Many machine learning students fail before they even start—because they pick the wrong research topic. Here's a simple 5-step framework to choose a topic that is feasible, publishable, and impactful.
- Identify a real problem – Look for gaps in existing literature or practical challenges.
- Assess data availability – Ensure you have access to quality datasets.
- Evaluate resources – Consider compute, time, and expertise required.
- Validate novelty – Check that your approach offers something new.
- Define success metrics – Set clear benchmarks to measure impact.
Stop wasting months on the wrong idea and start building something that matters.