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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

What is Nominal Data?

Yana Khare
Yana Khare Last Updated : 02 Jul, 2024
3 min read

Introduction

Nominal data is one of the most fundamental types of data in data analysis. Identifying and interpreting it is essential in many fields, including statistics, computer science, psychology, and marketing. This article examines nominal data’s characteristics, applications, and differences from other data types.

Overview

  • Understand the fundamentals of nominal data, a key concept in categorizing and analyzing non-ordered variables.
  • Explore how it labels and categorizes variables without numerical or ordered values.
  • Learn about the distinguishing characteristics of nominal data.
  • Discover the diverse applications in fields.
  • Compare nominal and ordinal data to grasp the differences in their classification, order, and analytical techniques.
What is Nominal Data?

Table of Contents

What is Nominal Data?

Nominal data that only names variables without giving them a numerical value is categorical. Unlike ordinal data, it labels or categorizes variables with an underlying order. For instance, each category is unique and non-quantifiable when gathering data regarding preferred forms of transportation, such as bicycles, cars, buses, trains, trams, or trains.

Characteristics

  • Categorization without Order: It categorizes variables into discrete groups without suggesting a hierarchy or ranking among them. For example, the blood kinds (A, B, AB, O) are nominally classified since no innate order exists.
  • Non-numeric Labels: Names, labels, or codes are often used to denote these data types. These labels don’t indicate any numerical relationship; they are only descriptive.
  • Mutually Exclusive: There is no overlap between the categories. Each data piece is unique to its category. For example, a survey respondent’s gender is nominal. They can only fall into one group.
  • No Arithmetic Operations: Arithmetic procedures such as addition and subtraction do not apply to nominal data. This is because they lack a meaningful numerical value.

Applications of Nominal Data

Nominal data is utilized across various domains to categorize and analyze attributes that don’t have a natural order. Here are some common applications:

  • Market Research: To segment consumers based on their brand preferences or product choices.
  • Healthcare: Classifying patients by blood type or genetic traits.
  • Sociological Studies: Identifying demographic groups based on categories like religion or ethnicity.
  • Human Resources: Organizing employees by department or job role.

Nominal vs Ordinal Data

AspectNominal DataOrdinal Data
DefinitionClassifies variables without any order.Categorizes and ranks variables in a meaningful order.
OrderLacks inherent order.Has a clear ranking or order.
ExamplesEye color, gender, types of fruits.Education level, customer satisfaction, socio-economic status.
Analysis TechniquesFrequency counts, mode.Medians, ranges, and rank-based statistical methods.
Typical RepresentationCategorical labels.Ordered categories or ranks.
ScaleNon-numeric or categorical with no sequence.Ordered categories often assigned numeric values for rank.
Statistical OperationsLimited to counting and grouping.Allows for ordering and comparison, but not arithmetic operations.

How to Analyze Nominal Data?

Analyzing it typically involves summarizing the frequency of each category. Here are some common techniques:

  • Frequency Distribution: Counting the number of occurrences for each category.
  • Mode: Identifying the category with the highest frequency.
  • Contingency Tables: Used to examine the relationship between two nominal variables.
  • Visualization: Visual representation of nominal data often includes:
  • Bar Charts: Displaying the frequency of each category with bars.
  • Pie Charts: Illustrating the proportion of each category within a whole.

Conclusion

Nominal data is critical in organizing and interpreting categorical information across various fields. Understanding its characteristics and how to analyze it is essential for practical data analysis and decision-making. Whether in market research, healthcare, or sociology, it helps categorize the world around us, providing a foundation for further analysis and insight.

Frequently Asked Questions

Q1. What is an example of nominal data?

A. An example of nominal data is the type of pet owned: cat, dog, bird, or fish. Each category represents a distinct type without any order or ranking. It simply labels categories without implying any quantitative value or order.

Q2. Is 0 and 1 nominal data?

A. Yes, 0 and 1 can represent this data when used to label categories, such as gender (0 for male, 1 for female) or the presence/absence of a feature. In such cases, they function as labels without numerical meaning or inherent order.

Q3. Why is it nominal data?

A. 0 and 1 are nominal data when used as labels because they represent different categories without implying any numerical value or order. They are identifiers that distinguish between categories, such as gender or presence/absence, rather than representing quantities or rankings.

Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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