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.