SpaceX Launch Analysis and Prediction

Project Overview

This project simulates a real-world data science scenario for a rocket company aiming to compete with SpaceX. The goal is to analyze launch data, predict first-stage reuse, and estimate launch costs using data science techniques.

Files in the Repository

• Lab 0: Data Collection - Acquiring and organizing datasets.
• Lab 1: Web Scraping - Extracting launch records from Wikipedia.
• Lab 2: Data Wrangling - Cleaning and preparing raw data.
• Lab 3: EDA & Feature Engineering - Exploring patterns and engineering features for modeling.
• Lab 4: SQL Querying and Analysis - Querying and analyzing SpaceX data using SQL.
• Lab 5: Interactive Visual Analytics - Developing dashboards for data visualization.
• Lab 6: Machine Learning Prediction - Training and evaluating models for first-stage reuse prediction.

Tasks Performed

• Data Collection: Gathered data from multiple sources, ensuring high quality.
• Web Scraping: Automated extraction of structured data from web pages.
• Data Wrangling: Cleaned, normalized, and prepared data for analysis.
• EDA and Feature Engineering: Explored data, visualized insights, and created new features.
• SQL Analysis: Analyzed SpaceX data using SQL queries.
• Machine Learning: Trained and compared models to predict outcomes.

Technologies Used

• Languages: Python, SQL
• Libraries: Pandas, Matplotlib, Seaborn, Scikit-learn, BeautifulSoup
• Tools: Jupyter Notebook, Git, GitHub

How to Use This Repository

1. Clone the repository:

   git clone https://github.com/Niloufarbeikahmadie/SpaceX-Falcon-Project.git

2. Install dependencies using requirements.txt if applicable.
3. Open and run the Jupyter notebooks to explore the analysis.

Author Niloufar Beikahmadi