Resume

Spearheaded the integration of TensorFlow and NLP models with Node-RED, significantly enhancing automation and efficiency in data analysis processes.

Innovated and implemented advanced NLP algorithms, improving ML model accuracy and contributing to groundbreaking research in machine learning integration.

Fostered cross-functional collaboration, driving the adoption of best practices in ML model development and deployment using Python and relevant ML libraries.

Engineered a sophisticated market basket analysis using Python and the Apriori algorithm, analysing over 500,000 transaction datasets to uncover product associations, subsequently increasing cross-selling strategies by 20%.

Engineered a machine learning model that categorized customer sentiment with 95% accuracy, leading to a 30% improvement in targeted marketing strategies and customer satisfaction rates.

Analyzed over 5TB of structured and unstructured data using advanced machine learning algorithms in Python, enhancing predictive models and leading to a 20% increase in decision-making efficiency.

Developed a machine learning model that analyzed and predicted customer behavior with 95% accuracy, leveraging Python, R, and TensorFlow to parse through 10TB of user data, which ultimately informed business strategy and decision-making.

Engineered a machine learning model to categorize customer feedback with a 95% accuracy rate, streamlining the decision-making process and increasing response efficiency by 30% during the summer internship.

Integrated Langchain, and Llama technologies to develop an advanced chatbot prototype during a summer internship, resulting in a 50% improvement in natural language understanding and response accuracy.