This project focused on predicting the landing success of SpaceX Falcon 9 rockets using machine learning techniques. By analyzing historical data and leveraging advanced algorithms, we developed a model capable of accurately predicting launch outcomes. This work has significant implications for the aerospace industry, as it can help optimize launch decisions and reduce costs.
I employed a data-driven approach to tackle this problem. By collecting and cleaning data from various sources, including the SpaceX REST API and web scraping, I created a comprehensive dataset. I then conducted exploratory data analysis (EDA) to identify key trends and patterns. Subsequently, I trained and evaluated several machine learning models, ultimately selecting the most suitable one based on performance metrics. The resulting model demonstrated impressive accuracy in predicting Falcon 9 landing success, offering significant potential for cost savings in future launches.
- Data Collection: Gathered relevant data from the SpaceX REST API and web scraping.
- Data Cleaning and Preprocessing: Cleaned and prepared the data for analysis, handling missing values and inconsistencies.
- Exploratory Data Analysis: Conducted EDA to understand the data, identify trends, and explore relationships between variables.
- Feature Engineering: Created new features to enhance model performance and capture relevant information.
- Model Selection and Training: Trained and evaluated various machine learning models, including Logistic Regression, Support Vector Machine, Decision Tree, and K-Nearest Neighbors.
- Hyperparameter Tuning: Optimized model performance by tuning hyperparameters.
- Model Evaluation: Assessed model accuracy and performance using appropriate metrics.
- Deployment: Considered the potential for deploying the model in a production environment.
This approach allowed me to systematically address the problem, build a robust model, and extract valuable insights from the data.