Applied Data Science Capstone Project

In this capstone project, we will predict whether the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website at a cost of 62 million dollars per launch, while other providers charge upwards of 165 million dollars each. Much of SpaceX's savings are due to their ability to reuse the first stage of the rocket. Therefore, determining if the first stage will land successfully can help predict the cost of a launch. This information can be valuable for an alternate company that wants to bid against SpaceX for a rocket launch.

Learning Objectives

• Develop Python code to manipulate data in a Pandas DataFrame.
• Convert a JSON file into a Pandas DataFrame.
• Create a Jupyter notebook and make it shareable using GitHub.
• Utilize data science methodologies to define and formulate a real-world business problem.
• Use data analysis tools to load a dataset, clean it, and extract interesting insights.

Project Overview

This project is divided into several modules, each focusing on different aspects of data science and machine learning. You will be provided with an overview of the problem and the tools you need to complete the course.

Modules

Module 1: Data Collection and Preprocessing

• Learn how to collect and preprocess data.
• Convert a JSON file into a Pandas DataFrame.
• Clean the data and handle missing values.

Module 2: Exploratory Data Analysis (EDA)

• Load the dataset into a Jupyter notebook.
• Perform exploratory data analysis to uncover insights and patterns.
• Visualize the data using various plotting libraries.

Module 3: Feature Engineering

• Identify and create new features that can improve the prediction model.
• Encode categorical variables using techniques like one-hot encoding.

Module 4: Model Development

• Develop machine learning models to predict the landing outcome of the Falcon 9 first stage.
• Evaluate the performance of different models using metrics like accuracy and confusion matrix.

Module 5: Model Evaluation and Deployment

• Fine-tune the model for better performance.
• Deploy the model and make it accessible for real-time predictions.

Usage

1. Launch Jupyter Notebook:

   jupyter notebook

2. Open the notebook:
   • Navigate to the directory where you cloned the repository.
   • Open the Jupyter notebook file (.ipynb) and follow the instructions to complete each module.

Contributing

If you wish to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add some feature').
5. Push to the branch (git push origin feature-branch).
6. Open a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• IBM Data Science Professional Certificate
• SpaceX for providing the data