Project Scoping: Defining the Problem and Success Criteria
Learners will write a one-page project charter that specifies the prediction target, success metrics, data sources, and deployment constraints before writing any code.
Why Scoping Is the Most Important Step
The single most common reason ML projects fail is not a poorly chosen algorithm — it is a poorly defined problem. Project scoping translates a vague business need ('we want to use AI') into a precise, measurable specification that guides every subsequent technical decision. A well-scoped project defines the prediction target, success criteria, data requirements, and deployment constraints before writing a single line of modelling code.
Choosing the Right ML Formulation
The first scoping decision is the ML task type. Ask: is the output a number (regression), a category (classification), a ranking (learning to rank), a cluster (unsupervised), or a sequence (NLP/time-series)? Each leads to different algorithms, data requirements, and metrics. For example, 'predict customer churn' is a binary classification problem, not a regression problem, even though the business ultimately cares about revenue.
# Scoping checklist — ML task type
questions = [
'What is the exact output the model should produce?',
'Is the output a number (regression) or a category (classification)?',
'Do we need probabilities, or just labels?',
'Is there a natural ordering in the labels (ordinal vs nominal)?',
'Is this supervised (labelled data exists) or unsupervised?',
'Is time ordering important (time-series)?'
]
for q in questions:
print('•', q)All lessons in this course
- Project Scoping: Defining the Problem and Success Criteria
- Data Wrangling and Exploratory Data Analysis
- Model Selection Tournament: Compare Five Algorithms
- Packaging, Documenting, and Presenting the Final Model