In this project, we will be making a predictive model using data collected through the Sloan Digital Sky Survey (SDSS) to classify: Galaxies, Stars, Quasars, through the unique properties using random tree classifier method. Also, we will also build an analysis of space objects properties based on the data and create a 3D model of space objects in the dataset, to get a better visualzation of what the spread of the universe looks like.
Project ini akan mencakup pembuatan model prediksi yang dapat mengklasifikasikan objek langit yang tertangkap dalam dataset Sloan Digital Sky Survey (SDSS) berdasarkan ciri" unik benda langit (Bintang, Galaksi, Quasar) menggunakan metode random tree classifier. Selain itu, juga membuat analisis perbedaan objek langit dari ciri" nya, serta membuat model 3D dari objek langit yang terdapat pada dataset, untuk mendapatkan visualisasi persebaran benda langit alam semesta.
Sky Portrait from SDSS
Machine learning model using Random Forest Classifier results spotted: 5000 Galaxies 4119 Stars 881 Quasars with an accuracy score of 98,93%
3D Mapping of the classified objects using coordinate data (declination and right ascension) and photometric redshift for calculating distances
Check out the powerpoint file Here for detailed analysis
(Pro Tip: run in the latest version of PowerPoint in Full Screen for best appearance!)
