Objective: This Project aims to forecast the likelihood of succesfull landings of SpaceX's Falcon-9 first stage.

Key Achievements:

Achieved 76.9% success rate at the KSC LC-39A launch site, making it the most reliable site.
Implemented a Decision Tree model, which proved to be the best-performing classification algorithm, demonstrating the highest accuracy in predicting landing outcomes.
Conducted comprehensive Exploratory Data Analysis (EDA) and built interactive visualizations, improving insights into launch performance and payload relationships.
Automated data collection via SpaceX REST API and Wikipedia web scraping, covering over 150 Falcon-9 launches, reducing manual data gathering efforts by over 80%.

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
Data Collection API.ipynb		Data Collection API.ipynb
Data Collection with Web Scraping.ipynb		Data Collection with Web Scraping.ipynb
Data Wrangling.ipynb		Data Wrangling.ipynb
EDA with Data Visualization.ipynb		EDA with Data Visualization.ipynb
EDA with SQL.ipynb		EDA with SQL.ipynb
Interactive Visual Analytics with Folium.ipynb		Interactive Visual Analytics with Folium.ipynb
Machine Learning Prediction.ipynb		Machine Learning Prediction.ipynb
README.md		README.md
ds-capstone-presentation.pdf		ds-capstone-presentation.pdf
spacex_dash_app.py		spacex_dash_app.py