How to Use Data Transformation Techniques in Exploratory Data Analysis

Exploratory Data Analysis (EDA) is a crucial step in the data science workflow, allowing analysts to investigate datasets, summarize their main characteristics, and uncover patterns or anomalies. One of the essential components of effective EDA is data transformation. Data transformation techniques modify data into a more suitable format for analysis, making patterns more evident and enhancing model performance.

This article delves into various data transformation techniques used in EDA and explains how and when to use them effectively.

Importance of Data Transformation in EDA

Before delving into specific methods, it’s important to understand why data transformation is necessary in EDA:

• Improves data interpretability

• Enhances visualization effectiveness

• Reduces skewness and stabilizes variance

• Handles outliers and missing values

• Prepares data for modeling

With these goals in mind, let’s explore the major categories and techniques of data transformation used during EDA.

1. Scaling and Normalization

Scaling adjusts the range of data while normalization transforms the data distribution.

a. Min-Max Scaling

This technique rescales features to a fixed range, usually [0, 1].

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