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Python 3
Data Encoding and Transformation

Data Encoding and Transformation

Lesson 4: Data Encoding and Transformation

Lesson 4: Data Encoding and Transformation

Machine learning models require numerical input. Since telecom datasets contain many categorical variables (such as contract type and payment method), these must be converted into numerical form before model training.

Binary Encoding: Used for yes/no type variables.

One-Hot Encoding: Used for multi-class categorical features to prevent false ordinal assumptions.

Why It Matters:

Ensures compatibility with machine learning algorithms.
Prevents the model from misinterpreting categorical values as ranked numbers.
Maintains fairness and accuracy in prediction.

Proper encoding ensures that categorical customer information contributes correctly to churn prediction.

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Data Encoding and Transformation

Contents

Lesson 4: Data Encoding and Transformation

Customer Churn Prediction Project Using Classification Techniques

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Computer Architecture

HTML 5 References Tutorial

Docker Tutorial

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GIT Guide

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Spring Framework Tutorial

Spring Boot Tutorial

Kotlin Tutorial

Apache Cordova Tutorial

Python Tutorial

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PL-SQL Tutorial

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