Applied Data Science Capstone

The Applied Data Science Capstone is the culminating course in the IBM Data Science Professional Certificate specialization. This final project serves as a comprehensive summary, applying all the knowledge acquired throughout the specialization.

Project Background

SpaceX, a pioneering force in the commercial space age, has revolutionized space travel by making it more affordable. The Falcon 9 rocket, advertised on SpaceX's website at a cost of $62 million, significantly undercuts competitors whose launches can cost upwards of $165 million. A key factor in these savings lies in SpaceX's ability to reuse the first stage of the rocket. The project aims to predict whether the first stage will successfully land, thereby determining the overall cost of a launch. Utilizing public information and machine learning models, the project seeks to contribute valuable insights to this domain.

Questions to be Answered

1. How do variables such as payload mass, launch site, number of flights, and orbits influence the success of the first stage landing?
2. Does the rate of successful landings increase over the years?
3. What is the best algorithm for binary classification in this case?

Methodology

1. Data Collection Methodology

   • Utilizing SpaceX Rest API.
   • Web scraping data from Wikipedia.

2. Data Wrangling

   • Filtering the data for relevance.
   • Addressing missing values.
   • Employing One Hot Encoding to prepare the data for binary classification.

3. Exploratory Data Analysis (EDA)

   • Using visualization techniques and SQL for in-depth exploration.

4. Interactive Visual Analytics

   • Leveraging Folium and Plotly Dash for an engaging visual representation of the data.

5. Predictive Analysis

   • Implementing classification models.
   • Building, tuning, and evaluating these models to ensure optimal results.

Importance and Application

This project isn't just an academic exercise but holds practical significance:

• Space Exploration Economics:

  • Understanding the factors influencing the success of first-stage landings can contribute to cost-efficient space exploration.

• Decision Support for Stakeholders:

  • Insights derived from the project can aid SpaceX and other stakeholders in making informed decisions about launch costs.

• Advancements in Machine Learning:

  • Application of various classification algorithms and model evaluation contributes to the broader field of machine learning.