands-on experience in data preprocessing, exploratory data analysis (EDA), feature engineering, and predictive modeling. I have worked on practical ML projects such as a Heart Disease Prediction system built using multiple algorithms including Logistic Regression, Decision Tree, Random Forest, SVM, kNN, Naive Bayes, and XGBoost where XGBoost achieved 94.6% accuracy and a 98.2% ROC–AUC, showing strong potential for early heart disease risk detection. I have also designed and developed an AI-based Mental Health Support Chatbot by fine-tuning a pretrained T5 transformer model, focusing on safe, high-quality, and empathetic responses through iterative training and evaluation. I am comfortable working with Python, SQL (MS SQL Server, PostgreSQL), and core data science libraries such as NumPy, Pandas, and Scikit-learn, along with NLP tools from Hugging Face and Streamlit for prototyping applications. I use Git and GitHub for version control, project organization, and collaboration. Currently, I am learning Deep Learning and MLOps to strengthen my understanding of model training, evaluation, and deployment practices, and I am focused on steadily growing my skills by building practical and responsible AI solutions.