Which algorithms are used in this stellar classification project?

The system implements and compares two classification algorithms: Random Forest and Logistic Regression. The Random Forest classifier achieved the best performance with 96.85% accuracy and an F1-score of 0.968.

What is the accuracy of this stellar classification model?

The Random Forest model achieves 96.85% test accuracy with a precision of 0.969, recall of 0.9685, and F1-score of 0.9684, making it highly reliable for astronomical object classification.

Who can benefit from this automated stellar classification system?

Astronomers and astrophysicists can use it for rapid analysis of survey data. Educational institutions can use it for astronomy education and research. It helps reduce manual classification effort and enables large-scale statistical studies.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Automated Stellar Classification Using Machine Learning

The Automated Stellar Classification Using Machine Learning system is a comprehensive machine learning solution that automatically classifies celestial objects from the Sloan Digital Sky Survey (SDSS) into three categories: GALAXY, QSO (Quasi-Stellar Object), and STAR. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced ensemble algorithms like Random Forest and Logistic Regression to achieve 96.85% accuracy.

The system leverages photometric and spectroscopic features from SDSS including magnitude measurements across five bands (u, g, r, i, z), redshift values, and other derived parameters. It provides interactive visualizations, feature importance analysis, model comparison, and real-time stellar classification to help astronomers process large-scale survey data efficiently.

Python 3.8+ Machine Learning Random Forest Logistic Regression Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Class Stellar Classification
Random Forest (96.85% Accuracy)
Logistic Regression (93.12% Accuracy)
SDSS Data Processing
Feature Importance Analysis
Real-time Object Classification
Model Performance Comparison
Interactive Visualizations
5-Fold Cross-Validation
Flask Web Application

Algorithms Used

🌲 Random Forest Classifier

Ensemble learning with 50 trees, handles high-dimensional photometric data effectively

🎯 Accuracy: 96.85%

📊 Logistic Regression

Linear probabilistic model with L2 regularization, provides interpretable coefficients

🎯 Accuracy: 93.12%

📊 StandardScaler

Feature scaling for magnitude measurements and redshift values

📊 Features: 17

🔍 5-Fold CV

Cross-validation for model stability and generalization

📊 RF: 96.85% ± 0.08%

Methodology & Workflow

1 Data Loading & Inspection
SDSS dataset with 10,000 records and 17 features

2 Data Preprocessing
Cleaning, label encoding, feature scaling with StandardScaler

3 Exploratory Data Analysis
Class distributions, feature correlations, magnitude analysis

4 Model Training
Random Forest & Logistic Regression with optimized parameters

5 Model Evaluation
Accuracy, F1-Score, Precision, Recall, 5-fold CV

6 Web Deployment
Flask web app for real-time stellar classification

Model Performance Comparison

Metric	Random Forest	Logistic Regression	Best
Accuracy	0.9685	0.9312	Random Forest
F1-Score	0.9684	0.9311	Random Forest
Precision	0.9690	0.9322	Random Forest
Recall	0.9685	0.9312	Random Forest
CV Mean (5-Fold)	0.9685	0.9313	Random Forest
CV Std Dev	0.0008	0.0012	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (10,000 SDSS records) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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