{"slug": "day-4-create-a-standard-ml-project-structure", "title": "Day 4: Create a Standard ML Project Structure", "summary": "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`.", "body_md": "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.\n\n```\nInspect the existing project at /root/code/fraud-detection/.\n\nThe final layout must match the tree below exactly:\n```\n\nfraud-detection/\n\n├── data/\n\n│ ├── raw/\n\n│ └── processed/\n\n├── models/\n\n├── notebooks/\n\n├── src/\n\n│ ├── data/\n\n│ ├── features/\n\n│ ├── models/\n\n│ └── utils/\n\n├── tests/\n\n├── configs/\n\n├── requirements.txt\n\n└── README.md\n\n```\nEvery subdirectory under src/ must contain an __init__.py file so that Python recognises it as a package.\n\nrequirements.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.\n\nREADME.md must begin with the heading # fraud-detection.\n\nReview the existing project and correct everything that does not match the requirements above.\n```\n\n🧭 Part 1: Lab Step-by-Step Guidelines\n\nRun the following commands on the controlplane host.\n\nStep 1 — Move into the project directory\n\n```\ncd /root/code/fraud-detection\n```\n\nStep 2 — Inspect the current structure\n\n```\ntree\n```\n\nIf tree is unavailable:\n\n```\nsudo apt update && sudo apt install tree\n```\n\nStep 3 — Check the required directory structure\n\nRun:\n\n```\ntree\nmv src/feature src/features\nmv src/util src/utils\nmkdir -p data/raw\nmkdir -p data/processed\nmkdir -p tests\nmkdir -p configs\n```\n\nCheck:\n\n```\nls src/features\nls src/utils\n```\n\nYou should see:\n\n**init**.py\n\nStep 4 — Verify and fix requirements.txt\n\nCreate/update the file:\n\n```\ncat > requirements.txt\n```\n\nOutput:\n\n```\nsklearn\npandas\nnumpy\n```\n\nCreate the correct requirements.txt:\n\n```\ncat > requirements.txt <<EOF\nscikit-learn\npandas\nnumpy\nmlflow\nEOF\n```\n\nCreate/update the README:\n\n```\ncat README.md\n```\n\nOutput:\n\n```\n# Fraud\n\nML project for fraud detection at xFusionCorp Industries.\n```\n\nReplace the README\n\nRun:\n\n```\ncat > README.md <<EOF\n# fraud-detection\n\nML project for fraud detection at xFusionCorp Industries.\nEOF\n```\n\nStep 7 — Verify the final structure and README.md content\n\nRun:\n\n```\ntree\ncat README.md\n```\n\nExpected structure:\n\nfraud-detection/\n\n├── data/\n\n│ ├── raw/\n\n│ └── processed/\n\n├── models/\n\n├── notebooks/\n\n├── src/\n\n│ ├── data/\n\n│ │ └── **init**.py\n\n│ ├── features/\n\n│ │ └── **init**.py\n\n│ ├── models/\n\n│ │ └── **init**.py\n\n│ └── utils/\n\n│ └── **init**.py\n\n├── tests/\n\n├── configs/\n\n├── requirements.txt\n\n└── README.md\n\n```\nroot@controlplane ~/code/fraud-detection via 🐍 v3.12.3 ➜  cat README.md \n# fraud-detection\n\nML project for fraud detection at xFusionCorp Industries.\n```\n\n🧠 Part 2: Simple Beginner-Friendly Explanation\n\nThis lab focuses on organising a machine learning project according to the xFusionCorp Industries standard structure.\n\nThe goal is to:\n\nstandardise project layout\n\nimprove maintainability\n\nmake collaboration easier for developers and data scientists\n\nYou must inspect the existing project and correct anything that does not match the required structure.\n\nUnderstanding the Required Project Structure\n\nThe final project must look exactly like this:\n\nfraud-detection/\n\n├── data/\n\n│ ├── raw/\n\n│ └── processed/\n\n├── models/\n\n├── notebooks/\n\n├── src/\n\n│ ├── data/\n\n│ ├── features/\n\n│ ├── models/\n\n│ └── utils/\n\n├── tests/\n\n├── configs/\n\n├── requirements.txt\n\n└── README.md\n\nEach folder has a specific purpose in an ML workflow.\n\nPurpose of Each Directory\n\ndata/\n\nStores datasets used in the project.\n\ndata/raw/\n\nContains original unmodified data.\n\nExample:\n\ntransactions.csv\n\ndata/processed/\n\nContains cleaned or transformed datasets used for training.\n\nExample:\n\nclean_transactions.csv\n\nmodels/\n\nStores trained machine learning models.\n\nExample:\n\nfraud_model.pkl\n\nnotebooks/\n\nContains Jupyter notebooks for experimentation and analysis.\n\nExample:\n\neda.ipynb\n\nsrc/\n\nContains the main Python source code for the application.\n\nThis keeps project logic organised and modular.\n\nWhy **init**.py Files Are Required\n\nEvery subdirectory under src/ must contain:\n\n**init**.py\n\nThis tells Python:\n\n“Treat this directory as a Python package.”\n\nWithout these files:\n\nimports may fail\n\nmodules may not be recognised correctly\n\nExample:\n\nfrom src.models.train import train_model\n\nPurpose of src/ Subdirectories\n\nDirectory Purpose\n\nsrc/data Data loading and preprocessing\n\nsrc/features Feature engineering logic\n\nsrc/models Training and prediction code\n\nsrc/utils Helper functions and utilities\n\nWhy requirements.txt Matters\n\nThe lab requires the following dependencies:\n\nscikit-learn\n\npandas\n\nnumpy\n\nmlflow\n\nThis file helps developers install all required Python packages consistently.\n\nImportant note:\n\nthe correct PyPI package name is scikit-learn\n\nnot sklearn\n\nWhy README.md Matters\n\nThe README file provides project documentation.\n\nThe lab specifically requires it to begin with:\n\nThis acts as the project title and ensures naming consistency.\n\nWhy Exact Naming Is Important\n\nLab validators check:\n\nexact folder names\n\nexact file names\n\nexact dependency names\n\nEven small differences such as:\n\nfeature instead of features\n\nutil instead of utils\n\ncan cause the lab to fail.", "url": "https://wpnews.pro/news/day-4-create-a-standard-ml-project-structure", "canonical_source": "https://dev.to/thukhakyawe_cloud/day-4-create-a-standard-ml-project-structure-3hi7", "published_at": "2026-06-03 12:35:33+00:00", "updated_at": "2026-06-03 12:42:29.619552+00:00", "lang": "en", "topics": ["machine-learning", "mlops", "artificial-intelligence", "ai-tools", "ai-infrastructure"], "entities": ["xFusionCorp Industries", "scikit-learn", "pandas", "numpy", "mlflow"], "alternates": {"html": "https://wpnews.pro/news/day-4-create-a-standard-ml-project-structure", "markdown": "https://wpnews.pro/news/day-4-create-a-standard-ml-project-structure.md", "text": "https://wpnews.pro/news/day-4-create-a-standard-ml-project-structure.txt", "jsonld": "https://wpnews.pro/news/day-4-create-a-standard-ml-project-structure.jsonld"}}