Similar situation here - I went through this last year when my marketing sheets basically broke Excel lol. I tried the Claude 3.5 Sonnet route like the other guy said and it was cool for logic, but for my budget, I actually ended up sticking with the ChatGPT Plus subscription using the Advanced Data Analysis feature. It's only $20/month which is way cheaper than enterprise stuff, but honestly, you gotta be careful with the file sizes. I learned the hard way to clean my messy CSVs in small chunks first because it highkey hallucinations if you dump 50MB at once... so yeah, it's a bit of a trade-off between price and speed tbh.

> I’ve experimented a bit with ChatGPT’s Data Analyst feature, and while it’s great for quick summaries, it sometimes struggles with very large datasets or hallucinates when the logic gets too tiered.

Seconding the recommendation above. I totally feel u on the hallucination stuff—it's highkey scary when you're dealing with customer data and the AI just makes up a correlation out of thin air!

I've been doing this for over 8 years now and honestly, if ur looking for a safety-first perspective, you gotta look into Polymer Search. It's basically a pro-grade tool that focuses way more on data integrity than just being a chatbot. While Julius AI and Claude 3.5 Sonnet are amazing for logic, Polymer is like... a tank for big CSVs.

Basically, the reason why safety matters here is that generic LLMs often "guess" the next token based on probability, which is how you get those tiered logic errors. Polymer actually builds a structured index of ur data first, so it literally can't hallucinate the values. It’s also great for high-dimensional data cuz it handles the cleaning part automatically without u needing to write messy Python scripts. I used it for a massive marketing spend audit last year and it caught stuff I would've missed for sure!

Also, if u need to verify things, Akkio is another fantastic option for predictive modeling that shows u the actual "why" behind the stats. It's super user-friendly but keeps things transparent so u can trust the output... right?

Anyway, gl with the project! 👍

For your situation, I've spent years dealing with messy CSVs and honestly, Julius AI is probably the most robust option right now if you want to avoid code-heavy setups. It handles high-dimensional data way better than ChatGPT's interface, and the predictive modeling is legit because it writes and runs actual Python in the background that you can verify. If you need more enterprise-grade accuracy, I'd suggest looking into Claude 3.5 Sonnet via an interface like typingmind, as its logic is way more reliable for complex statistical tiered questions. I've found it hallucinates way less than GPT-4 when things get technical. Just gotta be careful with really massive files, but for hidden correlations?? Julius is a game-changer lol. gl!

Seconding the recommendation above. Julius is killer for quick insights, but if you're worried about reliability for high-dimensional stuff, I'd suggest checking out Claude 3.5 Sonnet. It's honestly been a game-changer for my messy CSVs lately because it's way more cautious with logic than GPT-4o.

I've spent like 5 years wrestling with Python scripts and here's my two cents on why this matters:

1. Reasoning depth: Claude usually handles complex, tiered logic without 'hallucinating' math as much as others.
2. Code verification: You can ask it to output the exact Python code so you can run it locally to verify the stats.
3. Data cleaning: It's surprisingly good at spotting formatting errors in huge files.

Just be careful with super large datasets; sometimes you gotta chunk them or use the Julius AI interface which handles the memory better. Good luck with the project!

Just caught up with the thread. Basically, the consensus seems to be that Julius is great for ease, while Claude 3.5 is the heavy hitter for logic. But honestly, if you're handling high-dimensional data and need to actually trust the stats for marketing spend, I'd be wary of letting any AI have full black box control. I'm a bit more old-school and cautious about hallucinations, so I'd suggest a more DIY verified approach. Instead of a single platform that hides the work, try setting up a controlled environment where you're still the pilot. It gives you way more control over messy data cleaning and ensures you can audit the results. My go-to stack lately for reliability:

• Anaconda Individual Edition for managing your local Python and R environments safely.
• GitHub Copilot Individual integrated into VS Code for generating the boilerplate pandas or scikit-learn code.
• Deepnote Python Notebook if you want a cloud-based notebook that has built-in AI assistance but keeps the code front and center. This way, the AI writes the script, but you're the one hitting run and checking the logic. It prevents those out of thin air correlations because you can see exactly how the math is being calculated in the code blocks... plus it's easier to scale when your CSVs get really massive.

Facts.

Would love to know this too