🚀 Rocket Landing: SpaceX Data Analysis & Visualization

rocketlanding is a data science project focused on collecting, analyzing, and visualizing data related to SpaceX rocket landings. The repository showcases the entire workflow: from web scraping and data wrangling, to exploratory data analysis (EDA), machine learning, and building interactive dashboards.

---

📋 Table of Contents

• Project Overview
• Features
• Installation
• Usage
• Repository Structure
• Tech Stack
• Contributors
• License

---

🌟 Project Overview

This project demonstrates the end-to-end pipeline for analyzing rocket landing data:

• Collecting raw SpaceX launch data via web scraping
• Cleaning and wrangling data
• Running exploratory data analysis (EDA) using SQL and visualization tools
• Applying machine learning models to predict rocket landing outcomes
• Building interactive dashboards (with Dash and Folium) for visual representation

---

✨ Features

• Automated Web Scraping: Fetches latest rocket launch data from the web
• Comprehensive Data Wrangling: Cleans and formats multiple CSV datasets
• EDA: Explore trends with SQL queries and visualizations
• Machine Learning: Train models to predict successful landings
• Dashboards: Interactive visualization using Dash and Folium
• Reproducible Notebooks: All steps included as Jupyter notebooks

---

⚙️ Installation

1. Clone the repository:

   git clone https://github.com/jdhruv555/rocketlanding.git
   cd rocketlanding

2. Set up a Python virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

---

🚀 Usage

1. Data Collection & Wrangling:

   • Run Web_Scrapping.ipynb and Data_Collection.ipynb to scrape and organize the data.
   • Use Data_Wrangling.ipynb to clean and prepare datasets for analysis.

2. Exploratory Data Analysis:

   • Explore EDA_SQL.ipynb for SQL-based analysis.
   • Visualize data with EDA_Visualization.ipynb.

3. Machine Learning:

   • Open and run Machine_Learning.ipynb to train models and make predictions.

4. Dashboard:

   • Serve the interactive dashboard via Dashboard_dash.ipynb or use server.py to deploy locally.
   • For geographic visualizations, check Folium_Dashboard.ipynb.

5. Web App:

   • Use index.html, script.js, and styles.css for the static site (if any).

---

🗂 Repository Structure

• Web_Scrapping.ipynb —— Scrapes SpaceX data from the web
• Data_Collection.ipynb —— Data assembly and merging
• Data_Wrangling.ipynb —— Data cleaning and feature engineering
• EDA_SQL.ipynb —— EDA using SQL queries
• EDA_Visualization.ipynb —— Data visualizations (plots, charts)
• Folium_Dashboard.ipynb —— Map-based dashboard with Folium
• Dashboard_dash.ipynb —— Interactive dashboard with Dash
• Machine_Learning.ipynb —— Predictive modeling and evaluation
• requirements.txt —— Required dependencies
• index.html, script.js, styles.css —— Frontend/static files
• spacex_web_scrapped.csv, dataset_part_*.csv —— Processed datasets
• my_data1.db —— SQL database (if used)
• server.py —— Backend server for dashboards

---

🛠 Tech Stack

• Languages: Python, SQL, JavaScript, HTML, CSS
• Libraries:
  • Data: pandas, numpy, sqlite3
  • Visualization: matplotlib, seaborn, folium, plotly, dash
  • Machine Learning: scikit-learn
  • Web: Flask (optional), Dash
  • Jupyter Notebook

---

👥 Contributors

• jdhruv555 (Owner & Maintainer)

---

If you find this project useful, please ⭐ star and fork the repository! Happy Coding!