# Results Directory

This directory stores the outputs generated during the project, such as visualizations, models, and metrics.

## Structure

- `README.md`: This file explains the contents and structure of the `results/` directory.
- `figures/`: Contains visualizations like graphs and charts.
- `metrics/`: Stores evaluation metrics for supervised and unsupervised models.
- `models/`: Includes serialized models saved during training.

## Details

### Figures

- Purpose: Visualize data distributions, model performance, and clustering results.
- Format: PNG, PDF, or other supported formats.
- Examples:
  - Data distribution histograms.
  - Model accuracy and loss curves.
  - Clustering visualizations (e.g., Elbow Method, Silhouette Analysis).

### Metrics

- Contents: Performance metrics like confusion matrices, classification reports, and clustering scores.
- Format: CSV, JSON, or plain text.
- Examples:
  - Accuracy, precision, recall, and F1-score for classification models.
  - Silhouette scores and inertia values for clustering models.

### Models

- Format: Pickled files (`.pkl`) or HDF5 files (`.h5`).
- Usage: Loadable for prediction or further experimentation.
- Examples:
  - Trained classifiers (e.g., Logistic Regression, Random Forest).
  - Fine-tuned language models (e.g., BERT, Doc2Vec).

## Notes

- Figures are generated in the notebooks or scripts.
- Models are updated after significant training sessions.