Lab Information
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.
Lab Solutions
🧭 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
- Rename incorrect directories
mv src/feature src/features
mv src/util src/utils
- Create missing directories
mkdir -p data/raw
mkdir -p data/processed
mkdir -p tests
mkdir -p configs
- Verify init.py still exists
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:
fraud-detection
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
Fraud instead of # fraud-detection
can cause the lab to fail.