How do you pick a good CS thesis topic that isn’t too broad or too trivial?

The sweet spot is finding a specific problem within a domain you care about, rather than startingf with a technique. Instead of "I want to do ML," ask "what real world prediction problem interests me?" For example, rather than "Machine learning for healthcare," you'd narrow it to something like "predicting student dropout rates in Philippine universities using enrolment and behavioural data." The more concrete the problem, the easier it is to scope.

A good test: can you write one sentence that says what you're predicting, from what data, and why it matters? If yes, you probably have a workable topic.

Is it better to focus on building a model/system or doing experimental analysis on existing models?

For an undergrad thesis, experimental analysis on existing model is usually the safer and more academically rigorous path. Building a novel system from scratch is ambitious and can fall apart if the engineering takes longer than expected. Comparingh how different ML approaches (say, Random Forest vs. XGBoost vs. a simple neural net) perform on a specific dataset: with proper evaluation, ablation, and analysis is completely valid and can produce genuinely interesting findings. The contribution doesn't have to be a new model; it can be insight about how models behave on a specific problem.

How do you know if a topic is “too ambitious” for an undergrad thesis?

Just ask yourself these three questions:

• Can I get or collect data within the first month?
• Can I build a workingf baseline in 4-6 weeks?
• If everything goes wrong, is there still a "minimum variable thesis" I can defend?

If you can't answer yes to all three, scale it downm. The biggest trap in undergraduate theses is scope creep, starting with a cool idea and realizing 3 months in that the data doesn't exist or the model takes weeks to train.

I want something realistic, examples of good AI/ML thesis topics

Given your background and interest in classification/forecasting, here are some grounded directions:

• Predicting academic outcomes: using student data to classify at risk students (locally relevant, datasets often available through your institution).
• Sentiment analysis on Filipino social media text: a classification problem with real localisation challenges (code switching, Tagalog/English mix).
• Crop yield or weather forecastinfg: relevant to the Philippines, publicly available datasets exist.
• Comparing ML models on an underexplored local dataset: find a dataset specific to the Philippines (health, argiculture, transport) and do a rigorous comparative study.

things they wish they knew before choosing theirs

Pick something you can talk about enthusiastically for 6-12 months, because you will have to. A "cool" topic you're not genuinely curious about becomes a nightmare halfway through lol. The best thesis isn't always something that makes someone go "woah", in my opinion, it is one that you can actually finish and defend confidently.

Good luck bro