How to Crack Passwords and Strengthen Your Credentials Against Brute-Force Lesson - 32Ī Look at ‘What Is Metasploitable’, a Hacker’s Playground Based on Ubuntu Virtual Machines Lesson - 33 Your Guide to What Is 5G and How It Works Lesson - 31 What Are Keyloggers and Its Effect on Our Devices? Lesson - 29īest Guide to Understand the Importance of What Is Subnetting Lesson - 30 The Best Guide to Understand What Is TCP/IP Model? Lesson - 28 The Value of Python in Ethical Hacking and a Password Cracking Tutorial Lesson - 27 Your Best Guide to a Successful Cyber Security Career Path Lesson - 26 What Is Google Dorking? Your Way to Becoming the Best Google Hacker Lesson - 25 What Is NMap? A Comprehensive Tutorial for Network Mapping Lesson - 24
The Best and Easiest Way to Understand What Is a VPN Lesson - 23 Kali Linux Lesson - 21Īll You Need to Know About Parrot Security OS Lesson - 22 Your Guide to Choose the Best Operating System Between Parrot OS vs. The Top 8 Cybersecurity Skills You Must Have Lesson - 20 What Is a Brute Force Attack and How to Protect Our Data Against It? Lesson - 19 The Best Guide to The Top Cybersecurity Interview Questions Lesson - 18 Top 6 Cyber Security Jobs in 2022 Lesson - 17 The Top Computer Hacks of All Time Lesson - 16 The Best Ethical Hacking + Cybersecurity Books Lesson - 14ġ0 Types of Cyber Attacks You Should Be Aware in 2022 Lesson - 15 The Most Informative Guide on What Is an IP Address? Lesson - 13 What Is a Ransomware Attack and How Can You Prevent It? Lesson - 11Ī Look at the Top 5 Programming Languages for Hacking Lesson - 12 What Is a Firewall and Why Is It Vital? Lesson - 8Ī Definitive Guide to Learn the SHA 256 Algorithm Lesson - 10 How to Become an Ethical Hacker? Lesson - 7 What Is SQL Injection: How to Prevent SQL Injection Lesson - 6 What is Penetration Testing?: A Step-by-Step Guide Lesson - 5 Having into consideration the dataset we are working with and the model we are going to use.How to Become a Cybersecurity Engineer? Lesson - 3 It is more important to know what coding scheme should we use. To summarize, encoding categorical data is an unavoidable part of the feature engineering. Although, a very efficient coding system, it has few issues responsible for deteriorating the model performance. We perform Target encoding for train data only and code the test data using results obtained from the training dataset. Train_df=pd.DataFrame()Įncoder.fit_transform(data,data) One major issue with this approach is that the algorithm might consider them as some order, or there is some relationship between classes even though there is no relation or order between these classes. In this encoding technique, each category is allocated a value from 1 through N (here N is the number of categories for the feature). The subsequent code helps how to install. There are several ways we can encode these categorical variables as numbers and use them in an algorithm.įor encoding categorical data, we have a python package called category_encoders. While encoding Nominal data, we have to think about the presence or absence of a feature. In Ordinal data, while encoding, one should ensure the encoding of variables retains the information regarding the order in which the category is provided.įew examples as below for Nominal variable: “Strongly agree,” Agree, Neutral, Disagree, and “Strongly Disagree.”.Nominal Data:The categories do not have any order.Ordinal Data:The categories have particular order.In this article, I will be discussing various types of categorical data encoding methods with implementation in Python.Ĭategorical variables can be divided into two categories: It not only elevates the model quality but also helps in better feature engineering. And converting categorical data is an unavoidable activity. Most of the Machine learning algorithms cannot handle categorical variables unless we convert them to numerical values.Ī typical data scientist spends 70 – 80% of his time cleaning and preparing the data. The performance of a machine learning model not only depends on the model and the hyperparameters but also on how we process and feed different types of variables to the model.
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