This repository contains code and resources for classifying the success of SpaceX launches using machine learning techniques. The project aims to predict whether a SpaceX launch will be successful based on various factors and features.
- data: Contains datasets used for training and testing the classification models.
- notebooks: Jupyter notebooks for data exploration, preprocessing, model training, evaluation, and visualization.
- scripts: Python scripts for data preprocessing, model training, and other tasks.
- models: Saved trained models for future use or deployment.
- reports: Reports generated from the analysis, including model performance metrics and visualizations.
- README.md: This file providing an overview of the repository.
The classification task involves predicting the success outcome of SpaceX launches, which is typically binary (successful or unsuccessful). Features used for classification may include launch date, mission type, payload mass, launch site, and other relevant factors.
The analysis includes the following steps:
- Data Collection: Gathering historical data on SpaceX launches from public sources or APIs.
- Data Preprocessing: Cleaning, filtering, and transforming the raw data to prepare it for analysis.
- Feature Engineering: Extracting, selecting, or creating relevant features for the classification task.
- Model Selection: Choosing appropriate machine learning algorithms for classification, such as logistic regression, random forests, or neural networks.
- Model Training: Training classification models using labeled data from past SpaceX launches.
- Model Evaluation: Assessing the performance of trained models using metrics like accuracy, precision, recall, and F1-score.
- Model Deployment: If applicable, deploying the trained models for real-time or batch predictions.
To replicate the analysis or contribute to the project, follow these steps:
- Clone or download this repository to your local machine.
- Install the necessary dependencies listed in
requirements.txt. - Explore the notebooks and scripts to understand the analysis workflow.
- Execute the scripts or run the notebooks to perform analysis tasks.
- Modify, extend, or improve the analysis as needed.
- If contributing, follow the contribution guidelines outlined in
CONTRIBUTING.md.
The analysis utilizes data from various sources, including:
- SpaceX API (if available)
- Publicly available datasets on SpaceX launches
- Other relevant sources for supplementary data
Contributions to this project are welcome! If you have suggestions, bug reports, or want to add new analysis, please open an issue or submit a pull request following the guidelines outlined in CONTRIBUTING.md.
This project is licensed under the MIT License.
Feel free to modify this template to suit the specific details and requirements of your repository. Let me know if you need further assistance!