The majority of the project was developed during a 24-hour hackathon, with subsequent modifications primarily focused on the API component and the elimination of redundant code.
The project is now live and accessible through the deployment link below:
https://tata-power-hackathon-coep.onrender.com/
Please feel free to visit the link to explore the live version of our application.
The entire data visualization component dynamically generates and saves images on the server in real-time without hard coding, except for the explanation of the graphs and the suggestion to efficiency. However, when attempting to provide efficiency recommendation, we encountered unexpected errors caused by the sklearn module giving warnings and the child_process module repeatedly rejecting promises. To address this, we tested the data on our local machine, which was provided by Tata Power, and derived efficiency recommendations. We subsequently hard-coded these results.
Company login credentials were exclusively provided to the judges for accessing data visualization graphs and other materials, primarily focusing on the generalized trend of data.
Upon a successful company login, a company employee will see a list of options, as shown in the images. After clicking on any option, results will be received within 15-30 seconds. This delay is due to extensive computations taking place in the background, where the server generates images and saves them in real-time.
I want to extend a huge shout-out to my team members Aditya, Sourish, and Vatsal for their exceptional contributions. They were responsible for data preprocessing, data visualization, providing efficiency recommendations, conducting user behavior analysis, and ultimately, crafting these Big and Complex Python functions that serve as the backbone of this project.