This initiative, developed as part of IBM's Applied Data Science Capstone course on Coursera, aims to forecast the results of SpaceX Falcon 9 rocket launches. The project employs a comprehensive data science approach, encompassing several key stages:
- Gathering relevant information
- Cleaning and preparing the data
- Conducting in-depth exploratory analysis
- Creating dynamic visual representations
- Developing predictive models
By leveraging these techniques, the project seeks to gain insights into the factors influencing launch outcomes and to build accurate prediction models for future SpaceX missions.
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.