Predicting Stars, Galaxies & Quasars with ML Model

This project focuses on building machine learning models to classify celestial objects into stars, galaxies, and quasars using a provided dataset. The following sections detail the workflow and the steps taken in the notebook.

Workflow of the Notebook

1. Introducing Dataset
2. Importing Necessary Libraries and Modules
3. Exploring the Dataset
4. Preparing Data for the Model
5. Scaling the Data and Checking Distribution Plots
6. Building ML Models and Evaluating Results

Dataset Introduction

The dataset used for this project contains information about celestial objects. The classification task involves predicting whether an object is a star, galaxy, or quasar based on various features provided in the dataset.

Importing Necessary Libraries and Modules

We utilize several Python libraries for data manipulation, visualization, and building machine learning models. Here is the list of the libraries:

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
import warnings
warnings.filterwarnings("ignore")

%matplotlib inline

Loading and Exploring the Dataset

Importing the Dataset

for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))
        
data = pd.read_csv("/content/Skyserver_SQL2_27_2018 6_51_39 PM.csv")
data.head()

Dataset Dimensions and Preliminary Analysis

data.shape
data.describe()

Dropping Unnecessary Columns

data.drop(['objid','specobjid'], axis=1, inplace=True)
data.head(10)

Checking for Null Values

data.info()

The dataset is complete with no missing values.

Encoding Target Variable

le = LabelEncoder().fit(data['class'])
data['class'] = le.transform(data['class'])
data.head(10)

Final Dataset Information

data.info()

Data Preparation

Splitting Data into Features and Target

X = data.drop('class', axis=1)
y = data['class']

Data Scaling

Standardizing the dataset to have a mean of 0 and a standard deviation of 1.

scaler = StandardScaler(copy=True, with_mean=True, with_std=True)
X = scaler.fit_transform(X)

Building Machine Learning Models

We employ several machine learning models to classify the data:

• Decision Tree Classifier
• Logistic Regression
• Naive Bayes
• K-Nearest Neighbors
• Support Vector Machine

Training and Evaluating the Models

# Splitting the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Example for Decision Tree Classifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_pred = dt.predict(X_test)
print(f"Decision Tree Accuracy: {accuracy_score(y_test, y_pred)}")

# Repeat similar steps for other models

license

This program is under the MIT License

Conclusion

This notebook demonstrates the process of loading a dataset, performing exploratory data analysis, preparing the data, scaling it, and finally building and evaluating several machine learning models to classify celestial objects.

References

• NumPy Documentation
• Pandas Documentation
• Matplotlib Documentation
• Seaborn Documentation
• TensorFlow Documentation
• Scikit-Learn Documentation

---

Upload this notebook to a Kaggle session, link the dataset, and run the cells to reproduce the analysis and results.