Project Overview
Comprehensive analysis of 5,000+ confirmed exoplanets (38,000+ entries) from NASA Exoplanet Archive to identify classification patterns, validate physical relationships, and discover potentially habitable candidates using advanced statistical analysis and machine learning techniques. This project demonstrates large-scale data processing capabilities and innovative ML approaches applied to real-world scientific data.
Problem Statement & Business Impact
In the era of big data astronomy where thousands of exoplanets are discovered annually, this project addresses critical challenges:
✦ How to efficiently classify and analyze massive astronomical datasets?
✦ What are the limitations and biases in current detection methods?
✦ Can we automate the identification of potentially habitable worlds?
✦ How do we validate universal physical laws across diverse planetary systems?
Technical Stack & Architecture
1. Large-Scale Data Processing
✦ Dataset: 5,903 confirmed exoplanets after comprehensive cleaning
✦ Dimensionality: Multi-parameter analysis (mass, radius, temperature, orbital characteristics)
✦ Missing data strategy: Preserving maximum information while ensuring analysis integrity
2. Multi-Algorithm Classification Pipeline
✦ Size classification: 8-category system (Mars-sized to Super-Jupiter-sized)
✦ Binary composition: Rocky/gaseous using 3.0 g/cm³ density threshold
✦ Method comparison: Threshold vs Logistic Regression vs Advanced ML
3. Predictive Modeling for Habitability
✦ Criteria definition: Temperature (200-350K), radius (0.5-2 R_Earth), energy flux (0.8-1.2x Earth)
✦ Model validation: Train/test split (70/30) with proper preprocessing
✦ Independent testing: Post-training validation on separately created solar system dataset
Advanced Analytics Implementation
✦ Environment: Python, Jupyter Notebook
✦ Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn
✦ Approaches: Statistical analysis, supervised/unsupervised ML, physics validation
Robust Data Pipeline
✦ Strategic data cleaning: Intelligent handling of 60-80% missing values
✦ Scale normalization: Log-scale transformations for multi-order magnitude data
✦ Feature engineering: StandardScaler and optimized variable selection
✦ Quality assurance: 95th percentile outlier filtering with scientific validation
Key Results & Performance Metrics
Model Performance
✦ 96.3% accuracy in predicting detection methods (KNN & SVM)
✦ 86.1% precision for composition classification vs 83.7% traditional threshold approach
✦ 100% accuracy on habitability prediction validated independently on solar system data
✦ 4 habitable candidates identified from 1,639 analyzed (0.2% success rate)
Scientific Validation
✦ 99.9% conformity with Kepler's third law confirming universal physics
✦ 4 distinct planetary groups discovered through unsupervised clustering
✦ 391 gaseous vs 209 rocky planets identified using ML-enhanced classification
Key Insights & Innovation
Observational Bias Detection
✦ Hot Jupiter overrepresentation: Massive, close-in planets easier to detect via radial velocity
✦ Earth-like underrepresentation: Current technological limitations for small, distant planets
✦ Transit method dominance: 96.3% prediction accuracy confirmed
Physical Relationship Validation
✦ Stellar-planetary energy transfer: Stellar temperature correlation with planetary equilibrium (r=0.42)
✦ Universal gravitational physics: Kepler's law validation across 5,000+ systems
✦ Composition patterns: Clear density-based separation between rocky/gaseous populations
Methodological Innovation
✦ ML-enhanced classification: 30 additional rocky planets identified vs simple threshold
✦ Clustering optimization: Elbow method determining k=4 optimal clusters
✦ Cross-validation approach: Independent solar system testing ensuring model robustness
Technical Skills Demonstrated
Data Engineering
✦ Large-scale processing: 5,000+ observations with complex missing data patterns
✦ ETL pipeline: End-to-end data cleaning to model deployment
✦ Performance optimization: Efficient handling of astronomical data scales
Machine Learning
✦ Multi-algorithm comparison: KNN, SVM, Logistic Regression implementation
✦ Unsupervised learning: K-Means clustering with optimal parameter selection
✦ Model validation: Proper train/test methodology with independent validation
✦ Feature engineering: Handling diverse data types and scales
Statistical Analysis
✦ Hypothesis testing: Quantitative validation with uncertainty assessment
✦ Correlation analysis: Multi-parameter relationship identification
✦ Anomaly detection: Outlier identification and scientific interpretation
Real-World Applications
This project demonstrates capabilities directly applicable across industries.
Core Transferable Skills:
✦ Classification Systems: Multi-algorithm approach for any categorical prediction tasks
✦ Large-Scale Data Processing: Techniques for handling massive datasets with missing values
✦ Clustering & Pattern Discovery: Unsupervised learning for segmentation and grouping
✦ Model Validation: Robust testing methodologies for production environments
✦ Anomaly Detection: Outlier identification applicable to quality control and fraud detection
✦ Performance Optimization: Efficient algorithms for processing 5K+ data points
Industry Applications:
✦ Finance: Risk assessment, fraud detection, customer segmentation
✦ Healthcare: Patient classification, treatment optimization
✦ E-commerce: Product categorization, recommendation systems
✦ Manufacturing: Quality control, predictive maintenance
✦ Technology: Content moderation, user behavior analysis
Performance Optimization
✦ Computational efficiency: Optimized algorithms for 5K+ data points
✦ Memory management: Strategic data loading and processing
✦ Scalable architecture: Pipeline designed for larger datasets
Identified Habitable Candidates
Discovered through ML analysis:
✦ Kepler-1544 b: Optimal temperature and size parameters
✦ Kepler-155 c: Ideal energy flux and radius conditions
✦ Kepler-296 e: Earth-like temperature and size characteristics
✦ LP 890-9 c: Recently discovered habitable zone candidate
Large-scale data processing • Multi-algorithm optimization • Scalable ML pipeline