Welcome to the DSFX-RFFI-Analyzer repository! This toolkit combines the strengths of descriptive statistics (DSFX) and Random Forest Feature Importance (RFFI) to provide a robust data analysis solution.
Data analysis is a critical step in gaining insights and making informed decisions. DSFX-RFFI-Analyzer simplifies this process by offering:
- Comprehensive Descriptive Statistics: Gain a deep understanding of your data's characteristics, including central tendency, dispersion, and distribution.
- Feature Importance with Random Forest: Identify the most critical features in your dataset, enhancing predictive modeling and decision-making.
- Efficiency and Speed: Achieve efficient data analysis with reduced computational overhead.
- Data Profiling: Get a holistic profile of your dataset, including data summaries, distributions, and feature importance rankings.
- Interactive Visualizations: Explore data through interactive visualizations, facilitating intuitive exploration and interpretation.
To get started with DSFX-RFFI-Analyzer, follow these steps:
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Installation: Clone this repository to your local machine.
git clone https://github.com/yourusername/DSFX-RFFI-Analyzer.git
Dependencies: Install the necessary dependencies. You can find a list in the requirements.txt file. Usage: Explore the example notebooks in the examples directory to see how to use DSFX-RFFI-Analyzer for your data analysis tasks.
Contributions: We welcome contributions! If you have suggestions, bug fixes, or new features to add, please submit a pull request.
To acquire the main training file, please send me an email with the subject GitHub DSFX main data request.
Documentation For detailed documentation and examples, email me at hnamdari@wpi.edu.
License
Acknowledgments Special thanks to contributors and open-source libraries that make this project possible. Contact For questions or support, please contact us at [hnamdari@wpi.edu].
Happy analyzing!