Preethi V

Technology Stack: Python, AWS, Machine Learning, Time Series Analysis, Statistical Analysis, and Recommendation Systems. Strengthened network security through machine learning, achieving 80% increased DDoS attack identification and counteraction efficiency. Conducted comprehensive Arbor data analysis, revealing intricate DDoS attack trends, resulting in 50% enhancement in network defenses via data-driven strategies. Enhanced user satisfaction by implementing recommendation engines using user behavior and preferences for personalized video recommendations on major Indian OTT platform. Assessed business requirements to create focused solutions. Developed technical requirement documentation for products. Collaborated with stakeholders to transform client requirements into turnkey solutions.

Technology Stack: Python, MySQL, Hive SQL, Cloudera, Machine Learning and Deep Learning. Proficient in crafting and deploying fraud detection models through identification of irregular patterns and risk scoring methodologies. Designed and engineered recommendation system employing advanced machine learning and statistical techniques. System delivers personalized insurance plan recommendations tailored to individual policyholder requirements. Skilled at analysis of intricate transactional data to uncover patterns, trends, and anomalies associated with money laundering, terrorist financing, and financial structuring. Proficient in credit risk modeling, covering borrower credit worthiness assessment, loan default prediction, and credit portfolio optimization within financial institutions.

Technology Stack: Python, MySQL, Microsoft Azure, Machine Learning, Deep Learning, SQL, and Hive. Successful implementation of AI technologies, including Machine Learning, Deep Learning, and Data Science tailored for smart city use cases, resulting in enhanced data-driven outcomes. Accomplished development and deployment of robust enterprise application that automated data science workflows, leading to improved regression, classification, and clustering tasks. Proficiency demonstrated in data extraction, feature engineering, exploratory data analysis, and model training, all of which played crucial role in addressing complex challenges and supporting well-informed decision-making. Developed intricate algorithms based on deep-dive statistical analysis and predictive data modeling. Identified, measured, and recommended improvement strategies for KPIs across business areas.

Technology Stack: Python, MySQL, PowerBI, Machine Learning and Statistical Analysis. Excellence in constructing multivariate regression models from restaurant data, delivering meaningful insights, enhancing informed decision-making, and improving restaurant operations. Employed time-series analysis techniques for trend detection and future outcome forecasting, facilitating informed decision-making and strategy development within restaurant sector. Used statistical methods to analyze data and generate useful business reports.

Technology Stack: Python, MySQL, Machine Learning and Statistical Analysis. Achieved 30% improvement in revenue-influencing factors identification through advanced techniques, enabling more informed strategic decisions in restaurant industry. Demonstrated consistent proficiency growth in model development, data analysis, and data-driven decisions, extracting invaluable insights that contributed to increase in revenue prediction accuracy.

Enthusiastic about analytics, with a dedicated emphasis on harnessing Big Data technologies such as Hadoop, Hive, Spark, Flume, and Pig. Proficiency in working with extensive datasets, including Hadoop, Hive, Spark, Flume, and Pig, to extract valuable insights and predictions. Skilled in using analytics and machine learning for data-driven decisions, enhancing business strategies and outcomes.

Distinguished Teaching Faculty in Electrical and Electronics Department. Consistently achieved high academic performance among students through effective teaching.

At postgraduate level, completed project titled "Simulation of Voltage Source Converter-Based Multi-Terminal HVDC System using RTDS," focusing on control and operation aspects of Multi-Terminal DC (MTDC) networks. Emphasized control and operation aspects of multi-Terminal DC networks within project work.