No correlation found between dataset features
Think about the data generating process: For taxi fares, THE relevant thing is distance/time of the trip. This is by definition the case for most taxi services (Uber might be different). Maybe time of the day plays a role as well, e.g. when fares during day/night are different.

In a simple case you might not even need a statistic solution, viz. if the problem is deterministic, meaning fare is a simple linear function of distance.

You can use (for example) a linear regression to predict fare. In this case you could have a model:

fare = b0 + b1 * dist + error.


In this model, there is nothing wrong with having only one predictor (this is a univariate linear regression).

BTW: If you predict a continuous variable, you do regression. If you predict classes (like yes/no) you do classification.

If you have many variables with only weak correlation to y, you can also use regulation (by L1 norm), in linear regression this is called Lasso, to „shrink“ features which are irrelevant.

P.S. Plot price (y) against distance (x). You will likely find close alignment of both.