Applied Data Science Capstone

📄 Summary

This capstone project will ultimately predict if the Space X Falcon 9 first stage will land successfully.

The full report can be found here.

Context and Business Understanding

• SpaceX launches Falcon 9 rockets at a cost of around $62m. This is considerably cheaper than other providers (which usually cost upwards of $165m), and much of the savings are because SpaceX can land, and then re-use the first stage of the rocket.

• If we can make predictions on whether the first stage will land, we can determine the cost of a launch, and use this information to assess whether or not an alternate company should bid against SpaceX for a rocket launch.

📑 Main Topics

This project follows these steps:

1. Data Collection

   • Making GET requests to the SpaceX REST API
   • Web Scraping

2. Data Wrangling

   • Using the .fillna() method to remove NaN values
   • Using the .value_counts() method to determine the following:
     • Number of launches on each site
     • Number and occurrence of each orbit
     • Number and occurrence of mission outcome per orbit type
   • Creating a landing outcome label that shows the following:
     • 0 when the booster did not land successfully
     • 1 when the booster did land successfully

3. Exploratory Data Analysis

   • Using SQL queries to manipulate and evaluate the SpaceX dataset
   • Using Pandas and Matplotlib to visualize relationships between variables, and determine patterns

4. Interactive Visual Analytics

   • Geospatial analytics using Folium
   • Creating an interactive dashboard using Plotly Dash

5. Predictive Analysis (Classification)

   • Using Scikit-Learn to:
     • Pre-process (standardize) the data
     • Split the data into training and testing data using train_test_split
     • Train different classification models
     • Find hyperparameters using GridSearchCV
   • Plotting confusion matrices for each classification model
   • Assessing the accuracy of each classification model

🔑 Key Skills Learned/Used

• Using data science methodologies to define and formulate a real-world business problem
• Using data analysis and data visualisation to load a dataset, clean it, and find out interesting insights from it
• Interactive dashboard development with Plotly Dash
• Interactive map development using Folium
• Using machine learning to build a predictive model to help a business function more efficiently
• Structuring and building a data-findings report