Covers: theory of Tabular Data Preprocessing
Estimated time needed to finish: 5 minutes
Questions this item addresses:
  • What is Normalization?
  • What is Standardization?
  • Normalize or Standardize?
  • How do we implement feature scaling in Python?
How to use this item?

Read the sections addressed in the questions below.

Author(s) / creator(s) / reference(s)
Aniruddha Bhandari
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Tabular Data Preprocessing For Continuous Data

Contributors
Total time needed: ~25 minutes
Objectives
You will learn two methods to preprocess continuous tabular data.
Potential Use Cases
Time-series forecasting, anomaly detection, power generation
Who is This For ?
BEGINNERData Scientists new to Tabular Data/Time-Series Preprocessing.
Click on each of the following annotated items to see details.
Resources5/5
VIDEO 1. Introduction to Feature Normalization on Continuous Tabular Data
  • Why use normalization or standardization?
  • When do we perform feature normalization? When don't we?
8 minutes
ARTICLE 2. Understanding the Difference Between Normalization & Standardization
  • What is Normalization?
  • What is Standardization?
  • Normalize or Standardize?
  • How do we implement feature scaling in Python?
5 minutes
VIDEO 3. Using Normalization to Reconcile Financial Data
  • What's a real-life example of using feature scaling?
4 minutes
LIBRARY 4. Preprocessing Data for Tabular Built-In Algorithms
  • How to preprocess tabular data?
6 minutes
LIVE_SESSION 5. AISC Tabular Data Preprocessing Presentation
  • Can you summarize this recipe?
2 minutes

Concepts Covered

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