In the machine learning workflow, what comes after data collection?
Data preprocessing
Explanation
A typical Machine Learning workflow follows these steps:
Data Collection
Data Preprocessing (cleaning, handling missing values, formatting data)
Feature Engineering (selecting or creating useful features)
Model Training
Model Evaluation
Model Deployment
After collecting the data, it must be cleaned and prepared before it can be used to train a model.
a) Model training – Happens after preprocessing and feature engineering.
b) Data preprocessing – This is the next step after data collection.
c) Feature engineering – Usually follows data preprocessing.
d) Model evaluation – Happens after the model has been trained.
Data preprocessing because, after data collection, this crucial step prepares the data for analysis by cleaning and transforming it. This ensures that your machine learning model can learn effectively from high-quality data, aligning perfectly with the learning objective of understanding the machine learning workflow.
