Walk Forward Validation (WFV) involves a training window that moves forward in time, training the model on historical data and then validating it on future, unseen data points. Unlike traditional cross-validation where data is randomly split, WFV respects the sequence of time, making it ideal for datasets with time-dependent features like stock prices, weather patterns, or sales figures.
In the GDSC7 challenge, we’ve upgraded our agent system to create and display charts in response to user queries, using AWS S3 for image storage. The new chart.py tool leverages Pandas, Matplotlib, and Seaborn to generate various chart types, enhancing our system’s capability to present data visually. This integration allows us to effectively showcase complex information, such as the correlation between GDP and reading skills from the PIRLS 2021 study, improving user engagement and interaction.
In this post, I describe how I overcame AWS login challenges in a coding competition by using a method override trick. By defining a new function for authentication and dynamically replacing the existing method in an instantiated object, I was able to experiment with the Embedchain package without altering its class definition. This technique allowed for seamless integration with AWS services and added a valuable tool to my programming arsenal.