SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each. Much of the savings is because SpaceX can reuse the first stage of the rocket. This project aims to create a machine learning model to predict whether the first stage of the Falcon 9 rocket will successfully land, which can significantly impact the overall cost of a launch.
- Data Preprocessing: Cleaning and preparing the initial data for model training.
- Feature Engineering: Creating new features from existing data to improve model performance.
- Model Training: Using various machine learning algorithms (such as Logistic Regression, SVM, Decision Tree, and KNN) to create predictive models.
- Model Evaluation: Evaluating model performance using various metrics (such as Accuracy, Precision, Recall, and Confusion Matrix).
- Model Comparison: Comparing the performance of different models to select the best one.
- Visualization: Displaying results using charts and images.
The following machine learning algorithms are implemented and compared in this project:
- K-Nearest Neighbors (KNN): A non-parametric algorithm that classifies data based on the majority class among its k nearest neighbors.
- Decision Tree: A tree-like model that makes decisions based on feature values.
- Support Vector Machine (SVM): A powerful algorithm that finds the optimal hyperplane to separate data into different classes.
- Logistic Regression: A linear model that predicts the probability of a binary outcome.
- Python: The primary programming language for the project.
- Pandas: For data management and analysis.
- Matplotlib: For creating charts and images.
- Seaborn: For creating visually appealing statistical graphs.
- Scikit-learn: For training and evaluating machine learning models.
To install the required libraries, use the following command:
pip install pandas matplotlib seaborn scikit-learn