Below are the usual steps involved in building the ML pipeline:

  1. Import Data
  2. Exploratory Data Analysis (EDA)
  3. Missing Value Imputation
  4. Outlier Treatment
  5. Feature Engineering
  6. Model Building
  7. Feature Selection
  8. Model Interpretation
  9. Save the model
  10. Model Deployment *

Problem Statement and Getting the Data

I’m using a relatively bigger and more complicated data set to demonstrate the process. Refer to the Kaggle competition — IEEE-CIS Fraud Detection.

#ai #analytics #data-analysis #machine-learning #data-science

Building a Machine Learning Pipeline 
1.25 GEEK