https://github.com/felixcharotte/ibm_datascience_capstone

In this project, we predicted if the SpaceX Falcon 9 first stage will land successfully by following the data science methodology. We also summarized the results for the business stakeholders.
https://github.com/felixcharotte/ibm_datascience_capstone

analysis data-analysis data-science data-visualization databases folium jupyter-notebook machine-learning machine-learning-alrgorithms matplotlib pandas plotly plotly-dash python scikit-learn scipy seaborn sql

Last synced: 2 months ago
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In this project, we predicted if the SpaceX Falcon 9 first stage will land successfully by following the data science methodology. We also summarized the results for the business stakeholders.

• Host: GitHub
• URL: https://github.com/felixcharotte/ibm_datascience_capstone
• Owner: FelixCharotte
• Created: 2025-03-02T18:37:13.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-03-12T21:25:07.000Z (7 months ago)
• Last Synced: 2025-06-04T21:24:29.619Z (4 months ago)
• Topics: analysis, data-analysis, data-science, data-visualization, databases, folium, jupyter-notebook, machine-learning, machine-learning-alrgorithms, matplotlib, pandas, plotly, plotly-dash, python, scikit-learn, scipy, seaborn, sql
• Language: Jupyter Notebook
• Homepage:
• Size: 20.5 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# IBM 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](https://github.com/FelixCharotte/IBM_DataScience_Capstone/blob/b7c9bf0e404447cb190498c6ebe3083a6ddd2eee/IBM%20Data%20Science%20Capstone%20Project%202025.pdf).

### 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](https://github.com/FelixCharotte/IBM_DataScience_Capstone/tree/b7c9bf0e404447cb190498c6ebe3083a6ddd2eee/01.%20Data%20Collection)

    - Making GET requests to the SpaceX REST API

    - Web Scraping

2. [Data Wrangling ](https://github.com/FelixCharotte/IBM_DataScience_Capstone/tree/b7c9bf0e404447cb190498c6ebe3083a6ddd2eee/02.%20Data%20Wrangling)

    - 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](https://github.com/FelixCharotte/IBM_DataScience_Capstone/tree/b7c9bf0e404447cb190498c6ebe3083a6ddd2eee/03.%20Exploratory%20Data%20Analysis)

    - 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](https://github.com/FelixCharotte/IBM_DataScience_Capstone/tree/b7c9bf0e404447cb190498c6ebe3083a6ddd2eee/04.%20Interactive%20Visual%20Analytics)

    - Geospatial analytics using Folium

    - Creating an interactive dashboard using Plotly Dash

5. [Predictive Analysis (Classification)](https://github.com/FelixCharotte/IBM_DataScience_Capstone/tree/b7c9bf0e404447cb190498c6ebe3083a6ddd2eee/05.%20Predicitve%20Analysis%20(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.



  

  



Aknowledgement to DanielBarnes18