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    Implementation of The Data in Rapidminer

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    Ali
    This document discusses analyzing book data using RapidMiner. It describes the book data, which includes book titles, publisher names, and years from 2007 to 2020. The data is imported into RapidMiner for classification. Attribute types are identified for the data fields. A decision tree model is created using the decision tree operator to classify books by title, publisher, and year. The model's accuracy is evaluated using cross-validation and performance operators in RapidMiner.

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    Implementation of The Data in Rapidminer

    Uploaded by

    Ali
    36 views4 pages
    This document discusses analyzing book data using RapidMiner. It describes the book data, which includes book titles, publisher names, and years from 2007 to 2020. The data is imported into RapidMiner for classification. Attribute types are identified for the data fields. A decision tree model is created using the decision tree operator to classify books by title, publisher, and year. The model's accuracy is evaluated using cross-validation and performance operators in RapidMiner.

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    Original Title

    Analys And Discussion

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    This document discusses analyzing book data using RapidMiner. It describes the book data, which includes book titles, publisher names, and years from 2007 to 2020. The data is imported into RapidMiner for classification. Attribute types are identified for the data fields. A decision tree model is created using the decision tree operator to classify books by title, publisher, and year. The model's accuracy is evaluated using cross-validation and performance operators in RapidMiner.

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    36 views4 pages

    Implementation of The Data in Rapidminer

    Uploaded by

    Ali
    This document discusses analyzing book data using RapidMiner. It describes the book data, which includes book titles, publisher names, and years from 2007 to 2020. The data is imported into RapidMiner for classification. Attribute types are identified for the data fields. A decision tree model is created using the decision tree operator to classify books by title, publisher, and year. The model's accuracy is evaluated using cross-validation and performance operators in RapidMiner.

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    of 4

    Analys And Discussion

    Dataset
    This data in this study uses data from Kaggle entitled Book Data. This data
    consists of 460 pieces, with data on book titles, publisher names and publisher years.
    This data has attributes or features that describe each example.

    Fig 4.1 Data Book

    The following is a list of attributes in this study and their explanation:


    1.book titles
    2.publisher names
    3.publisher years (numeric from 2007 to 2020)

    Implementation of the data in RapidMiner

    To implement Classification in Rapid Miner, the first thing to do is import data.


    The data that must be imported is the training data used in Excel and the test data
    which contains all the data from the database, namely 460 data.

    When importing data, note that each table column is assigned the correct type
    attribute. If given the wrong attributes, the end result will also be wrong.

    In pattern recognition, information retrieval and classification, precision is the


    share of relevant samples among samples taken, while memory is the share of the
    total number of relevant samples actually retrieved. Both precision and recall are
    based on understanding and measuring the relevance of using a cross validation
    system to check accurately and also check for low probability.
    Fig 4.2 Cross Validation Book data

    There are several types of attributes that we will use, namely :

    a) Binominal: data type used for data types that only have two types, such as
    YES / NO, or 0/1.
    b) Polynominal: the type of data used for data types that have more than 2 types.
    c) REAL: used for data types that have a decimal in the number.
    d) Integer: used for data types that use numbers for data, without any decimals.

    Fig 4.3 Attributes Data Book

    For each data, the following are the types of data used for training data and testing
    data :
    1.Book Titles : Polynominal
    2.Publisher Names : Polynominal
    3.Publisher Years : Integer

    At the data book processing stage, the accuracy of each publisher, the name of the
    book, and the year of publication will be shown. where cross validation will show
    accurate results from the titles of books that are most interested in to those that are
    least in demand from year to year

    Modeling is a stage that directly involves data mining techniques, namely by


    selecting data mining techniques and determining the micro accurate.
    Fig 4.4 Performance Data Book Cross Validation

    Modeling is a stage that directly involves data mining techniques, using the decision
    tree technique to describe the book title from the year it was published, the name of
    the book and also where the book was published.

    Fig 4.5 The Form Decision Tree Data Book

    The decision tree will also specify the existing book data by describing how many
    books have been published from a publisher according to the book title listed.

    Fig 4.6 Spesification Decision Tree


    Process implementation using Rapid Miner
    Open Rapid Miner then in the repository table create a new repository then enter
    the data book. Select the retrieval operator then enter the book data into the retrieval
    operator in the fast miner design process area, then enter the decision tree operator
    into the field, connect the two operators, then enter the applied model and the
    performance operator into the design field and then enter the performance dataset
    back into the data testing then connect with the operator model apply, as shown in
    figure 4.7.

    Fig 4.7 Rapid Miner Operators

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