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.