📊 Applied Data Science Capstone

The Capstone is the final course in the IBM Data Science Professional Certificate program. It's a project that combines all the skills and knowledge you've gained throughout the specialization.

📖 Project Background

SpaceX is leading the way in the commercial space industry by making space travel cheaper. They advertise Falcon 9 rocket launches on their website for $62 million, while others charge over $165 million. SpaceX saves money by reusing the first stage of their rockets. So, if we can predict whether they'll reuse it, we can estimate launch costs. Using public information and machine learning, we'll try to forecast if SpaceX will reuse the first stage.

🔍 Questions to be answered

• How do factors like payload mass, launch site, number of flights, and orbits influence the likelihood of a successful first stage landing?

• Does the rate of successful landings increase over the years?

• What is the best algorithm that can be used for binary classification in this case?

📈 Methodology

1. Data collection methodology

• Using SpaceX Rest API.
• Using Web Scrapping from Wikipedia.

2. Data Wrangling

• Filtering the data.
• Dealing with missing values.
• Using One Hot Encoding to prepare the data to a binary classification.

3. Exploratory data analysis (EDA) using visualization and SQL

• Visualized launch records for each site.
• Leveraged SQL queries to extract and aggregate relevant data for deeper analysis.

4. Interactive Visual Analytics Using Folium and Plotly Dash

• Created interactive maps to visualize launch sites and landing locations.
• Developed interactive dashboards to explore various factors influencing first stage landing success.

5. Predictive analysis using classification models.

• Constructing, fine-tuning, and assessing classification models to achieve optimal performance.