Trained Model: Optimized and ready-to-deploy machine learning model files.
Documentation: Project report with problem definition, workflow, and deployment steps.
Codebase: Clean, well-documented scripts for data preprocessing, training, and evaluation.
Dataset: Processed dataset and description (if shareable).
Performance Metrics: Key results with graphs (e.g., accuracy, precision, ROC curve).
Deployment Artifacts: APIs, Docker setup, or cloud-hosted solutions (if applicable).
User Interface: Interactive dashboards or tools (if in scope).
Recommendations: Future improvement suggestions and actionable insights.
Presentation: Final walkthrough and live demo (if required).
Support: Post-delivery support for bug fixes or clarifications.