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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

Intro to Rapidminer: A No-Code Development Platform for Data Mining (with Case Study)

M
Muhammad Last Updated : 04 Oct, 2021
7 min read
This article was published as a part of the Data Science Blogathon 

Cover image of the article

Image 1

What is data mining?

Data mining is the process of finding interesting patterns and knowledge from large amounts of data. Data sources include databases, data warehouses, web, and other information repositories or data that is flowed into the system dynamically. This analysis is carried out for the decision-making process.

Data mining is used to process data that initially has no meaning into information and then the information becomes knowledge. Data Mining, also known as Knowledge-Discovery-in-Databases.

Several methods or data mining tasks can be used to find, analyze, explore, and mine knowledge. There are 5 main tasks (Larose, 2005): Estimation, Forecasting, Classification, Clustering, and Association.

What are No-code development platforms?

Python and R are the most popular programming languages for data mining at the moment. But if you are short on time or not really familiar with Python, you can utilize the no-code development platforms.

A no-code development platform enables us to perform data mining tasks with drag-and-drop. It also enables us to develop data mining projects quickly without coding. Developers and non-developers can use these tools to practice rapid data mining development with customized workflows and functionality.

What is Rapidminer?

Rapidminer is a comprehensive data science platform with visual workflow design and full automation. It means that we don’t have to do the coding for data mining tasks. Rapidminer is one of the most popular data science tools.

This is the graphical user interface of the blank process in rapidminer. It has the repository that holds our dataset. We can import our own datasets. It also offers many public datasets that we can try. We can also work with a database connection.

Below the repository window, it has an operator. The operators include everything we need to build a data mining process, such as data access, data cleansing, modeling, validation, and scoring.

On the right is a parameters window. The parameters window is to adjust the operators.

GUI of Rapidminer

Rapidminer GUI

Rapidminer can be downloaded from their official website(https://rapidminer.com/). It has a free version with limited functionality. The free version includes 10,000 data rows and 1 Logical Processor.

They also offer an educational program. So that students, professors, instructors, and researchers can have a free educational license for free.

Case Study on Rapidminer

Let’s go to practice with Rapidminer. In this study case, we will perform a data mining process using the built-in dataset, using the classification method to compare accuracy from the various algorithms.

Activity Selection

This is the first interface that will pop up when we launch the rapidminer application. The blank process is to build from scratch. It works by dragging-and-dropping operators to the process field manually.  This is the menu that you want to choose if you are at the intermediate level with this application.

Turbo Prep is for dataset preparation only. It includes transform, cleaning, and combines datasets.  Auto Model will bring us the wizard to perform data mining tasks. Just like installing an application on Windows. Next-Next and Finish.

It also has a lot of templates for us to start with. We will choose the Auto Model for this study case.

Activity Selection Window in Rapidminer

Importing Dataset

Here we can choose the dataset that we will use. We can import our own dataset or select from the available dataset provided by rapidminer. Import-New-Data button below the select data list is to import our own dataset.

To use the available dataset from Rapidminer, click on the samples folder, then expand the data folder, and let’s choose the Titanic dataset for our study case and click the green Next button.

Notice in the progress bar there are only six easy steps to perform a data mining task with rapidminer.

Importing dataset in Rapidminer

Datamining Method Selection

The details from a chosen dataset will be displayed. The titanic is a dataset to predict whether the passenger will survive on the titanic ship from the available input parameters. There are eleven input (x) parameters and one label (y) from this dataset.

There are three actions that we can choose for our dataset. Predict, clusters and outliers. The outliers button will help us detect outliers in our data. Clusters will help us detect common groups in our data. Predict will classify the data from the given input parameter.

Here we can observe our dataset’s input parameter. We can see that the Titanic dataset consists of both categorical and numerical data. The target label is in categorical, yes, or no format.

Select the predict button to do classification, select the Survived column as a label or classification target, and click on the Next button.

Data Mining Method Selection in Rapidminer

Data Balance

After selecting the data mining method and selecting the target column, we will be served with the data balance in a chart. See that the No data is more than the Yes data. This condition is quite common in reality. The ratio is around 60:40 which is still acceptable.

We need to start to worry when the ratio is higher than 70:30. A highly imbalanced class will lead to imbalanced prediction. The classification is usually tended predicted to the majority class.

Checking the Target class distribution

Input Selection

In this section, we can exclude the columns from the input parameters. The default is all column is included. Rapidminer will recommend which columns should be included or excluded.

Notice that the first three rows are excluded by default. This happens because the status is red. The red status will automatically be excluded by rapidminer though we still can include it. You can hover the red circle in the status column to see the details.

Input Selection in Rapidminer

The Quality column will help us make a decision. It consists of five important parameters CISMT.

  • Correlation (C) : measures the linear correlation between the data column and the target column.
  • ID-ness (I) : measures the likelihood of the column resemble an ID.
  • Stabilty (S) : Indicates that nearly all values are identical.
  • Missing (M) : measures how much missing value is in the column.
  • Text-ness (T) : measures the likelihood of the column resemble free text.
Checking the status of Quality column

Algorithm Selection

Here’s the algorithm selection. Rapidminer will serve several popular classification algorithms for us to choose from.

This is the list of algorithms you can choose:

  1. Naive Bayes
  2. Generalized Linear Model
  3. Logistic Regression
  4. Fast Large Margin
  5. Deep Learning
  6. Decision Tree
  7. Random Forest
  8. Gradient Boosted Trees
  9. Support Vector Machine

We can choose all of them If the dataset being used is small. But we need to be wise when using a large dataset. Because the more algorithm being selected, the more time and hardware resources will be needed.

After choosing them, click on the run button.

Selecting the Algorithm in Rapidminer

 

Getting Insights from the Result

Depending on how many algorithms have been chosen, it will take longer to process. After we’re waiting for a while, the results will be served. The results will appear as a table and charts.

The first chart will show us the classification error comparison. Here we can see from our Titanic data, DT and SVM show the worst performance. The smaller the better for the charts. The second chart will shows us the runtimes comparison. The random forest algorithm takes the longest runtime.

While the charts give us a quick insight, the table will provide the details. The model name column also has badges. See the Deep learning, it has two badges. The badges show that deep learning gets the best overall performance and the best low-cost computation.

Getting insights from the result in Rapidminer

 

Exporting Result

Imagine if we have to prepare our data manually, and create the classification code with a deep learning algorithm. That one itself must be taken so many hours to code. It’s only one algorithm, how about coding all of them and create the visualizations.

In just ten minutes we have already finished our data mining process without the fuss to code them from the ground up. All we have to do is to click the Next button and finish.

We also can save the result in various formats. Excel is one of them. Click the export button from the previous dialog and just click on the desired format and finished.

Exporting the result in Rapidminer

Conclusion

  • No-code development platforms can greatly simplify data mining works
  • Rapidminer is one of the tools that are effective for data mining tasks and safe a lot of times.
  • Rapidminer also includes data pre-processing and algorithms selection
  • At the end of the task, rapidminer will serve visualizations for us to get an insight.
  • All the tasks done in rapidminer are so effortless compared to manual coding.

What’s next

  • Build a data mining model with different datasets
  • Build a data mining model with your own dataset
  • Try to use another method, clustering

Short Author Bio

My name is Muhammad Arnaldo, a machine learning and data science enthusiast. Currently a master’s student of computer science in Indonesia.

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

blogathondata miningRapidMiner
M
Muhammad

My name is Muhammad Arnaldo, a machine learning and data science enthusiast. Currently a master’s student of computer science in Indonesia.

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Simplifying Data Preparation and Machine Learning Tasks using RapidMiner
Simplifying Data Preparation and Machine Learning Tasks using RapidMiner
Data Mining: The Knowledge Discovery of Data
Data Mining: The Knowledge Discovery of Data

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