Day 4: Create a Standard ML Project Structure

A developer at xFusionCorp Industries restructured a fraud-detection machine learning project at `/root/code/fraud-detection/` to match the company's standard layout. The project now includes directories for `data/raw/`, `data/processed/`, `models/`, `notebooks/`, `src/` with subpackages, `tests/`, and `configs/`, along with a corrected `requirements.txt` listing scikit-learn, pandas, numpy, and mlflow, and a `README.md` beginning with `# fraud-detection`.

A colleague has started a new ML project at /root/code/fraud-detection/, but the layout does not match the xFusionCorp Industries standard. Bring the project in line with the team's conventions. Inspect the existing project at /root/code/fraud-detection/. The final layout must match the tree below exactly: fraud-detection/ ├── data/ │ ├── raw/ │ └── processed/ ├── models/ ├── notebooks/ ├── src/ │ ├── data/ │ ├── features/ │ ├── models/ │ └── utils/ ├── tests/ ├── configs/ ├── requirements.txt └── README.md Every subdirectory under src/ must contain an init .py file so that Python recognises it as a package. requirements.txt must list the following dependencies, one per line: scikit-learn, pandas, numpy, and mlflow. The canonical PyPI name for the scikit-learn package is scikit-learn. README.md must begin with the heading fraud-detection. Review the existing project and correct everything that does not match the requirements above. 🧭 Part 1: Lab Step-by-Step Guidelines Run the following commands on the controlplane host. Step 1 — Move into the project directory cd /root/code/fraud-detection Step 2 — Inspect the current structure tree If tree is unavailable: sudo apt update && sudo apt install tree Step 3 — Check the required directory structure Run: tree mv src/feature src/features mv src/util src/utils mkdir -p data/raw mkdir -p data/processed mkdir -p tests mkdir -p configs Check: ls src/features ls src/utils You should see: init .py Step 4 — Verify and fix requirements.txt Create/update the file: cat requirements.txt Output: sklearn pandas numpy Create the correct requirements.txt: cat requirements.txt <<EOF scikit-learn pandas numpy mlflow EOF Create/update the README: cat README.md Output: Fraud ML project for fraud detection at xFusionCorp Industries. Replace the README Run: cat README.md <<EOF fraud-detection ML project for fraud detection at xFusionCorp Industries. EOF Step 7 — Verify the final structure and README.md content Run: tree cat README.md Expected structure: fraud-detection/ ├── data/ │ ├── raw/ │ └── processed/ ├── models/ ├── notebooks/ ├── src/ │ ├── data/ │ │ └── init .py │ ├── features/ │ │ └── init .py │ ├── models/ │ │ └── init .py │ └── utils/ │ └── init .py ├── tests/ ├── configs/ ├── requirements.txt └── README.md root@controlplane ~/code/fraud-detection via 🐍 v3.12.3 ➜ cat README.md fraud-detection ML project for fraud detection at xFusionCorp Industries. 🧠 Part 2: Simple Beginner-Friendly Explanation This lab focuses on organising a machine learning project according to the xFusionCorp Industries standard structure. The goal is to: standardise project layout improve maintainability make collaboration easier for developers and data scientists You must inspect the existing project and correct anything that does not match the required structure. Understanding the Required Project Structure The final project must look exactly like this: fraud-detection/ ├── data/ │ ├── raw/ │ └── processed/ ├── models/ ├── notebooks/ ├── src/ │ ├── data/ │ ├── features/ │ ├── models/ │ └── utils/ ├── tests/ ├── configs/ ├── requirements.txt └── README.md Each folder has a specific purpose in an ML workflow. Purpose of Each Directory data/ Stores datasets used in the project. data/raw/ Contains original unmodified data. Example: transactions.csv data/processed/ Contains cleaned or transformed datasets used for training. Example: clean transactions.csv models/ Stores trained machine learning models. Example: fraud model.pkl notebooks/ Contains Jupyter notebooks for experimentation and analysis. Example: eda.ipynb src/ Contains the main Python source code for the application. This keeps project logic organised and modular. Why init .py Files Are Required Every subdirectory under src/ must contain: init .py This tells Python: “Treat this directory as a Python package.” Without these files: imports may fail modules may not be recognised correctly Example: from src.models.train import train model Purpose of src/ Subdirectories Directory Purpose src/data Data loading and preprocessing src/features Feature engineering logic src/models Training and prediction code src/utils Helper functions and utilities Why requirements.txt Matters The lab requires the following dependencies: scikit-learn pandas numpy mlflow This file helps developers install all required Python packages consistently. Important note: the correct PyPI package name is scikit-learn not sklearn Why README.md Matters The README file provides project documentation. The lab specifically requires it to begin with: This acts as the project title and ensures naming consistency. Why Exact Naming Is Important Lab validators check: exact folder names exact file names exact dependency names Even small differences such as: feature instead of features util instead of utils can cause the lab to fail.