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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
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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
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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
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8 Ways to Improve Accuracy of Machine Learning Models
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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

Data Visualisation: Alluvial Diagram vs. Sankey Diagram

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lori6905752 Last Updated : 22 Jun, 2022
4 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Most people tend to have a very minimal set of information about Alluvial and Sankey diagrams. The reality is that the two diagrams have superficial similarities to each other. On the flip side, the two diagrams are completely different, underlying superficial assumptions. Let’s discuss these two diagrams briefly in this article!

Data Visualisation

What is an Alluvial Diagram?

This type of flow diagram represents different forms of changes that made the flow of material, energy, cost, or measurable resource in a certain time-bound. Considering the physical appearance of Alluvial Diagrams and their contribution to the flow, the diagram is named immediately after the alluvial fans.

Note that the fans are formed in a natural way when soil is deposited from streaming water. On the Alluvial Diagram, variables are calibrated on the vertical axis, which is always parallel. Besides, there are values represented with blocks on every axis. The height of each block is meant to represent the height of the streamflow and the size of the container.

The field represents the size of the element within the block and is connected to the stream field directly. Normally, the Alluvial diagram is considered an alternate form of parallel sets although used as categorical variables. It is mainly used to determine trends and time in the long run.

What is a Sankey Diagram?

A Sankey diagram is a form of visualization responsible for depicting a given flow from one set to another. The things that are connected are named nodes, and the connection between these nodes are called links. Sankey Diagrams are mainly used, especially when you want to showcase many-to-many mapping between different domains.

Google Analytics mainly adopts this mechanism to showcase how traffic flows from one page to the other pages of your website. Sankey Diagrams were named after Captain Sankey, who invented the diagram of steam engine efficiency.

Common Features that Lead to Confusion in all the Diagrams

In Sankey and Alluvial diagrams, different features make the two confusing. In the two diagrams, nodes are represented by bars that are placed vertically with their height, delivering the idea of volume or count.

The links applied between the nodes consist of curves that have thickness related to the height of the nodes that encodes the count or volume just the same way the node height operates. Also, in all the diagrams, the user needs to highlight a stripe across the diagram. This is to help in identifying how wide the stripe is at different points.

Differences between Sankey Diagrams and Alluvial Diagrams

Alluvial diagrams mainly focus on showcasing quantities’ appearance from one state to another throughout different processes. Alternatively, a Sankey Diagram is a streamlined flow chart that can easily visualize quantitative values at every phase of the whole process.

Alluvial and Sankey diagrams have a noteworthy difference considering the consistency of the nodes on the lines. Sankey diagrams have line sets that are uniform in length & analytics while Alluvial diagrams have a lot of variations.

Alluvial Diagrams are mainly applied in multi-dimensional data analysis cases. Emphasis mainly focuses on the frequencies and proportions between different dimensions and how they relate to each other.

Sankey Diagrams are applied in cases that require quantity visualization between various stages of the entire process. Note that a Sankey Diagram can visualize the quantity of the incoming and outgoing parts of the flow. This helps locate the parts with dominant contribution and the places where the quantity gets lost within the process.

Tools Used to Make Sankey Diagrams

Google Charts

The Google charts tools are easy and simple to use when making Sankey diagrams. You can opt to use attractive charts and data tools. Note that the process of making Sankey Diagrams using Google charts is easily customizable.

Highcharts

This mechanism was instigated by Viki Sogn, based in Norway; Highcharts allows its users to make Sankey Diagrams online. You can edit your code as well whenever the need arises.

RAWGraphs

This is a free tool used for creating Sankey Diagrams by using the drag-and-drop option. You just have to drag and drop your data on the website or import it online.

Tools Used to Make Alluvial Diagrams

DisplayR

The DisplayR is an online tool primarily meant for making Alluvial Diagrams. It comes with a bunch of customization options that give users an added advantage when using them. Besides, the tool’s free version does not allow the user to download the final output of the diagram.

The Alluvial Diagram Generator

This is an automated online tool that requires a web-based application to help you generate Alluvial Diagrams. It gives you a chance to upload your data and generate the final output of your desire. You can easily download the final output or share it using an email.

Conclusion

Alluvial Diagram vs. Sankey Diagram has many things in common. Despite the common features between the two, they are not similar if we review the internal features. They need a deep observation of the physical attributes and their useful capabilities as well. This will help you identify the remarkable similarities and differences between the two.

FAQs

What are Alluvial plots?

Alluvial plots are considered a form of Sankey Diagrams that are a notable tool for exploring categorical data. They play a major role in grouping categorical roles into the flows that can be easily tracked and analyzed by the diagram.

What is a Sankey Diagram good for?

It is no secret that Sankey Diagrams are applied in different scenarios daily. The diagrams are mainly applied in the visualization of material, energy flow, and cost. Alternatively, they showcase energy or mass flows in the form of arrows proportional to the size of the flow.

How do you create an Alluvial Diagram?

● Download the dataset and the questionnaire responses from Dropbox.

● Click and open the RAW.

● Drag your preferred data into the box.

● Choose the Alluvial Diagram from the list.

● Opt for various dimensions.

● Increase the width of the visualization.

● Amped the visualization.

What is an Alluvial structure?

The alluvial diagram is a type of flow chart that represents changes that are made in the flow of material, energy, cost, or measurable resource over time. In that sense, it helps identify patterns and trends. It is named after the alluvial fan, a flow of water extending over a surface that becomes flat, so it refers to both its shape and flow.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

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Can I build an alluvial diagram in which not all bands have the same number of nodes?

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