High-Level Project Summary
Using the BI and Machine Learning interface and a user-friendly application, we are able to make the analysis of Parker probe data easier and more interesting. We include various matrices, altitude mean functions, latitudes from satellite areas, longitudes, and time by making sub trees with day, month, and year. With 95% accuracy, the linear projection method provides the user with the maximum depth and minimum frequency after selecting a satellite name. To grow a massive bond between space and people, we allow users to upload their own data or report by data validation and logistic terms.
Link to Final Project
Link to Project "Demo"
Detailed Project Description
By analyzing existing picture information, we created a BI and ML interface with a user-friendly application including satellite code, satellite number, longitude, latitude, timestamp, frequency, and profundity. As a result of the recent NASA Solar Parker Probe data, we were able to utilize it, for the creation of our BI interface with fetched matrix, linear projection, and random forest so that users can get a 2D view for superior understanding. The BI tool is especially utilized to make things simpler for both clients and engineers. Users would have the choice to upload their own data by [reforming appropriate data validation and logistic terms, anyone can share and upload their own data to improve space science technology development. Here is the output of each created dashboard and linear projection.
The dashboard was created by POWER BI and Data was analyzed by Python (Data Grip), Jupyter Notebook, and Orange for easier modeling. Data sampler and linear projection are shown by Mitosheet to reduce time and space complexity from O(2n) to O(n). The application “SUNIX” was designed and developed by Google Glide. All these things are integrated with the Atlassian project Agile.
Link to the App
https://sunix.glideapp.io/
Our project solves the complexity of data analysis and data understanding for every level of users and developers. Now, users will take a deep sense of sun and parker probe with the easiest way by clicking on dashboard slicers or application tabs. On the other hand, developers will also be able to make understand data to any user o decision makers for a better upgradation of technologies. Our project is important as it is showing every satellite code and number with their corresponding time, depth, frequency, and latitude. Kids will also drag their attention to space data analysis after seeing the easier methods and techniques of our project. Our project obtains an interest in every segment of space science with “SUNIX PROBE’"
Space Agency Data
We made extensive use of the ACE (SCISAT) dataset ( https://data.asc-csa.gc.ca/dataset/02969436-8c0b-4e6e-ad40-781cdb43cf24 )for analyzing the gasses individually such as Acetone, Acetylene, Ethane, etc. We have also used the SDO Data hub (https://sdo.gsfc.nasa.gov/data/) of NASA which helped us for a better understanding of probe, sun, and frequency to make our own ML interface.
We also used the 3D models of Goddard Media Studios (https://svs.gsfc.nasa.gov/14036) to make the linear project model trained and accurate.
Hackathon Journey
We passed an amazing time while developing the project because we had knowledge about data science, but it was seeming a bit tough at the stage of linear projection with the massive dataset. We were a little afraid to work on Apache hive as we had 40% knowledge of it initially. But we adopted new technologies and tried to implement all these things in a tricky way and we have done at last.
Special thanks to Sritha Zith Dey Babu for Leading the team, Vishali Pathania for primary query and dashboard creation, Saurabh Verma for great video and graphical presentation, Vishesh Chaudhary for creating power apps, Ayush Pal nailed the projection of the project and Dhiraj was outstanding with creativity and theorems.
References
Full project link:
GitHub - team-secret-hub/SUNPROBE
That link contains:
1) Sun data dashboard
2) Ground station dashboard
3) Collective dashboard of all three datasets.
Android Application installation:
Click on the link below or Scan this QR code:
ML projection:
Jupyter notebook: Project Jupyter | Home
Data Modelling:
Orange: https://orangedatamining.com/
Dashboard Creation:
Power BI: https://powerbi.microsoft.com
Datasets:
https://izw1.caltech.edu/ACE/ASC/DATA/level3/mag/ACESpec.cgi?LATEST=1
https://sdo.gsfc.nasa.gov/data/dashboard/?d=0193;0171;HMIBC
https://sdo.gsfc.nasa.gov/data/latest48.php?q=ar_map
https://sppgway.jhuapl.edu/rPlotTime
Tags
#sun #power bi #parker probe #NASA #Orange