○ Established and deployed a predictive churn model for healthcare clients, utilizing advanced regression techniques, and identifying critical factors influencing patient attrition, such as treatment outcomes, cost, and access to care, implemented targeted retention strategies, a 20% reduction in patient churn, significantly improving patient satisfaction and loyalty. ○ Developed and deployed a recurrent neural network (RNN) using TensorFlow for fraud detection in healthcare insurance claims, achieving a 98% accuracy rate in identifying fraudulent claims. ○ Built a chatbot using LangChain and a pre-trained LLM to provide automated customer support for healthcare insurance inquiries, reducing response times by 50% and improving customer satisfaction. ○ Implemented predictive models using machine learning algorithms (random forest, logistic regression) to forecast patient readmission rates with 70% accuracy, leading to a 50% reduction in healthcare costs. ○ Accomplished an Azure-based NLP pipeline to analyze customer feedback, leveraging BERT embeddings with Hugging Face Transformers and Azure ML to extract sentiment and key themes, driving a 20% increase in customer satisfaction.

○ Developed a robust recommendation system utilizing NLP techniques and NLTK library to enhance product discovery. Extracted meaningful insights from product descriptions and user reviews to create personalized recommendations, resulting in a 20% increase in click-through rates and a 30% boost in sales. ○ Achieved a substantial 30% boost in overall model performance by integrating Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) into a hybrid architecture for complex data analysis tasks ○ Created interactive dashboards in Tableau to visualize 50+ customer segmentation models, enabling marketing teams to identify high-value target segments and achieve a 20% increase in customer acquisition. ○ Trained XGBoost model to identify products and promotions correlated with higher customer purchases during promotional seasons. Model insights helped create an optimized promotion strategy for peak season, leading to a 10% increase in demand for promoted products and an 8% growth in overall profits. ○ Generated anomaly detection models using statistical and machine learning techniques to identify outliers and anomalies in large- scale datasets. This led to a 40% reduction in false positives and improved anomaly detection accuracy. ○ Employed a comprehensive suite of data science and machine learning libraries such as Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn, PyTorch, and NLTK to refine models and workflows, boosting development speed by 25%. ○ Streamlined the ML model deployment process by integrating AWS CodePipeline, achieving a 50% reduction in deployment time and supporting continuous integration/continuous delivery (CI/CD) for machine learning projects.