Project Overview

Use this page to orient readers to the data science project itself. Replace the placeholder prompts with real content as your work progresses.

Executive Summary

• Problem statement: What business/research question are you answering?
• Stakeholders: Who will consume the insights/models?
• Success metrics: Define quantitative or qualitative measures of success.
• Timeline: Current phase, major milestones, expected delivery dates.

Objectives & Scope

• Primary objectives (bulleted list).
• Out-of-scope considerations or known exclusions.
• Assumptions and constraints (data availability, compute limits, compliance).

Project Workflow

1. Data acquisition – summary of data sources to be ingested (see “Data & Experimentation”).
2. Exploration & analysis – notebooks, hypothesis generation.
3. Feature engineering & modelling – pipelines covered in “Modeling Strategy”.
4. Evaluation & results – reporting cadence and stakeholders (see “Results & Reporting”).
5. Deployment or delivery – how the findings/models will be consumed.

Repository Highlights

• README.md – quickstart commands and developer workflow.
• scripts/run_pipeline.py – example training pipeline (supports --demo).
• tests/unit/ – unit tests demonstrating expected behaviour.
• docs/ – these documentation pages; publish via GitHub Pages.

How to Contribute

• Update this page whenever objectives, scope, or milestones shift.
• Link to deeper documentation (e.g., dedicated experiment logs, dashboards, presentations).
• Use consistent headings across sections so teammates can quickly find information.