🚀 SpaceX Falcon 9 Landing Success Prediction

IBM Applied Data Science Capstone | End-to-End Data Science & Machine Learning Project

Business Question: Can historical launch characteristics predict whether a Falcon 9 first-stage booster will land successfully—and help assess the economics of reusable rocket launches?

Built an end-to-end data science pipeline covering REST API data collection, web scraping, data wrangling, SQL & Python EDA, geospatial analytics, interactive dashboard development, and machine learning classification.

🏆 Project Result

4 machine learning models compared → GridSearchCV hyperparameter tuning → Decision Tree selected → 88.89% test accuracy

Project Area	Implementation
Business Objective	Predict Falcon 9 first-stage landing success
Data Collection	SpaceX REST API + Wikipedia Web Scraping
Data Preparation	JSON Normalization, Null Handling, Feature Engineering
Exploratory Analysis	SQL + Python
Geospatial Analytics	Folium Interactive Maps
Interactive Dashboard	Plotly Dash
Machine Learning	Logistic Regression, SVM, Decision Tree, KNN
Model Optimization	GridSearchCV
Best Model	Decision Tree — 88.89% Test Accuracy

🛠️ Core Tech Stack

Python · SQL · Pandas · NumPy · Scikit-learn · GridSearchCV · BeautifulSoup · REST API · Plotly Dash · Folium · Jupyter Notebook

Project Context: Completed as part of the IBM Applied Data Science Capstone within the IBM Data Science Professional Certificate. This is a scenario-based educational portfolio project and does not represent employment with IBM or SpaceX.

🎯 Business Problem

Commercial rocket launches are extremely expensive. In the IBM capstone scenario, SpaceX advertises Falcon 9 launches at approximately $62 million, while launches from other providers can cost upwards of $165 million.

A major contributor to SpaceX's cost advantage is its ability to recover and reuse the Falcon 9 first-stage booster.

Therefore, understanding whether the first stage is likely to land successfully can provide valuable insight into launch economics and reusable-launch operations.

Project Objective

The objective of this project is to build an end-to-end data science solution that answers:

Can we predict whether the Falcon 9 first stage will land successfully using historical launch characteristics?

To solve this problem, the project combines:

SpaceX REST API data collection
Wikipedia web scraping
Data cleaning and wrangling
SQL exploratory analysis
Python data visualization
Geospatial launch-site analysis
Interactive Plotly Dash analytics
Machine learning classification
Hyperparameter tuning and model evaluation

The final output is a predictive classification workflow capable of distinguishing between successful (1) and unsuccessful (0) first-stage landing outcomes.

🎯 Business Problem

Commercial rocket launches are extremely expensive. In the IBM capstone scenario, SpaceX advertises Falcon 9 launches at approximately $62 million, while launches from other providers can cost upwards of $165 million.

A major contributor to SpaceX's cost advantage is its ability to recover and reuse the Falcon 9 first-stage booster.

Therefore, understanding whether the first stage is likely to land successfully can provide valuable insight into launch economics and reusable-launch operations.

Project Objective

The objective of this project is to build an end-to-end data science solution that answers:

Can we predict whether the Falcon 9 first stage will land successfully using historical launch characteristics?

To solve this problem, the project combines:

SpaceX REST API data collection
Wikipedia web scraping
Data cleaning and wrangling
SQL exploratory analysis
Python data visualization
Geospatial launch-site analysis
Interactive Plotly Dash analytics
Machine learning classification
Hyperparameter tuning and model evaluation

The final output is a predictive classification workflow capable of distinguishing between successful (1) and unsuccessful (0) first-stage landing outcomes.

🔄 End-to-End Data Science Workflow

The project follows a structured data science lifecycle, progressing from raw data acquisition to predictive modeling and stakeholder-facing analytics.

1️⃣ Data Collection — SpaceX REST API

Retrieved historical SpaceX launch data using Python requests.
Worked with the SpaceX REST API /v4/launches/past endpoint.
Converted nested JSON responses into structured Pandas DataFrames using json_normalize.
Used additional API endpoints to retrieve information associated with boosters, launchpads, payloads, and cores.
Filtered the collected records to focus on Falcon 9 launches.
Replaced missing PayloadMass values using the mean while retaining meaningful LandingPad null values.

📓 01_Data-Collection-API.ipynb

2️⃣ Data Collection — Web Scraping

Collected historical launch records from a snapshot of the Wikipedia List of Falcon 9 and Falcon Heavy launches.
Used Requests and BeautifulSoup to retrieve and parse HTML.
Extracted launch-table headers and individual launch records.
Parsed fields including flight number, date, booster version, launch site, payload, payload mass, orbit, customer, launch outcome, and booster landing status.
Converted the extracted records into a Pandas DataFrame for downstream analysis.

📓 02_Webscraping.ipynb

3️⃣ Data Wrangling

Cleaned and transformed the collected launch data.
Examined launch attributes required for landing-success analysis.
Converted landing outcomes into the supervised machine-learning target:
- 1 = Successful Landing
- 0 = Unsuccessful Landing
Prepared the dataset for exploratory analysis and predictive modeling.

📓 03_Data_Wrangling.ipynb

4️⃣ Exploratory Data Analysis — SQL & Python

Used SQL to investigate launch data and operational patterns.
Used Python visualization to explore relationships between launch characteristics and landing success.
Analyzed factors such as launch site, payload mass, orbit, and flight progression.
Prepared categorical variables using One-Hot Encoding for machine-learning analysis.

📓 04-EDA-With-SQL.ipynb
📓 05_EDA_Data_Visualization.ipynb

5️⃣ Geospatial Launch-Site Analysis

Built interactive maps using Folium.
Visualized launch-site locations and landing outcomes.
Examined launch-site proximity to surrounding geographic features and infrastructure.
Used spatial analysis to support reasoning about launch-site characteristics.

📓 06_Launch_Site_Location.ipynb

6️⃣ Interactive Analytics Dashboard

Developed an interactive web application using Plotly Dash.
Added launch-site selection through a dropdown filter.
Built launch-success pie-chart analysis.
Added an interactive payload-mass range slider.
Created payload mass vs. landing-success scatter analysis.
Implemented reactive Dash callbacks to update visualizations based on user selections.

💻 07_Dashapp.py

7️⃣ Machine Learning & Predictive Analysis

Standardized model input features.
Split the dataset into training and testing sets.
Trained 4 classification algorithms:
- Logistic Regression
- Support Vector Machine (SVM)
- Decision Tree Classifier
- K-Nearest Neighbors (KNN)
Performed hyperparameter optimization using GridSearchCV.
Evaluated optimized models on the test dataset.
Used confusion-matrix analysis for classification evaluation.
Selected the strongest model based on observed test accuracy.

📓 08_Machine_Learning_Predictions.ipynb

🔍 Key Analytical Findings

The exploratory and visual analysis identified several patterns associated with Falcon 9 first-stage landing performance:

Landing success improved over time, indicating that flight progression and accumulated launch experience were associated with stronger landing outcomes in the analyzed data.
Launch sites showed different landing success rates, making launch location a relevant feature for predictive analysis.
The course analysis observed approximately 60% success at CCAFS LC-40, compared with around 77% at KSC LC-39A and VAFB SLC-4E.
Payload mass and launch site interacted with landing outcomes. For example, the analyzed course data showed CCAFS LC-40 launches above 10,000 kg with a 100% success rate in that observed subset.
Orbit type, payload characteristics, launch site, and other operational attributes provided useful information for investigating landing success.
Geospatial analysis demonstrated how launch-site locations and their proximity to surrounding infrastructure and geographic features can be explored interactively.
These findings supported the decision to use multiple launch characteristics together rather than relying on a single variable for landing prediction.

💡 Analytical Takeaway

The EDA suggested that Falcon 9 landing success is associated with a combination of operational, geographic, and mission characteristics. These relationships provided the analytical foundation for the subsequent machine-learning classification stage.

📊 Interactive Analytics Dashboard

To make the launch analysis accessible beyond static notebooks, I developed an interactive Plotly Dash application for exploring Falcon 9 launch performance.

Dashboard Capabilities

Filter results by launch site
Compare successful launches across sites
Analyze success vs. failure for individual launch sites
Filter launches by payload mass range
Explore the relationship between payload mass and landing success
Compare results across booster version categories
Dynamically update visualizations through Dash callbacks

Launch Site Success Analysis	Payload Mass vs. Landing Success

💼 Business Value

The dashboard converts analytical results into an interactive decision-support interface, allowing stakeholders to investigate launch-site performance and payload-related landing patterns without directly working with the underlying code.

💻 07_Dashapp.py

🤖 Machine Learning Model Performance

Four classification algorithms were trained and optimized to predict whether the Falcon 9 first-stage booster would land successfully.

To ensure a consistent comparison, the models were tuned using GridSearchCV and evaluated using the same train/test framework.

Classification Model	Training Accuracy	Test Accuracy
Decision Tree Classifier	87.50%	88.89%
K-Nearest Neighbors (KNN)	84.82%	83.33%
Support Vector Machine (SVM)	84.82%	83.33%
Logistic Regression	84.64%	83.33%

🏆 Best Performing Model

Decision Tree Classifier — 88.89% Test Accuracy

Best hyperparameters identified through GridSearchCV:

{
    "criterion": "gini",
    "max_depth": 8,
    "max_features": "sqrt",
    "min_samples_leaf": 1,
    "min_samples_split": 5,
    "splitter": "random"
}

Model Selection

The tuned Decision Tree Classifier achieved the highest observed test accuracy among the four evaluated models.

Logistic Regression, SVM, and KNN each achieved 83.33% test accuracy, while the Decision Tree reached 88.89% on the capstone test split.

This comparison demonstrates a complete model-selection workflow:

Preprocessing → Train/Test Split → Model Training → Hyperparameter Tuning → Model Comparison → Evaluation → Final Model Selection

Note: The 88.89% result represents performance on the test split used in this capstone experiment and should not be interpreted as production-level performance on future SpaceX launches.

📓 08_Machine_Learning_Predictions.ipynb

🛠️ Technology Stack

Area	Technologies & Methods
Programming	Python
Data Analysis	Pandas, NumPy, SQL
Data Collection	REST API, Requests, JSON, Web Scraping
Web Scraping	BeautifulSoup4, HTML Parsing
Data Preparation	Data Wrangling, Null Handling, Feature Engineering
Visualization	Matplotlib, Seaborn, Plotly
Geospatial Analytics	Folium
Interactive Analytics	Plotly Dash, Dash Callbacks
Machine Learning	Scikit-learn, Classification
Model Optimization	GridSearchCV, Hyperparameter Tuning
Model Evaluation	Train/Test Split, Accuracy, Confusion Matrix
Development Environment	Jupyter Notebook

💼 Skills Demonstrated

Data Acquisition & Engineering

REST API integration and JSON normalization
Web scraping and HTML table parsing
Data cleaning, transformation, and null-value handling
Feature preparation for machine learning

Analytics & Visualization

SQL-based exploratory data analysis
Python exploratory data analysis
Statistical and visual pattern discovery
Interactive geospatial analysis
Stakeholder-oriented dashboard development

Machine Learning

Binary classification
Feature preprocessing and standardization
One-hot encoding
Logistic Regression
Support Vector Machine
Decision Tree Classification
K-Nearest Neighbors
GridSearchCV hyperparameter optimization
Model comparison and evaluation

Business & Communication

Translating a business problem into an analytical objective
Connecting exploratory findings to predictive modeling
Building interactive decision-support visualizations
Comparing model performance using measurable results
Communicating technical findings through a final capstone presentation

📂 Repository Structure

Applied-Data-Science-Capstone/
│
├── data/
│   ├── 1_dataset_part_1.csv
│   ├── 2_spacex_web_scraped.csv
│   ├── 3_dataset_part_2.csv
│   ├── spacex_launch_dash.csv
│   ├── pie_chart.png
│   └── scatter_plot.png
│
├── 01_Data-Collection-API.ipynb
├── 02_Webscraping.ipynb
├── 03_Data_Wrangling.ipynb
├── 04-EDA-With-SQL.ipynb
├── 05_EDA_Data_Visualization.ipynb
├── 06_Launch_Site_Location.ipynb
├── 07_Dashapp.py
├── 08_Machine_Learning_Predictions.ipynb
├── 09_Capstone_Final_Presentation.pdf
├── requirements.txt
├── LICENSE
└── README.md

🔎 Quick Navigation by Skill

Want to Review	File
REST API / Data Collection	`01_Data-Collection-API.ipynb`
Web Scraping	`02_Webscraping.ipynb`
Data Wrangling	`03_Data_Wrangling.ipynb`
SQL Analysis	`04-EDA-With-SQL.ipynb`
Python EDA	`05_EDA_Data_Visualization.ipynb`
Geospatial Analytics	`06_Launch_Site_Location.ipynb`
Interactive Dashboard	`07_Dashapp.py`
Machine Learning	`08_Machine_Learning_Predictions.ipynb`
Final Presentation	`09_Capstone_Final_Presentation.pdf`

🚀 Getting Started

1. Clone the Repository

git clone https://github.com/usmanali9999/Applied-Data-Science-Capstone.git
cd Applied-Data-Science-Capstone

2. Install Dependencies

pip install -r requirements.txt

3. Launch Jupyter Notebook

jupyter notebook

Run the notebooks sequentially to follow the end-to-end workflow:

01 → API Data Collection
02 → Web Scraping
03 → Data Wrangling
04 → SQL EDA
05 → Python EDA & Visualization
06 → Geospatial Analysis
07 → Interactive Dash Application
08 → Machine Learning & Model Evaluation

4. Run the Interactive Dashboard

python 07_Dashapp.py

The Dash application is configured to run locally on port 7070.

5. Review the Final Presentation

For a concise overview of the complete project, methodology, and machine-learning results:

📄 09_Capstone_Final_Presentation.pdf

🎓 Project Context

This project was completed as part of the IBM Applied Data Science Capstone, within the IBM Data Science Professional Certificate.

The capstone provided a scenario-based business problem centered on predicting SpaceX Falcon 9 first-stage landing success. The repository demonstrates the practical application of data collection, data wrangling, SQL, exploratory analysis, interactive visualization, geospatial analytics, and machine learning within an end-to-end data science workflow.

Disclaimer: This is an educational portfolio project completed through the IBM Data Science Professional Certificate curriculum. It does not represent employment, consulting work, or an official project performed for IBM or SpaceX.

👤 Author

Muhammad Usman Ali

Data Analyst / Data Science Portfolio

GitHub: usmanali9999
LinkedIn: linkedin.com/in/usmanali9999

📄 License

This project is distributed under the MIT License. See LICENSE for details.

Name		Name	Last commit message	Last commit date
data		data
.gitignore		.gitignore
01_Data-Collection-API.ipynb		01_Data-Collection-API.ipynb
02_Webscraping.ipynb		02_Webscraping.ipynb
03_Data_Wrangling.ipynb		03_Data_Wrangling.ipynb
04-EDA-With-SQL.ipynb		04-EDA-With-SQL.ipynb
05_EDA_Data_Visualization.ipynb		05_EDA_Data_Visualization.ipynb
06_Launch_Site_Location.ipynb		06_Launch_Site_Location.ipynb
07_Dashapp.py		07_Dashapp.py
08_Machine_Learning_Predictions.ipynb		08_Machine_Learning_Predictions.ipynb
09_Capstone_Final_Presentation.pdf		09_Capstone_Final_Presentation.pdf
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt

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🚀 SpaceX Falcon 9 Landing Success Prediction

IBM Applied Data Science Capstone | End-to-End Data Science & Machine Learning Project

🏆 Project Result

🛠️ Core Tech Stack

🎯 Business Problem

Project Objective

🎯 Business Problem

Project Objective

🔄 End-to-End Data Science Workflow

1️⃣ Data Collection — SpaceX REST API

2️⃣ Data Collection — Web Scraping

3️⃣ Data Wrangling

4️⃣ Exploratory Data Analysis — SQL & Python

5️⃣ Geospatial Launch-Site Analysis

6️⃣ Interactive Analytics Dashboard

7️⃣ Machine Learning & Predictive Analysis

🔍 Key Analytical Findings

💡 Analytical Takeaway

📊 Interactive Analytics Dashboard

Dashboard Capabilities

💼 Business Value

🤖 Machine Learning Model Performance

🏆 Best Performing Model

Model Selection

🛠️ Technology Stack

💼 Skills Demonstrated

📂 Repository Structure

🔎 Quick Navigation by Skill

🚀 Getting Started

1. Clone the Repository

2. Install Dependencies

3. Launch Jupyter Notebook

4. Run the Interactive Dashboard

5. Review the Final Presentation

🎓 Project Context

👤 Author

Muhammad Usman Ali

📄 License

⭐ If you found this project useful, consider starring the repository.

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