1. obj_ID = Object Identifier, the unique value that identifies the object in the image catalog used by the CAS


2. alpha = Right Ascension angle (at J2000 epoch)


3. delta = Declination angle (at J2000 epoch)


4. u = Ultraviolet filter in the photometric system


5. g = Green filter in the photometric system


6. r = Red filter in the photometric system


7. i = Near Infrared filter in the photometric system


8. z = Infrared filter in the photometric system


9. run_ID = Run Number used to identify the specific scan


10. rereun_ID = Rerun Number to specify how the image was processed


11. cam_col = Camera column to identify the scanline within the run


12. field_ID = Field number to identify each field


13. 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)


14. class = object class (galaxy, star or quasar object)


15. redshift = redshift value based on the increase in wavelength


16. plate = plate ID, identifies each plate in SDSS


17. MJD = Modified Julian Date, used to indicate when a given piece of SDSS data was taken


18. fiber_ID = fiber ID that identifies the fiber that pointed the light at the focal plane in each observation

import pandas as pd 

import matplotlib.pyplot as plt

import seaborn as sns

import warnings

warnings.filterwarnings('ignore')​

Load Dataset:

star_df = pd.read_csv('star_classification.csv')

star_df.head()

star_df.columns

Drop Unwanted Columns:

star_df = star_df[['alpha', 'delta', 'u', 'g', 'r', 'i', 'z','class','redshift']]

star_df.head()

EDA:

galaxy = star_df[star_df['class']=='GALAXY']

star = star_df[star_df['class']=='STAR']

qso = star_df[star_df['class']=='QSO']

Alpha Vs Redshift:

plt.figure(figsize=(9,7))

sns.scatterplot(x='alpha',y='redshift',data=galaxy,color='r');

plt.title("Alpha Vs Redshift for Galaxy",{'fontsize':20});

plt.figure(figsize=(9,7))

sns.scatterplot(x='alpha',y='redshift',data=star,color='r');

plt.title("Alpha Vs Redshift for Star",{'fontsize':20});

plt.figure(figsize=(9,7))

sns.scatterplot(x='alpha',y='redshift',data=qso,color='r');

plt.title("Alpha Vs Redshift for QSO",{'fontsize':20});

Green filter Vs Red Filter:

plt.figure(figsize=(9,7))

sns.scatterplot(x='g',y='r',data=galaxy,color='b');

plt.title("Green filter Vs Red Filter for Galaxy",{'fontsize':20});

plt.figure(figsize=(9,7))

sns.scatterplot(x='g',y='r',data=star,color='b');

plt.title("Green filter Vs Red Filter for Star",{'fontsize':20});

plt.figure(figsize=(9,7))

sns.scatterplot(x='g',y='r',data=qso,color='b');

plt.title("Green filter Vs Red Filter for QSO",{'fontsize':20});

Target Feature:

plt.figure(figsize=(8,7))

sns.countplot(star_df['class']);

plt.title("Distribution of Target Feature",{'fontsize':20});

Data Spliting:

X = star_df.drop('class',axis=1)

y = star_df['class']

Conver Categorical column into numeric:

from sklearn.preprocessing import LabelEncoder

encoder = LabelEncoder()

y = encoder.fit_transform(y)

from sklearn.model_selection import train_test_split

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)

Data Scaling:

from sklearn.preprocessing import StandardScaler

scalar = StandardScaler()

X_train = scalar.fit_transform(X_train)

X_test  = scalar.transform(X_test)

Model Training:

from sklearn.ensemble import RandomForestClassifier

from sklearn.svm import SVC

from sklearn.neighbors import KNeighborsClassifier

import math

models = {

    KNeighborsClassifier(n_neighbors=3):'K-Neighbors Classifier',

    SVC():"Support Vector Machine",

    RandomForestClassifier():'Random Forest Classifier'

}

for m in models.keys():

    m.fit(X_train,y_train)

for model,name in models.items():

     print(f"Accuracy Score for {name} is : ",math.floor(model.score(X_test,y_test)),"%")

Random Forest gives higher accuracy on test data. So, we choose it for prediction.
Classification Report for Each Model:
Class 0: Galaxy
Class 1: QSO
Class 2: Star

from sklearn.metrics import classification_report

for model,name in models.items():

    y_pred = model.predict(X_test)

    print(f"Classification Report for : {name}")

    print(classification_report(y_test,y_pred))

Thank You ):