SpaceX Launch Success Prediction

A comprehensive data science project analyzing SpaceX Falcon 9 rocket launches to predict landing outcomes and identify key factors influencing mission success. This project demonstrates the complete data science pipeline from data collection to machine learning model deployment.

📋 Table of Contents

• Overview
• Business Problem
• Dataset
• Methodology
• Key Findings
• Technologies Used
• Project Structure
• Installation & Setup
• Usage
• Results
• Future Improvements
• Contributing

🎯 Overview

SpaceX revolutionized the space industry by offering Falcon 9 rocket launches at $62 million compared to competitors' $165 million+. The key to cost reduction is the reusability of the first stage boosters. This project builds a machine learning pipeline to predict the probability of successful first stage landings, enabling better pricing strategies for competing against SpaceX.

💼 Business Problem

Objective: Create a machine learning model to predict the landing outcome of SpaceX Falcon 9 first stage boosters.

Key Questions:

• What factors influence successful rocket landings?
• How do variables like payload mass, orbit type, and launch site affect outcomes?
• What are the optimal conditions for successful landings?
• How has SpaceX's success rate evolved over time?

📊 Dataset

Data Sources:

• SpaceX REST API: Historical launch data
• Wikipedia Web Scraping: Additional Falcon 9 launch records
• Launch Sites: Geographic and proximity analysis

Key Features:

• Flight Number, Launch Site, Payload Mass
• Orbit Type, Mission Outcome, Booster Version
• Landing Outcome, Launch Date
• Geographic coordinates and site proximities

Dataset Size: 101 launches (2010-2020) Target Variable: Landing Success (Binary: 0=Failure, 1=Success)

🔬 Methodology

1. Data Collection

• REST API Integration: Automated data retrieval from SpaceX API
• Web Scraping: BeautifulSoup for Wikipedia data extraction
• Data Validation: Comprehensive cleaning and preprocessing

2. Data Wrangling & EDA

• Missing value imputation and data type conversions
• Feature engineering for categorical variables
• Statistical analysis and correlation studies
• SQL Analysis: Complex queries for data insights

3. Data Visualization

• Matplotlib/Seaborn: Statistical plots and trend analysis
• Folium Maps: Interactive geospatial analysis of launch sites
• Plotly Dash: Interactive dashboards for data exploration

4. Machine Learning Pipeline

• Algorithms Tested:
  • K-Nearest Neighbors (KNN)
  • Decision Tree Classifier
  • Logistic Regression
• Model Selection: GridSearchCV for hyperparameter optimization
• Evaluation Metrics: Accuracy, Precision, Recall, F1-Score

🏆 Key Findings

Launch Success Insights

• Overall Success Rate: Improved from 33% (2013) to 83% (2020)
• Best Performing Site: KSC LC-39A (76.9% success rate)
• Optimal Orbit Types: ES-L1, GEO, HEO, SSO (100% success rate)
• Payload Impact: Lighter payloads (<4000kg) show higher success rates

Site Analysis

• Geographic Distribution: All launch sites located within the United States
• Proximity Factors:
  • Launch sites maintain distance from cities for safety
  • Close proximity to coastlines for trajectory optimization
  • No direct railway access to launch sites

Temporal Trends

• Learning Curve: Clear improvement in success rates over time
• First Successful Ground Landing: December 22, 2015
• Technology Evolution: Consistent advancement in landing capabilities

🛠 Technologies Used

Programming Languages:

• Python 3.8+

Data Science Libraries:

• Data Manipulation: Pandas, NumPy
• Visualization: Matplotlib, Seaborn, Plotly
• Machine Learning: Scikit-learn
• Web Scraping: BeautifulSoup, Requests
• Geospatial Analysis: Folium
• Database: SQLite

Development Environment:

• Jupyter Notebooks
• IBM Watson Studio (Development Platform)

📁 Project Structure

spacex-launch-prediction/
│
├── data/
│   ├── raw/                    # Raw data files
│   ├── processed/              # Cleaned datasets
│   └── external/               # External data sources
│
├── notebooks/
│   ├── 01-data-collection-api.ipynb
│   ├── 02-data-collection-webscraping.ipynb
│   ├── 03-data-wrangling.ipynb
│   ├── 04-eda-dataviz.ipynb
│   ├── 05-eda-sql.ipynb
│   ├── 06-interactive-visual-analytics-folium.ipynb
│   ├── 07-plotly-dash-dashboard.ipynb
│   └── 08-machine-learning-prediction.ipynb
│
├── src/
│   ├── data/                   # Data processing modules
│   ├── features/               # Feature engineering
│   ├── models/                 # ML model implementations
│   └── visualization/          # Plotting utilities
│
├── reports/
│   ├── figures/                # Generated plots and charts
│   └── presentation.pdf        # Final presentation
│
├── requirements.txt
├── README.md
└── LICENSE

📈 Results

Model Performance

• Best Algorithm: Decision Tree Classifier
• Accuracy: 86.25%
• Key Features: Payload mass, orbit type, launch site, booster version

Business Impact

• Cost Optimization: 62% cost reduction compared to traditional launches
• Risk Assessment: Improved bidding strategies against SpaceX
• Success Prediction: 86% accuracy in landing outcome prediction

Visualization Highlights

• Interactive launch site maps with success/failure indicators
• Temporal trend analysis showing improvement over time
• Payload vs. success rate correlation analysis
• Comprehensive dashboard for stakeholder presentations

🙏 Acknowledgments

• IBM Developer Skills Network for the project framework