Git Product home page Git Product logo

gaiborjosue / exoplanet_prediction_ml Goto Github PK

View Code? Open in Web Editor NEW
0.0 1.0 0.0 1.63 MB

The Exoplanet Predictor Version 1.0 - Predict the number of planets of a system based on the stellar characteristics. The usage of this tool is through the command line (CMD).

Home Page: https://edwardgaibor.me

License: MIT License

Jupyter Notebook 99.46% Python 0.54%
exoplanet-analysis machine-learning-algorithms decision-tree-classifier

exoplanet_prediction_ml's Introduction

Capture

The Exoplanet Predictor

The Exoplanet Predictor Version 1.0 - Predict the number of planets of a system based on the stellar characteristics. The usage of this tool is through the command line (CMD).

Usage

Single Prediction

To use this tool you have two options, the first one is to predict the number of planets that a system with only ONE star has. For this, enter the directory of the model_predictor.py file and open the CMD. And type:

model_predictor.py -h

This will display the help menu of the tool, in which you can find the 8 required inputs with their description.

For example, you will need to enter the following in the command line: model_predictor.py -t VALUE -r VALUE -m VALUE -mt VALUE -a VALUE -d VALUE -v VALUE -l VALUE

After you enter the required fields, the algorithm will do its job ;).

Multiple Predictions

So, what happens if you need to predict the number of planets for multiple systems at once? Here is when the second option of usage comes to the game. For this option you will need to provide the path of the CSV (dataframe file) in which you store all the hundreds of systems. Please note that for the headers of your database it must contain st_teff st_rad st_mass st_met st_age st_dens st_radv st_logg, separated with a comma ",". If you need an example of what a dataframe file should look like please refer to Data/PSCompPars_2021.04.20_19.50.36.csv

This means you should enter the following in the command line: model_predictor.py -data PATH TO YOUR FILE

After you enter the required fields, it will promt you the following: The Predictions were saved to "predictions.txt" file. Each line of the TXT file corresponds to each prediction. PD: The datafile was cleaned, which means that every NaN value was removed.

You can find the predictions.txt file in the current directory.

References

The database used to train the algorithm was produced by the NASA Exoplanet Archive http://exoplanetarchive.ipac.caltech.edu

For a web-based experience please visit: https://github.com/leiDnedyA/interactive-exoplanet-predictor

exoplanet_prediction_ml's People

Contributors

gaiborjosue avatar

Watchers

 avatar

exoplanet_prediction_ml's Issues

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.