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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

🏆 Certificates

To verify the certificates, click the images to follow the links.