## Question

Independent and dependent variables for predictions

What independent and dependent variables are supposed to be used in our models?

Does Y equal the leading variable of this:

Dependent variable= IF (close_price_tomorrow / open_price_tomorrow) >= 1.01 THEN 1 ELSE 0

?

and

Are the independent variables or Xs the following

(1) Open

(2) High

(3) Low

(4) Close

(5) Volume

(6) Dependent variable= IF (close_price_tomorrow / open_price_tomorrow) >= 1.01 THEN 1 ELSE 0

?

The recent assignment about manually calculating the Naive Bayes and KNN was very helpful.

However, they only have 2 independent variables (tenure, spend)

and one dependent variable (churn).

The Bayes with many independent variables is easier to understand than the KNN with more

than 2 independent variables.

It is difficult to think about calculating dissimilarity based on the Pythagorean theorem

in 6 dimensions as opposed to 2 dimensions.

What independent and dependent variables should we use for predictions?

How do you suggest changing our thinking of Naive Bayes and KNN

with 6 independent variables instead of only 2?

## Answers and follow-up questions

** Answer or follow-up question 1** I figured it out.

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