[Project Name: e.g., Retail Sales Forecasting]

test_change

Author: TODO_STUDENT (Your Group Name or number)
Course: MLOps: Master in Business Analytics and Data Sciense Status: Session 1 (Initialization)

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1. Business Objective

Replace this section with your project definition.

• The Goal: What business value does this model create?

  Example: Reduce food waste by 10% by predicting daily bakery demand.

• The User: Who consumes the output and how?

  Example: Store managers receive a weekly PDF report on Monday mornings.

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2. Success Metrics

How do we know if the project is successful?

• Business KPI (The "Why"):

  Example: Reduce unsold inventory costs by $5,000/month.

• Technical Metric (The "How"):

  Example: Model MAPE (Mean Absolute Percentage Error) < 15% on the test set.

• Acceptance Criteria:

  Example: The model must outperform the current "moving average" baseline.

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3. The Data

• Source: (e.g., Company Database, Kaggle CSV, API).
• Target Variable: What specifically are you predicting/ classifying?
• Sensitive Info: Are there emails, credit cards, or any PII (Personally Identifiable Information)?

  ⚠️ WARNING: If the dataset contains sensitive data, it must NEVER be committed to GitHub. Ensure data/ is in your .gitignore.

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4. Repository Structure

This project follows a strict separation between "Sandbox" (Notebooks) and "Production" (Src).

.
├── README.md                # This file (Project definition)
├── environment.yml          # Dependencies (Conda/Pip)
├── config.yaml              # Global configuration (paths, params)
├── .env                     # Secrets placeholder
│
├── notebooks/               # Experimental sandbox
│   └── yourbaseline.ipynb   # From previous work
│
├── src/                     # Production code (The "Factory")
│   ├── __init__.py          # Python package
│   ├── load_data.py         # Ingest raw data
│   ├── clean_data.py        # Preprocessing & cleaning
│   ├── validate.py          # Data quality checks
│   ├── train.py             # Model training & saving
│   ├── evaluate.py          # Metrics & plotting
│   ├── infer.py             # Inference logic
│   └── main.py              # Pipeline orchestrator
│
├── data/                    # Local storage (IGNORED by Git)
│   ├── raw/                 # Immutable input data
│   └── processed/           # Cleaned data ready for training
│
├── models/                  # Serialized artifacts (IGNORED by Git)
│
├── reports/                 # Generated metrics, plots, and figures
│
└── tests/                   # Automated tests

5. Execution Model

The full machine learning pipeline will eventually be executable through:

python src/main.py