ml-star-classification's Introduction

Machine Learning Star Classification

Dataset used: https://www.kaggle.com/datasets/fedesoriano/stellar-classification-dataset-sdss17/data

Dataset Structure

The data consists of 100,000 observations of space taken by the SDSS (Sloan Digital Sky Survey). Every observation is described by 17 feature columns and 1 class column which identifies it to be either a star, galaxy or quasar.

obj_ID = Object Identifier, the unique value that identifies the object in the image catalog used by - the CAS
alpha = Right Ascension angle (at J2000 epoch)
delta = Declination angle (at J2000 epoch)
u = Ultraviolet filter in the photometric system
g = Green filter in the photometric system
r = Red filter in the photometric system
i = Near Infrared filter in the photometric system
z = Infrared filter in the photometric system
run_ID = Run Number used to identify the specific scan
rereun_ID = Rerun Number to specify how the image was processed
cam_col = Camera column to identify the scanline within the run
field_ID = Field number to identify each field
spec_obj_ID = Unique ID used for optical spectroscopic objects (this means that 2 different observations with the same spec_obj_ID must share the output class)
class = object class (galaxy, star or quasar object)
redshift = redshift value based on the increase in wavelength
plate = plate ID, identifies each plate in SDSS
MJD = Modified Julian Date, used to indicate when a given piece of SDSS data was taken
fiber_ID = fiber ID that identifies the fiber that pointed the light at the focal plane in each observation

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