What is involved:
Clearly articulate the problem you’re trying to solve and the outcomes you expect.
Follow up questions
What assumption can we make based on the problem?
What kind of questions are good to ask?
Business Context:
- What are the desired outcomes and how would success be measured?
- What are the limitations and feasibility of using machine learning in this context?
2. Data Availability and Quality:
- What data is available in quantity and quality and relevant to the problem?
- What is the format and structure of the data?
3. Feature Engineering and Model Selection:
- What are the key features or variables that might be predictive of the desired outcome?
- What type of machine learning model might be best suited for this problem (e.g., classification, regression, Clustering)?
4. Evaluation and Deployment:
- How will we evaluate the performance of the machine learning model?
- What metrics will be used to measure success?