This project is part of the Applied Data Science Capstone by IBM on Coursera. It focuses on using data science techniques to predict the outcomes of SpaceX Falcon 9 rocket launches through data collection, data wrangling, exploratory data analysis (EDA), interactive visual analytics, and predictive analysis.

Experience the interactive SpaceX Launch Prediction dashboard hosted live at PythonAnywhere. Explore the data-driven insights and visualizations in real-time.

• data/: Contains datasets used in the project.
• Data Collection and Data Wrangling/: Notebooks for data collection, web scraping, and data preparation.
• Exploratory Data Analysis/: Notebooks for performing EDA using Pandas, visualization libraries, and SQL.
• Interactive Visual Analytics and Dashboard/: Scripts and notebooks for creating interactive visualizations and dashboards with Plotly, Dash, and Folium.
• Predictive Analysis/: Notebook for developing and evaluating machine learning models to predict launch success.

• spacex_launch_dash.csv: Launch data for dashboard visualizations.
• spacex_launch_geo.csv: Geographical data for mapping launch sites.

1. Clone this repository and navigate to the project's root directory:

   git clone https://github.com/TimKong21/SpaceX-Launch-Prediction.git
   cd SpaceX-Launch-Prediction

2. Set Up a Virtual Environment in the project directory:

   # For Windows
   python -m venv venv
   .\venv\Scripts\activate
   
   # For MacOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install Jupyter Notebook or JupyterLab to run .ipynb notebooks. If you don't have them installed, you can install JupyterLab with:

   pip install jupyterlab

4. Install required Python packages by running:

   pip install -r requirements.txt

5. Open the notebook(s) of interest with Jupyter by running:

   jupyter lab

6. To run the dashboard, navigate to the dashboard directory and then start the Dash app:

   cd ".\Interactive Visual Analytics and Dashboard\"
   python .\launch_record_analysis_dash_app.py