Quasar Classification Project

Introduction

This repository contains the code and documentation for a machine learning project aimed at classifying astronomical objects into galaxies, quasars, and stars using photometric data from the Sloan Digital Sky Survey (SDSS).

Project Overview

The project uses two distinct machine learning approaches:

1. Naive Bayes Classifier: A traditional machine learning approach using a Naive Bayes classifier to predict the class of astronomical objects.
2. Deep Neural Network: A deep learning approach using a neural network to predict the class of astronomical objects.

Code Structure

The code is organized into the following sections:

• Data Loading: Loads the SDSS dataset and preprocesses the data for training and testing.
• Naive Bayes Classifier: Implements a Naive Bayes classifier using scikit-learn.
• Deep Neural Network: Implements a deep neural network using TensorFlow and Keras.
• Training and Evaluation: Trains and evaluates both models using the preprocessed data.

Requirements

• Python 3.8+
• scikit-learn
• TensorFlow
• Keras
• NumPy
• Pandas
• Matplotlib
• Seaborn

Usage

1. Clone the repository using git clone https://github.com/your-username/Quasar-Classification-Project.git
2. Install the required packages using pip install -r requirements.txt
3. Run the code using python main.py

Results

The project achieves an accuracy of 92.16% using the Naive Bayes classifier and 93% using the deep neural network. The results are visualized using confusion matrices, ROC curves, and precision-recall curves.

Contributing

Contributions are welcome! Please submit a pull request with your changes and a brief description of your contribution.