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0 a0u5g formation-ibm-spss-modeler-v16-segmentation-clients
This one-day (7 hour) course from CERTYOU provides an intermediate level introduction to using IBM SPSS Modeler to classify customers. The course will cover rule-based models like CHAID and C&R Tree, traditional statistical models like Discriminant and Logistic regression, and machine learning models like Neural Networks. Students will learn techniques for predicting customer behaviors like churn, response to marketing, or machine part failures. The course objectives are to understand model selection, build and customize models in SPSS Modeler, and evaluate model accuracy. Prerequisites include introductory SPSS Modeler training.
0 a0u5g formation-ibm-spss-modeler-v16-segmentation-clients
- 1. CERTYOU, 37 ruedes Mathurins, 75008 PARIS - SAS au capital de 10 000 Euros Tél : 01 42 93 52 72 - Fax : 01 70 72 02 72 - contact@certyou.com - www.certyou.com RCS de Paris n° 804 509 461- TVA intracommunautaire FR03 804509461 - APE 8559A Déclaration d’activité enregistrée sous le N° 11 75 52524 75 auprès du préfet de région d’Ile-de-France IBM SPSS Modeler v16 : Segmentation clients Formation Informatique / SGBD et Aide à la décision / Analyse décisionnelle Classifying Customers Using IBM SPSS Modeler (V16) is an intermediate level course that provides an overview of how to use IBM SPSS Modeler to predict the category to which a customer belongs. Students will be exposed to rule induction models such as CHAID and C and R Tree. They will also be introduced to traditional statistical models and machine learning models. Business use case examples include: predict whether a customer switches to another provider/brand and whether a customer responds to a particular advertising campaign. Although this course focuses on classifying customers (including students, patients, employees, and so forth), the techniques can also be applied to business questions such as predicting breakdown of machine parts. OBJECTIFS • Explain the concept of field measurement level and its implications for selecting a modeling technique • Determine the classification model to use • Explain how CHAID grows a tree • Customize two options in the CHAID node • Using Traditional Statistical Models • Explain key concepts for Neural Net • Customize one option in the Neural Net node PUBLIC This intermediate course is for IBM SPSS Modeler Analysts who have completed the Introduction to IBM SPSS Modeler and Data Mining course who want to become familiar with the modeling techniques used to classify customers in IBM SPSS Modeler. This includes data analysts and analytics business users. PRE-REQUIS You should have: Experience using IBM SPSS Modeler, including familiarity with the IBM SPSS Modeler environment, creating streams, importing data (Var. File node), basic data preparation (Type node, Derive node, Select node), reporting (Table node, Data Audit node), and creation of models. Introduction to IBM SPSS Modeler and Data Mining (V16) PROGRAMME Introduction to Classifying Customers List three modeling objectives List two business questions that involve classifying customers Explain the concept of field measurement level and its implications for selecting a modeling technique List three types of models to classify customers Determine the classification model to use Building Your Tree Interactively with CHAID Explain how CHAID grows a tree Build a customized model using CHAID Evaluate a CHAID model by means of accuracy, risk, response and gain Use the model nugget to score records Building Your Tree Interactively with C&R Tree and Quest Explain how C and R Tree grows a tree Explain how Quest grows a tree Build a model interactively using C and R Tree and Quest List two differences between CHAID, C and R Tree, and Quest Building Your Tree Directly Customize two options in the CHAID node Customize two options in the C and R Tree node Customize two options in the Quest node Customize two options in the C5.0 node Use the Analysis node and the Evaluation node to evaluate and compare models List two differences between CHAID, C and R Tree, Quest, and C5.0 Using Traditional Statistical Models Explain key concepts for Discriminant Customize one option in the Discriminant node Explain key concepts for Logistic Customize one option in the Logistic node List two differences between Discriminant and Logistic Using Machine Learning Models A retenir Durée : 1 jour soit 7h. Réf. 0A0U5G Dates des sessions Cette formation est également proposée en formule INTRA-ENTREPRISE. Inclus dans cette formation Coaching Après-COURS Pendant 30 jours, votre formateur sera disponible pour vous aider. CERTyou s'engage dans la réalisation de vos objectifs. Votre garantie 100% SATISFACTION Notre engagement 100% satisfaction vous garantit la plus grande qualité de formation.
- 2. CERTYOU, 37 ruedes Mathurins, 75008 PARIS - SAS au capital de 10 000 Euros Tél : 01 42 93 52 72 - Fax : 01 70 72 02 72 - contact@certyou.com - www.certyou.com RCS de Paris n° 804 509 461- TVA intracommunautaire FR03 804509461 - APE 8559A Déclaration d’activité enregistrée sous le N° 11 75 52524 75 auprès du préfet de région d’Ile-de-France IBM SPSS Modeler v16 : Segmentation clients Formation Informatique / SGBD et Aide à la décision / Analyse décisionnelle Explain key concepts for Neural Net Customize one option in the Neural Net node