I would like to get some suggestions about the best way to normalize my dataset to use as ML inputs.
My dataset looks like this:
-------------------------------------------------------------------------
| date | holiday | weekday | type | max_temp | min_temp | qty |
-------------------------------------------------------------------------
1 | 01/31/22 | 0 | tue | casual | 35.25 | 23.44 | 1,358 |
2 | 07/02/21 | 1 | mon | member | 34.33 | 7.29 | 1,358 |
3 | 03/12/20 | 0 | sat | casual | 12.21 | 2.18 | 1,358 |
...
n
I'm using Python to clean the data, and I intend to use this dataset to apply some linear regression, random forests, and XGBoost algorithm to predict the last column (qty).
Any suggestions for the best practice to prepare my data?