I have built a classification model using the following steps (and in the mentioned order) in Python -
- Data cleaning - Removing unwanted variables and separating Predictor variables from response variable
- Label Encoding
- Standardization( StandardScaler)
- Train Test Split
- Model Building
- Model Testing using Test data
a) Now I have a new dataset, and I want to predict using the above built model. How do I do it ? Which of the above steps should I follow and which ones should I skip ? b) Also, is the arrangement of any of the steps aforementioned very, very wrong so that it needs to be changed ?