Yamini Priyanka Kandikattu

Project Title: Covid-19 Data Analytics and Forecasting

Duration: June 2020 - June 2020

Project Description:

In this project, I leveraged data analytics and machine learning techniques to predict the number of Covid-19 active cases for future dates. This involved using the fbprophet machine learning library developed by Facebook. My primary goal was to provide accurate forecasts that could assist in understanding and planning for the pandemic's progression.

Project Title: Disease Predictions from Symptoms

Duration: Aug 2022 to Dec 2022

Project Description:

In this project, I developed a predictive model to forecast diseases based on symptoms exhibited by patients. The primary objective was to assist healthcare professionals in early disease detection and intervention. Leveraging a large medical dataset, I applied machine learning algorithms and natural language processing (NLP) techniques to identify disease patterns and make accurate predictions.

Project Title: Predicting Mortality Rates in the USA

Duration: Jan 2023 to June 2023

Project Description:

In this project, I focused on predicting mortality rates in the United States. By analyzing a comprehensive dataset encompassing various socio-economic and healthcare-related factors, I aimed to identify key determinants of mortality rates at the state and county levels. This project had practical applications for public health policy and resource allocation.

Link : https://sites.google.com/d/1FmzQEfP93oyUlDm3eu9bpcrrYMzJTGTP/p/1hX9UX1nvGoPFz0OOe7bDZ0bVtNWLj3UB/edit

Project : Optifly

Duration : August 2023 to December 2023

Project Description : OptiFly is a comprehensive data-driven travel optimization project that leverages advanced cloud technologies. The project begins by extracting travel data from Bureau of Transportation Statistics (BTS) and Aviation Stack, storing it in Google Cloud Storage (GCS). The data undergoes transformation using Google Cloud Dataproc, employing Apache PySpark for efficient handling of big data tasks. Fact and dimension tables are created, adhering to a star schema, ensuring a structured format suitable for analysis. The orchestrated Extract, Load, Transform (ELT) process is managed by Apache Airflow with Docker support. The transformed data is loaded into Google BigQuery, a scalable data warehouse. Finally, Looker and Tableau fetch data from BigQuery to create an intuitive dashboard, providing actionable insights for optimal travel decision-making

Project Title: Flying High with AI

Project Description : Embark on a thrilling adventure with 'Flying High with AI,' a project that explores the synergy between artificial intelligence (AI) and the exhilarating world of flight. This innovative endeavor focuses on implementing AI, particularly neural networks, to teach an autonomous entity to navigate the challenges of flight. Drawing inspiration from the simplicity of gaming, the project leverages the principles of reinforcement learning, allowing the system to learn and adapt through trial and error. 'Flying High with AI' is not just a technical exploration; it's a captivating journey into the potential of AI in a dynamic and engaging context. Join us as we witness the convergence of cutting-edge technology and the joy of flight, showcasing the incredible capabilities of AI in a thrilling and accessible manner