# Suggestion on Preprocessing dataset

I am trying to preprocess my dataset and needs some suggestion on it.

The training data shape is : (166573, 14)

The distribution of features :

As you can see, only the first 4 columns go to different max values. Rest of the columns have either 1 or 0 value (max: 1, min: 0)

**Null Handling: **

I have dropped claims_daysaway column as most of the values are NULL and replace tier's NaN values with its mean value.

Scaling features:

I have scaled features where max value varies and left others untouched.

X['scaled_distance']= sc.fit_transform(X['distance'].values.reshape(-1,1))
X['scaled_visit_count'] = sc.fit_transform(X['visit_count'].values.reshape(-1,1))
X['scaled_tier'] = sc.fit_transform(X['tier'].values.reshape(-1,1))


Is this right approach? or should I scale all features?

There are other techniques like logistic regression where scalling is optional because they work fine without scalling, the result is the same but sometimes you need to compare the resulting $$\beta$$'s and the only way to do this is if the $$X$$s have the same scale.