Applied Data Science Capstone

This repository contains a series of labs and projects for the Applied Data Science Capstone from Coursera, centered on analyzing and visualizing SpaceX Falcon 9 launch data. The project covers data collection, cleaning, exploratory analysis, visualization, and predictive modeling, providing hands-on experience with real-world data science workflows.

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Project Overview

The primary goal of this project is to analyze SpaceX Falcon 9 launch records to uncover factors influencing launch success, with a focus on launch site locations and outcomes. By leveraging data science techniques, the project aims to answer questions such as:

• What factors affect the success rate of Falcon 9 launches?
• How do launch site proximities to the equator, coastline, railways, and highways correlate with launch outcomes?
• Can we predict the success of future launches based on historical data?

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Repository Contents

• Lab1.2_WebScrapping.ipynb
  Web scraping Falcon 9 launch records from Wikipedia using BeautifulSoup and converting them into a structured DataFrame.

• Lab2_DataWrangling.ipynb
  Exploratory Data Analysis (EDA) and data wrangling to understand landing outcomes and prepare data for supervised learning.

• Lab3_SQL.ipynb
  SQL-based analysis and data exploration.

• Lab5_InteractiveVisuals.ipynb
  Interactive visualization of launch sites, success/failure outcomes, and geographic proximities using Folium.

• Lab7_Predictions.ipynb
  Data preprocessing and predictive modeling to forecast launch successes.

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Key Tasks & Objectives

• Collect and clean Falcon 9 launch data.
• Visualize launch sites and their proximity to geographic features.
• Analyze correlations between launch outcomes and site characteristics.
• Build predictive models to estimate the likelihood of launch success.

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Installation

1. Clone the repository:

   git clone https://github.com/Rhodham96/AppliedDataScienceCapstone.git
   cd AppliedDataScienceCapstone

2. Install dependencies (recommended: use a virtual environment):

   pip install -r requirements.txt

   Or, install directly in notebooks as shown:

   import piplite
   await piplite.install(['folium', 'pandas', 'scikit-learn', 'beautifulsoup4', 'requests'])

3. Open the Jupyter notebooks to explore and run the code.

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Usage

• Start with Lab1.2_WebScrapping.ipynb to collect and prepare the data.
• Proceed through each lab in order for a guided experience from data wrangling to modeling and visualization.
• Interactive maps and plots are generated using Folium and Plotly Dash.

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Data Sources

• Primary data: Wikipedia - List of Falcon 9 and Falcon Heavy launches
• Additional data as referenced within each notebook.

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Authors

• Rhodham96
  Based on IBM Data Science Professional Certificate Capstone structure.

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License

This project is for educational use. See individual notebook cells for attributions and citations.

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For questions or suggestions, please open an issue or submit a pull request.