This repository provides a computational environment for analyzing datasets for machine learning tasks. It includes scripts for data generation, visualization of datasets, and analysis of model predictions. The project is compatible with Python 3.10 or higher and integrates key libraries such as scikit-learn, pandas, and matplotlib for streamlined workflows.
- Visualize generated datasets and save the plots as PNG files (dataset-image.png).
- Analyze and visualize model predictions on test data (predictions.png).
- Comprehensive documentation of meeting attendees, agendas, and minutes.
- Clear assignment of action items to team members.
- Includes a Conda environment (environment.yaml) to ensure reproducibility for analytics and visualizations.
- data/: Stores datasets.
- docs/: Documentation and guides.
- images/: Contains plots and visualizations (dataset-image.png, predictions.png).
- reports/: Analysis or report outputs.
- scr/: Source code for data analysis, visualization, and machine learning models.
- .gitignore: Ensures unnecessary files (e.g., logs, temporary files) are not tracked.
- environment.yaml: Conda environment file to replicate the computational environment.
Python 3.10 or higher
- numpy: For data generation
- pandas: For data manipulation and saving
- matplotlib: For visualization
- scikit-learn: For machine learning models
- JupyterLab: For interactive analysis
This project is licensed under the MIT License. See the LICENSE file for details.