The given challenge asked us to create a dashboard for a Plant Manager, so we set about creating a useful, quick-glance dashboard built entirely as a WebApp so that the data can be viewed on any device on the local network. With portability and cross-platform functionality in mind, this WebApp is platform and device agnostic.
Processes the given .CSV data into dymanic graphs that fill up a dashboard view, and output data anomalies to the Alerts page
Using Python, with the Streamlit API to handle frontend work.
Learning Streamlit and interfacing with python was a challenge we ran into. Along with working with Pandas dataframe and bringing it all together to work as a cohesive system. We also had an issue with python 3.11 not being compatible with the streamlit library we were using. Converting Unix time to GMT gave us a bit of trouble but we worked through it and got it to work.
Utilising the Pandas and streamlit library. Being able to filter data through date and time. We came together to successfully collaborate and contribute towards the best experience for the end user. We also made the Webapp work cross-platform in a manner where it is platform and device agnostic
Working with Pandas dataframe and streamlit library. Working with different branches on Git.
Implementing Machine learning for time series data forecasting.