SpaceX Falcon 9 Landing Prediction

Project Overview

A machine learning project focused on predicting the successful landing of SpaceX Falcon 9 first stages. The project applies data science techniques to analyze launch data and build predictive models that can determine landing outcomes with high accuracy, providing valuable insights for future space missions.

Key Features

• Data Collection: Developed a web scraping system using Beautiful Soup to extract SpaceX Falcon 9 launch data, followed by comprehensive data preprocessing and exploratory analysis.
• Predictive Modeling: Implemented Random Forest and Logistic Regression algorithms using scikit-learn to predict landing outcomes based on multiple flight parameters.
• Accuracy Achievement: Achieved 83.33% accuracy in predicting first-stage landing success, demonstrating the efficacy of the models and analysis approach.

Technologies Used

Implementation Details

The project workflow included:

1. Web scraping of SpaceX launch data from multiple sources
2. Data cleaning and preprocessing to handle missing values and outliers
3. Exploratory data analysis to identify patterns and key features
4. Feature engineering to extract meaningful predictors
5. Model selection, training, and hyperparameter tuning
6. Evaluation using cross-validation and performance metrics