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Python 3
Handling Missing Values

Handling Missing Values

Lesson 2: Handling Missing Values

Lesson 2: Handling Missing Values

Handling missing values is a crucial part of data cleaning. Missing values can cause errors in calculations or misleading trends if ignored.

In this project, instead of deleting rows with missing data, meaningful placeholder values are used. This approach preserves the full dataset while clearly marking unknown information.

Data Preservation: Keeps all sales records intact without dropping rows.
Meaningful Defaults: Uses clear placeholders like “Unknown” or “Not Assigned.”
Smooth Data Processing: Prevents issues during aggregation and visualization.

This method ensures the dataset remains complete and analysis-ready.

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Handling Missing Values

Contents

Lesson 2: Handling Missing Values

Sales Data Analysis Project for Beginners Using Data Science

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Java Tutorial

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PHP Tutorial

Android Tutorial

Linux Guide Tutorial

Data Structures and Algorithms Tutorial

Computer Architecture

HTML 5 References Tutorial

Docker Tutorial

GIT (Using Github) Tutorial

Go Language Tutorial

GIT Guide

CSS Tutorial

Advanced Data Structures

Spring Framework Tutorial

Spring Boot Tutorial

Kotlin Tutorial

Apache Cordova Tutorial

Python Tutorial

SASS-SCSS Tutorial

MongoDB Tutorial

Numpy Tutorial

PL-SQL Tutorial

Python Built-in Functions Tutorial

Pandas Tutorial

Elasticsearch Tutorial

Matplotlib Tutorial

Web Scraping Tutorial

Networking with Python

Tkinter Tutorial

Java Programs Tutorial

Java Examples Tutorial

Servlet Tutorial

JSP Tutorial

Java Type Conversion Tutorial

Java 8 Tutorial

Java 9 Tutorial

Java 10 Tutorial

Java 11 Tutorial

Java Util Library Tutorial

Building a Contact Us Form in ReactJS

Building a Age Calculator App Using ReactJS

Movie Recommendation System Project Using Content-Based Filtering

Recipe Finder App using ReactJS

Student Performance Analysis Project for Beginners Using Data Science

Word Counter Tool Using ReactJS