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Data Mining with RapidMiner
Rating: 3.1 out of 5(39 ratings)
669 students

Data Mining with RapidMiner

Data Mining with RapidMiner
Created byGoh Ming Hui
Last updated 3/2019
English

What you'll learn

  • Data Mining using RapidMIner

Course content

1 section • 39 lectures • 1h 22m total length

  • Getting Started2:36

    Begin by visiting the official RapidMiner website to download the software, provide an email as required, download the Windows installer, and complete the installation to start data mining with RapidMiner.

  • Getting Started0:19
  • Data Mining Process5:37
  • Download Dataset1:11

    Download datasets for statistical learning in data mining with RapidMiner, review the iris dataset, and search online to locate high risk datasets for analysis.

  • Read CSV0:49
  • Data Understanding - Statistics0:45
  • Data Understanding - Scatterplot0:55
  • Data Understanding - Line0:44
  • Data Understanding - Bar0:37
  • Data Understanding - Histogram0:46
  • Data Understanding - Boxplot0:32
  • Data Understanding - Pie0:40

    Explore data visualization by creating a pie chart in RapidMiner, adjust chart parameters and legend, and plug the pie chart into RapidMiner Studio.

  • Data Understanding - Scatterplot Matrix0:42
  • Data Preparation - Normalization1:41

    Explore data preparation with RapidMiner by applying normalization to Iris data, examining all four features, and generating normalized results.

  • Data Preparation - Replace Missing Values1:14
  • Data Preparation - Remove Duplicates1:08
  • Data Preparation - Detect Outlier0:46
  • Modeling: Simple Linear Regressions3:07
  • Modeling: Simple Linear Regressions (RapidMIner)4:21
  • Modeling: KMeans Clustering3:05

    Learn how the k-means clustering algorithm groups data into k clusters by assigning each point to the nearest centroid, recomputing centroids as means, and repeating until clusters stabilize.

  • Modeling: KMeans Clustering using RapidmIner1:51
  • MOdeling: Agglomeration CLustering3:45
  • MOdeling: Agglomeration CLustering using RapidMIner1:07

    Learn how to perform agglomerative clustering using RapidMiner, building hierarchical clusters and evaluating clustering outcomes.

  • MOdeling: Decision Tree ID3 Algorithm9:15
  • MOdeling: Decision Tree ID3 Algorithm using RapidMIner2:19

    Explore building a decision tree with the ID3 algorithm in RapidMiner, selecting attributes, assigning labels, and visualizing the resulting diagram with a 70/30 data split.

  • MOdeling: Decison Tree ID3 Algorithm using RapidMiner0:33
  • Evaluation: Decison Tree ID3 Algorithm using Rapidminer0:42
  • Modeling: KNN Classification3:50
  • Modeling: KNN Classification using RapidmIner1:06

    Explore modeling with KNN classification using RapidMiner, comparing it to decision trees and CNN classification while preparing datasets and running predictions.

  • Evaluation: KNN Classification using RapidMIner0:44

    Explore the evaluation of knn classification using RapidMiner, focusing on model validation, performance assessment, and interpreting output results.

  • Modeling: Naive Bayes Classification5:36
  • Modeling: Naive Bayes Classification using RapidmIner0:58
  • Evaluation: Naive Bayes Classification using RapidmIner1:10
  • MOdeling: Neural Network5:44
  • Modeling: Neural Network using Rapidminer0:59

    Learn how to build and run a neural network classification in RapidMiner, from selecting the neural network operator and feeding data to setting parameters and obtaining predictions.

  • Evaluation: Neural Network using RapidmIner1:20

    Explore evaluating a neural network model in RapidMiner by validating predictions, examining performance classifications, and generating a conversion matrix to assess results.

  • What ALgorithm to Use?1:35
  • Model Evaluation3:45
  • Evaluation: K Fold Cross Validation4:32

Requirements

  • Basic COmputer Knowledge

Description

Why learn Data Analysis and Data Science?


According to SAS, the five reasons are


1. Gain problem solving skills

The ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life.


2. High demand

Data Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase.


3. Analytics is everywhere

Data is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.


4. It's only becoming more important

With the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities.


5. A range of related skills

The great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths.  Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.


The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities.

This is the bite-size course to learn Data Mining using RapidmIner. This course uses CRISP-DM data mining process.

You will learn RapidMiner to do data understanding, data preparation, modeling, and Evaluation. You will be able to train your own prediction models with Naive Bayes, decision tree, knn, neural network, and linear regression, and evaluate your models very soon after learning the course.


You can take the course as following and you can take an exam at EMHAcademy to get SVBook Advance Certificate in Data Science using DSTK, Excel, and RapidMiner:

- Introduction to Data and Text Mining using DSTK 3

- Data Mining with RapidMiner

- Learn Microsoft Excel Basics Fast

- Learn Data analysis using Microsoft Excel Basics Fast.


Content

  1. Getting Started

  2. Getting Started 2

  3. Data Mining Process

  4. Download Data Set

  5. Read CSV

  6. Data Understanding: Statistics

  7. Data Understanding: Scatterplot

  8. Data Understanding: Line

  9. Data Understanding: Bar

  10. Data Understanding: Histogram

  11. Data Understanding: BoxPLot

  12. Data Understanding: Pie

  13. Data Understanding: Scatterplot Matrix

  14. Data Preparation: Normalization

  15. Data Preparation: Replace Missing Values

  16. Data Preparation: Remove Duplicates

  17. Data Preparation: Detect Outlier

  18. Modeling: Simple Linear Regression

  19. Modeling: Simple Linear Regression using RapidMiner

  20. Modeling: KMeans CLustering

  21. Modeling: KMeans Clustering using RapidmIner

  22. Modeling: Agglomeration CLustering

  23. Modeling: Agglomeration Clustering using RapidmIner

  24. Modeling: Decision Tree ID3 Algorithm

  25. Modeling: Decision Tree ID3 Algorithm using RapdimIner

  26. Modeling: Decision Tree ID3 Algorithm using RapidMiner

  27. Evaluation: Decision Tree ID3 Algorithm using RapidmIner

  28. Modeling: KNN Classification

  29. Modeling: KNN CLassification using RapidmIner

  30. Evaluation: KNN Classification using RapidmIner

  31. Modeling Naive Bayes Classification

  32. Modeling: Naive Bayes Classification using RapidmIner

  33. Evaluation: Naive Bayes Classification using RapidMIner

  34. Modeling: Neural Network Classification

  35. Modeling: Neural Network Classification using RapidmIner

  36. Evaluation: Neural Network Classification using RapidmIner

  37. What Algorithm to USe?

  38. Model Evaluation

  39. k fold cross-validation using RapdimIner

Who this course is for:

  • Beginner Data Scientist or Analyst interested in RapidMiner

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