ML project structure in ML Python - ML Experiment: Train & Evaluate

Experiment - ML project structure

Problem:You want to organize a machine learning project so it is easy to understand, maintain, and improve.

Current Metrics:No measurable metrics yet because the project is unorganized and confusing.

Issue:The project files and code are scattered without clear folders or naming. This makes it hard to find data, code, or results and slows down progress.

Your Task

Create a clear folder and file structure for a machine learning project that separates data, code, models, and results.

Do not change the machine learning model or data itself.

Focus only on organizing files and folders.

Use simple and common folder names.

Hint 1

Hint 2

Hint 3

Hint 4

Hint 5

Solution

ML Python

project_root/
├── data/
│   ├── raw/
│   └── processed/
├── notebooks/
│   └── exploration.ipynb
├── scripts/
│   ├── train.py
│   └── preprocess.py
├── models/
│   └── model_v1.pkl
├── results/
│   └── evaluation.txt
└── README.md

# README.md example content:
# This project folder contains:
# - data/: raw and processed data
# - notebooks/: Jupyter notebooks for exploration
# - scripts/: Python scripts for training and preprocessing
# - models/: saved machine learning models
# - results/: evaluation outputs and plots

# This structure helps keep the project organized and easy to navigate.

Created main folders: data, notebooks, scripts, models, results.

Separated raw and processed data inside data folder.

Moved code files into scripts folder.

Kept notebooks separate for exploration and visualization.

Saved models and results in their own folders.

Added README.md to explain the project structure.

Results Interpretation

Before: Files scattered, hard to find data or code.
After: Clear folders for data, code, models, and results. Easy to navigate and update.

A well-structured project folder helps you and others understand and work on the machine learning project efficiently. Organization is a key step before improving models.

Bonus Experiment

Add automated scripts to preprocess data and train the model, and organize them in the scripts folder.

💡 Hint

Create separate Python scripts for each step and use clear function names. This makes running and updating your project easier.