https://github.com/kmohamedalie/ibm-data-science-spacex-falcon9
Predicting if SpaceX Falcon 9 first stage landing will be successful using Datascience
https://github.com/kmohamedalie/ibm-data-science-spacex-falcon9
astronomy dash-plotly data-science falcon9 folium-python ibm machine-learning-algorithms pptx predictive-modeling rocket spacex spacex-launches
Last synced: 7 months ago
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Predicting if SpaceX Falcon 9 first stage landing will be successful using Datascience
- Host: GitHub
- URL: https://github.com/kmohamedalie/ibm-data-science-spacex-falcon9
- Owner: Kmohamedalie
- License: mit
- Created: 2023-08-28T01:48:06.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2023-10-19T21:09:25.000Z (about 2 years ago)
- Last Synced: 2025-04-06T06:44:37.793Z (7 months ago)
- Topics: astronomy, dash-plotly, data-science, falcon9, folium-python, ibm, machine-learning-algorithms, pptx, predictive-modeling, rocket, spacex, spacex-launches
- Language: Jupyter Notebook
- Homepage: https://github.com/Kmohamedalie/IBM-Data-Science-SpaceX-Falcon9
- Size: 3 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# IBM Applied Data Science Capstone SpaceX - Falcon 9
The capstone project is about predicting if [SpaceX's](https://www.spacex.com/) [Falcon 9](https://en.wikipedia.org/wiki/Falcon_9) rockets will land successfully in the first stage.
SpaceX advertises Falcon 9 rocket launches on its [website](https://www.spacex.com/vehicles/falcon-9/), with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because SpaceX can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against SpaceX for a rocket launch. [- IBM](https://www.ibm.com/us-en?ar=1)
**Course:** [IBM - Applied Data Science Capstone](https://www.coursera.org/learn/applied-data-science-capstone)
**Notebooks:** [Link to the notebooks](https://github.com/Kmohamedalie/IBM-Data-Science-SpaceX-Falcon9/tree/master/Notebooks)
**Machine Learning Notebook:** [SpaceX Machine Learning Prediction](https://github.com/Kmohamedalie/IBM-Data-Science-SpaceX-Falcon9/blob/master/Notebooks/7.%20SpaceX_Machine_Learning_Prediction_Part_5.jupyterlite.ipynb)
https://github.com/Kmohamedalie/IBM-Applied-Data-Science-Capstone/assets/63104472/adb201cc-e654-43a1-992e-f19975f6adb9
**Source:** [SpaceX - Falcon 9 | Overview](https://www.youtube.com/watch?v=Z4TXCZG_NEY)
### Activities:
* [Data collection](https://github.com/Kmohamedalie/IBM-Data-Science-SpaceX-Falcon9/blob/master/Notebooks/1.jupyter-labs-spacex-data-collection-api.ipynb) through an [API](https://en.wikipedia.org/wiki/API)
* Webscraping using [Beautiful Soup](https://github.com/Kmohamedalie/IBM-Data-Science-SpaceX-Falcon9/blob/master/Notebooks/2%20-jupyter-labs-webscraping.ipynb)
* [Data Wrangling]() with [Pandas](https://pandas.pydata.org/)
* [Analyzing](https://github.com/Kmohamedalie/IBM-Data-Science-SpaceX-Falcon9/blob/master/Notebooks/4.%20jupyter-labs-eda-sql-coursera_sqllite.ipynb) the data using [SQLITE](https://docs.python.org/3/library/sqlite3.html)
* [EDA](https://github.com/Kmohamedalie/IBM-Data-Science-SpaceX-Falcon9/blob/master/Notebooks/5.%20jupyter-labs-eda-dataviz.ipynb) using [matplotlib](https://matplotlib.org/) and [seaborn](https://seaborn.pydata.org/) for visualizing hidden patterns.
* [Predictive modeling](https://github.com/Kmohamedalie/IBM-Data-Science-SpaceX-Falcon9/blob/master/Notebooks/7.%20SpaceX_Machine_Learning_Prediction_Part_5.jupyterlite.ipynb) using four(4) different machine learning algorithms
- [Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)
- [Support Vector Machine](https://scikit-learn.org/stable/modules/svm.html)
- [Decision Tree Algorithm](https://scikit-learn.org/stable/modules/tree.html)
- [K Nearest Neighbour](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)
- [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)
### Results: All findings have been summarised in the [Final pdf presentation](https://github.com/Kmohamedalie/IBM-Data-Science-SpaceX-Falcon9/blob/master/IBM-DS-capstone-SpaceX-Falcon9.pdf)
Verifiable course: [Credly completion achievement](https://www.credly.com/badges/70443acf-7c9b-4aae-8b51-87bbbcb0a4f0/public_url)