I have Automobile Dataset i want to predict how?

atifraza127atifraza127 MemberPosts:1Learner I
edited November 2018 inHelp

My data is attached in excel file. I want to predict this file. what prediction method I used.

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Answers

  • sgenzersgenzer Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM ModeratorPosts:2,959Community Manager

    hello@atifraza127- welcome to the community. Have you gone through the tutorials on how to do predictive analyics? You'll find lots of information here:https://community.www.turtlecreekpls.com/t5/Getting-Started-Forum/bd-p/GettingStartForum

    Scott

  • lionelderkrikorlionelderkrikor Moderator, RapidMiner Certified Analyst, MemberPosts:1,195Unicorn

    HI@atifraza127,

    Once you have read the resources to get started and see the tutorials :

    The fist step, is to build classifier model(s), which make the relationship(s) between your label attribute (in your caseChance of Stolen, I suppose) and the other attributes, from your training dataset (your file). Then you have to choose the model which has the best performances (accuracy, recall, precision etc.).

    To perform these tasks, you can find here a process to compare the performances of 5 models.

    NB : Don't hesitate to test the different classifier models proposed by RapidMiner :







    <运营商激活= " true " class = "过程”兼容ibility="8.0.001" expanded="true" name="Process">

    <运营商激活= " true "class="read_csv" compatibility="8.0.001" expanded="true" height="68" name="Read CSV" width="90" x="45" y="34">





















    <运营商激活= " true "class="set_role" compatibility="8.0.001" expanded="true" height="82" name="Set Role" width="90" x="179" y="34">




    <运营商激活= " true "class="loop" compatibility="8.0.001" expanded="true" height="82" name="Loop" width="90" x="313" y="34">



    <运营商激活= " true "class="concurrency:cross_validation" compatibility="8.0.001" expanded="true" height="145" name="Cross Validation" width="90" x="112" y="34">

    <运营商激活= " true "class="select_subprocess" compatibility="8.0.001" expanded="true" height="82" name="Select Subprocess" width="90" x="179" y="34">


    <运营商激活= " true "class="concurrency:parallel_decision_tree" compatibility="8.0.001" expanded="true" height="103" name="Decision Tree" width="90" x="179" y="34"/>








    <运营商激活= " true "class="concurrency:parallel_random_forest" compatibility="8.0.001" expanded="true" height="103" name="Random Forest" width="90" x="112" y="34"/>








    <运营商激活= " true "class="h2o:gradient_boosted_trees" compatibility="7.6.001" expanded="true" height="103" name="Gradient Boosted Trees" width="90" x="45" y="34">










    <运营商激活= " true "class="naive_bayes" compatibility="8.0.001" expanded="true" height="82" name="Naive Bayes" width="90" x="45" y="34"/>








    <运营商激活= " true "class="h2o:deep_learning" compatibility="7.6.001" expanded="true" height="82" name="Deep Learning" width="90" x="45" y="34">























    <运营商激活= " true "class="apply_model" compatibility="8.0.001" expanded="true" height="82" name="Apply Model" width="90" x="112" y="34">


    <运营商激活= " true "class="performance_classification" compatibility="8.0.001" expanded="true" height="82" name="Performance" width="90" x="246" y="34">














    <运营商激活= " true "class="log" compatibility="8.0.001" expanded="true" height="82" name="Log (3)" width="90" x="313" y="30">

























    Once you have determined the best model, you can apply it to a score dataset to predict the label attribute.

    I hope it will be helpful,

    Regards,

    Lionel

    sgenzer
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