SpaceX Falcon 9 First Stage Landing Prediction - Data Science Capstone Project

Welcome to the "SpaceX Falcon 9 First Stage Landing Prediction" repository. This project is part of a Data Science Capstone, where I assume the role of a Data Scientist working for a startup intending to compete with SpaceX. Throughout this project, I follow the Data Science methodology, which includes data collection, data wrangling, exploratory data analysis, data visualization, model development, model evaluation, and reporting results to stakeholders.

Project Overview

The main goal of this project is to predict whether the first stage of the SpaceX Falcon 9 rocket will successfully land. By leveraging Data Science skills and the models developed in this project, the startup I work for can make more informed bids against SpaceX for rocket launches. ##Repository Contents This repository contains the following files:

• requirements.txt : contains all dependencies.
• SpaceX_Machine Learning Prediction_Part_5.ipynb: Notebook containing the machine learning model development and prediction tasks.
• Spacex.csv: Dataset containing the SpaceX launch data.
• ds-capstone-presentation-optimized.pdf: Final presentation summarizing the project findings and results.
• edadataviz.ipynb: Notebook for exploratory data analysis and data visualization.
• jupyter-labs-eda-sql-coursera_sqllite.ipynb: SQL-based exploratory data analysis using SQLite.
• jupyter-labs-spacex-data-collection-api.ipynb: Notebook for data collection using SpaceX API.
• jupyter-labs-webscraping.ipynb: Web scraping notebook to gather additional data.
• lab_jupyter_launch_site_location.ipynb: Notebook analyzing launch site locations.
• labs-jupyter-spacex-Data wrangling.ipynb: Notebook for data wrangling and preprocessing.
• my_data1.db: SQLite database containing processed data.
• spacex_dash_app.py: Dash application for interactive data visualization and dashboard.
• spacex_launch_dash.csv: CSV file used for the Dash application.

Getting Started

To get started with this project, follow these steps:

1. Clone the repository:

      git clone https://github.com/yourusername/spacex-landing-prediction.git
      cd spacex-landing-prediction
   

2. Install the required dependencies:

      pip install -r requirements.txt
   

3. Open the Jupyter notebooks to explore the data and run the analyses (Shift + Enter in a cell to run it)

4. Run the Dash application for interactive visualizations:

      python spacex_dash_app.py
   

Project Workflow

The project follows a structured workflow as outlined below:

1. Data Collection: Gather data from various sources including APIs and web scraping.
2. Data Wrangling: Clean and preprocess the data to make it suitable for analysis.
3. Exploratory Data Analysis (EDA): Perform EDA to understand the data and extract insights.
4. Data Visualization: Create visualizations to represent the data and findings.
5. Model Development: Develop machine learning models to predict the landing outcome.
6. Model Evaluation: Evaluate the models to ensure they meet the performance criteria.
7. Reporting: Summarize the findings and present them to stakeholders.

Contributing

Contributions to this project are welcome. If you have any suggestions or improvements, please create a pull request or open an issue.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgements

This project is part of a Data Science Capstone course. Special thanks to the course instructors and the SpaceX API for providing the data.

Feel free to explore the notebooks and the Dash application to gain a deeper understanding of the SpaceX Falcon 9 first stage landing prediction. Happy coding! 🚀