The assignment is VERY unclear on this page. We are told to use the formula from the top of the page, which, from the looks of it, should use the `nr_of_transactions`

and the `amount_spent`

columns, based on the formula AND the description. Why do we use the scaled columns in order to create the `score`

column?

Furthermore, the solution itself is unlike anything I’ve ever seen at DQ or anywhere else for that matter. Why are there backslashes `\`

ahead of the minus `-`

symbols in the scaling calculations? Are we escaping the minus symbols? I included the code from the solution below, but I would’ve expected something like this to work:

```
best_churn['scaled_tran'] = (best_churn['nr_of_transactions'] - best_churn['nr_of_transactions'].min()) / (best_churn['nr_of_transactions'].max() - best_churn['nr_of_transactions'].min())
```

Why doesn’t that work?

Screen Link: Learn data science with Python and R projects

```
best_churn["scaled_tran"] = (best_churn["nr_of_transactions"] \
- best_churn["nr_of_transactions"].min()) \
/ (best_churn["nr_of_transactions"].max() \
- best_churn["nr_of_transactions"].min())
best_churn["scaled_amount"] = (best_churn["amount_spent"] \
-best_churn["amount_spent"].min()) \
/ (best_churn["amount_spent"].max() \
- best_churn["amount_spent"].min())
best_churn["score"] = 100*(.5*best_churn["scaled_tran"] \
+ .5*best_churn["scaled_amount"])
best_churn.sort_values("score", inplace=True, ascending=False)
```