Falcon 9 - Predicting First Launch

Project Overview

Welcome to the "Falcon 9 - Predicting First Launch" repository! This is the central hub for our Data Science Capstone project, which focuses on predicting the successful launch of SpaceX's Falcon 9 rockets.

Project Objective: The primary goal of this project is to create a predictive model that can anticipate the success or failure of Falcon 9 rocket launches based on historical data and various features.

Table of Contents

1. Introduction
2. Data
3. Methodology
4. Code Structure
5. Setup and Installation
6. Usage
7. Results
8. Contributing
9. License
10. Contact

Introduction

SpaceX's Falcon 9 rocket has become a revolutionary force in the space industry, with a track record of numerous successful launches. However, predicting the outcome of each launch can be challenging due to the complex interplay of various factors. This project employs Data Science techniques and Machine Learning models to create a predictive tool for launch outcomes. By doing so, we aim to improve the understanding of the factors that contribute to successful launches and reduce the risks associated with space missions.

Data

The success of this project relies on quality data. We use historical data from SpaceX's Falcon 9 rocket launches, which include information about launch dates, mission names, payloads, launch outcomes, and various other technical parameters. The dataset is available in the data directory.

Methodology

Our predictive model employs a combination of Data Preprocessing, Feature Engineering, and Machine Learning techniques. The following libraries are central to our project:

• Python
• Pandas
• NumPy
• Scikit-Learn
• Matplotlib
• Seaborn

The detailed methodology and data processing steps can be found in the Jupyter Notebook located in the notebooks directory.

Code Structure

The project repository has the following structure:

- data/              # Contains the dataset
- notebooks/         # Jupyter notebooks for data analysis and modeling
- README.md          # This documentation

Setup and Installation

To run this project, you will need to have Python installed along with the required dependencies listed in the requirements.txt file. You can install these dependencies using pip:

pip install -r requirements.txt

Usage

To get started with the project, follow these steps:

1. Clone this repository:

   git clone https://github.com/jgfurlan/Capstone.git

2. Install the dependencies (as mentioned in the "Setup and Installation" section).

3. Open the Jupyter notebooks in the notebooks directory to explore the data analysis and modeling process.

4. Run the Python code in the src directory to train and evaluate the predictive model.

Results

Our project's final report, detailing the model's performance and insights gained from the analysis, can be found in the notebooks directory. The results and findings are summarized in the report.

Contributing

We welcome contributions and suggestions to improve this project. If you'd like to contribute, please follow these guidelines:

1. Fork the repository.

2. Create a new branch for your feature or bug fix.

3. Make your changes and commit them with descriptive messages.

4. Push your changes to your fork and submit a pull request.

We appreciate your valuable contributions!

License

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

Contact

If you have any questions, suggestions, or feedback, please feel free to reach out to the project maintainers:

• João Gabriel Furlan: [joaogfurlan@hotmail.co.uk]

Thank you for your interest in the "Falcon 9 - Predicting First Launch" project! We hope our work can contribute to a better understanding of rocket launch success and support advancements in space exploration.