Detailed project roadmap with milestones and deadlines.

Functional and non-functional requirements. User stories and acceptance criteria.

Data acquisition plan. Raw data sets. Data cleaning and preprocessing scripts. Exploratory data analysis (EDA) report.

Jupyter notebooks or scripts for model training. Description of model architecture and algorithms used. Hyperparameter tuning details. Model training logs and metrics.

Evaluation metrics and performance reports (e.g., accuracy, precision, recall).

Detailed description of the final model. Assumptions and limitations. Guidelines for model retraining and updates.

API endpoints for model inference. Integration with existing systems and applications. Load testing and performance optimization reports.

Code documentation (comments, readme files). API documentation (Swagger, Postman collections). User manuals and guides for using the model and API.

Complete source code repository. Access credentials for all services and environments. Final project report and summary. Post-deployment support plan.