Every data project requires clean-up and preparation for the models achieve the best expected outcome. These templates are used during the first modelling phase.

Remember, Garbage In - Garbage Out

Garbage In - Garbage Out

Machine Learning Process Diagram (Source:) TowardDataScience.com)


Steps involved in data preprocessing:

  1. Importing required libraries
  2. Importing the data set
  3. Handling the Missing Data.
  4. Encoding Categorical Data.
  5. Splitting the data set into test set and training set.
  6. Feature Scaling
Template (Direct Link) Language
Basic Data Preprocessing Python
Basic Data Preprocessing R
Categorical Data Python
Categorical Data R
Missing Data Python
Missing Data R


More about the templates here