Jayateja Vanamu

Results-driven Data Analyst with a strong focus on productivity and efficiency in task completion. Expertise in data visualization, statistical analysis, and predictive modeling supports informed decision-making. Proven problem-solving skills and critical thinking abilities enhance team collaboration and project success. Committed to leveraging data insights to drive strategic initiatives.

• DATABRICKS: LAKEHOUSE FUNDAMENTALS
• DATABRICKS: GENERATIVE AI FUNDAMENTALS
• DATACAMP: MACHINE LEARNING WITH CARET IN R
• COURSERA: IBM GENERATIVE AI FUNDAMENTALS

Baggage Handling Analysis Project - American Airlines Data Challenge, Conducted a comprehensive analysis of baggage handling operations at American Airlines to enhance operational efficiency, resource allocation, and predictive modelling for future bag arrivals., Conducted Exploratory Data Analysis (EDA) involving data preprocessing, identifying hourly baggage handling patterns, and day-of-week analysis., Analyzed bag load consistency, station efficiency, early bags, and identified peak periods using descriptive statistics and visualizations., Developed prediction models (Linear Regression, Decision Tree Regressor, Random Forest Regressor) for bag arrivals with a focus on improving accuracy and resource planning., Utilized time series forecasting techniques (SARIMA model) to predict future bag arrivals with precision, aiding in efficient resource management and operational planning., Provided insights into hourly bag count trends, day-of-week analysis, and arrival patterns, enabling actionable recommendations for operational efficiency, resource allocation, and performance monitoring., Recommended strategies for operational improvements, resource planning, forecasting models' adoption, and time series analysis utilization., Highlighted operational implications including resource optimization, planning for peak hours, and informed decision-making based on hourly patterns., Emphasized the project's real-world impact, contributing to operational excellence, risk mitigation against baggage loss/theft, and optimizing baggage policies. Customer Feedback Analysis with Sentiment Prediction, Built a sentiment analysis system to classify and extract insights from customer feedback, leveraging AWS Glue for data preparation, SageMaker for fine-tuning BERT models, and Lambda for real-time inference. Integrated AWS Kendra for intelligent feedback search, enabling actionable insights for improved decision-making.