For this Hackathon our inspiration aspired from other powerful data analysis tools such as Tableau and Power BI. We liked the idea of creating a tool for EOG resources because it will provide there Data Engineers to have a streamlined process to analyze there data instead of configuration other applications to fit there data.
What it does
It analyzes 20 different CSV files with over 10,000 datapoints in each file. After our application analyzes this information it provided the users of these applications with Charts, Graphs and much more datapoints that would be helpful for any data driven team. We have went a step ahead to create a Machine learning Model that predicts what type drill bit should be used in future expeditions. This model works by the user putting in the parameters of future expeditions, then it would give the user the output of the drilbit's that should be used.
How we built it
Tools we have used to create this full stack applications was by using Python, R, Streamlit, and Core ML
Challenges we ran into
Challenges we have ran into was cleaning all 20 of the datasets that was given by EOG and another biggest challenge we had was figuring out all the computations for the charts and graphs we had to display.
Accomplishments that we're proud of
We are proud of converting Apple's Core ML model we have made into a tensorflow model in order to run in a website, Another we are proud of learning streamlit and using it for this project
What we learned
We have learned on how to create ML models and converting them into different libraries so it can work any application no matter what tech stack the application is using. We have also learned on how to use Streamlit features to graph and chart the findings we found.
What's next for EOG Resources Analysis & Prediction
Our future plan is to make the model into a supervised learning model and implementing a deep learning model so it can learn and optimize on its own.