Welcome to Falcon 9 Analytics, a comprehensive data science project designed to delve into the robust history and technological advances of SpaceX's Falcon 9 rocket launches. Utilizing a mix of modern data collection techniques and cutting-edge analytical tools, this project offers a detailed exploration of SpaceX's operational successes and areas of potential enhancement.
In this project, we:
- Collect Data: Harness REST APIs and implement web scraping on Wikipedia to gather detailed information about Falcon 9 launches.
- Perform Exploratory Data Analysis (EDA): Use core Python libraries to explore, manipulate, clean, and visualize the data, gaining preliminary insights and preparing the dataset for deeper analysis.
- Map Visualization: Employ Folium to create illustrative maps that provide geographical insights into launch sites and trajectories.
- Interactive Dashboard: Develop an interactive dashboard using Plotly Dash, enabling users to visually interact with the data and uncover hidden patterns.
- Predictive Modeling: Construct predictive machine learning models to forecast landing outcomes. This involves rigorous feature engineering, hyperparameter tuning, and cross-validation to ensure model accuracy and reliability.
This project offers a holistic analysis of SpaceX's Falcon 9 rocket launches, covering data collection, exploratory analysis, map visualization, an interactive dashboard, and predictive modeling.
- SpaceX REST API: We utilize the official SpaceX API to fetch real-time data about rocket launches.
- Wikipedia: Data concerning historical launches is scraped from the List of Falcon 9 and Falcon Heavy launches using BeautifulSoup4.
- Python: The core programming language used for data collection, manipulation, and analysis.
- Pandas & NumPy: For data manipulation and numerical operations.
- Matplotlib & Seaborn: Used for creating static plots and visualizations.
- Folium: For generating interactive maps to visualize geographical data.
- Plotly Dash: For building the interactive web-based dashboard that displays analysis results.
- Requests: Employed to fetch web pages from the internet.
- BeautifulSoup: Utilized for web scraping data from Wikipedia.
- Scikit-Learn: For building and tuning machine learning models.
Clone the repository and navigate to the project directory:
git clone https://github.com/Hrishikesh-Papasani/SpaceX-Falcon9-Analysis-ML-Prediction.git
cd SpaceX-Falcon9-Analysis-ML-PredictionYou Run the dashboard with:
python app.pyDistributed under the MIT License. See LICENSE for more information.
Hrishikesh Reddy Papasani
Connect on LinkedIn: LinkedIn Profile
Contact: [email protected]