ML Data Preprocessing MCQ Test
Test how well you understand cleaning, transforming and preparing data before feeding it into Machine Learning models.
Data Preprocessing for Machine Learning: MCQ Practice
Data preprocessing is often the most time‑consuming and important part of a Machine Learning project. These MCQs focus on missing values, encoding, feature scaling and data leakage so you can validate your understanding of the full preprocessing workflow.
Why Preprocessing Matters
Even the best algorithm will fail if the data is dirty. Good preprocessing improves model stability, speed and accuracy.
Key Preprocessing Concepts Covered
Handling Missing Data
Mean/median imputation, dropping rows, and advanced techniques like KNN imputer.
Encoding Categorical Features
One‑hot encoding, label encoding, and when each is appropriate.
Feature Scaling
Normalization vs standardization and why scaling matters for distance‑based models.
Data Leakage
How to avoid leaking test information into training through improper preprocessing.
Typical Preprocessing Workflow
Raw Data → Cleaning & Imputation → Encoding → Scaling → Train/Test Split → Model