How to do this properly

How do I train a model without writing code?

Upload a spreadsheet, pick the column you want to predict, and let a tool run the pipeline — cleaning, splitting, training several models, and comparing them against a baseline. The part that matters is the comparison, not the training.

How do I forecast next month's sales from my spreadsheet?

Put your history in date order, decide how far ahead you need, and always compare the forecast against the naive one — repeating the last value. A forecast that cannot beat that is not a forecast.

Can AI write a research paper with real citations?

It can, but most tools invent references. The test is whether every number can be traced to a quote that appears verbatim in a paper you can open — and whether the tool tells you which papers it searched and how many it discarded.

My model is 94% accurate. Is that good?

On its own, no — it is not information. If 94% of your rows are one class, a model that always guesses that class scores 94% too. The only number that means anything is how much better you are than doing nothing.

What should I do about missing values in my data?

Drop rows when few are affected, use the median rather than the mean when a column has outliers, and add a flag when the fact that something was missing is itself meaningful. Whatever you choose, decide it on the training data only.

How do I know which columns actually matter?

Scoring each column against the target on its own is fast and misses anything that only matters in combination. When your columns are correlated — as sensor readings usually are — you need a method that judges them together.