Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente |
dm:mains.santanna.dm4crm.2017 [18/05/2017 alle 18:13 (8 anni fa)] – [Calendar] Anna Monreale | dm:mains.santanna.dm4crm.2017 [23/05/2017 alle 15:30 (8 anni fa)] (versione attuale) – [Exercises] Anna Monreale |
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|10. | 22.05.2017 - 14:00-18:00 | Prediction models for promotion performance and churn analysis | {{:dm:5.dml-ml-crm-redemption-churn-promozioni-profili-innovatori.pptx.pdf| slides}} {{:dm:crm_dm-survey.pdf|Survey of DM applications in CRM}} {{:dm:change-customer-behavior.pdf|Mining changes in customer behavior in retail marketing}} | Giannotti, Guidotti | | |10. | 22.05.2017 - 14:00-18:00 | Prediction models for promotion performance and churn analysis | {{:dm:5.dml-ml-crm-redemption-churn-promozioni-profili-innovatori.pptx.pdf| slides}} {{:dm:crm_dm-survey.pdf|Survey of DM applications in CRM}} {{:dm:change-customer-behavior.pdf|Mining changes in customer behavior in retail marketing}} | Giannotti, Guidotti | |
|11. | 23.05.2017 - 09:00-13:00 | Mobility data mining & big data analytics | | Giannotti | | |11. | 23.05.2017 - 09:00-13:00 | Mobility data mining & big data analytics | | Giannotti | |
|12. | 23.05.2017 - 14:00-18:00 | Big Data Analytics: Privacy awareness | {{:dm:privacy-intro.pdf|Slides Privacy}}| Giannotti, Guidotti | | |12. | 23.05.2017 - 14:00-18:00 | Big Data Analytics: Privacy awareness | {{:dm:privacy-intro.pdf|Slides Privacy}} {{ :dm:06_class_mobility_mining.zip |}}| Giannotti, Guidotti | |
===== Datasets ===== | ===== Datasets ===== |
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**Guidelines:** | **Guidelines:** |
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Each group (2-3 people) is required to deliver a report (max 10 pages including all figures) describing the methods adopted and the discussion of the most interesting achieved results with reference to the tasks listed below. Assume that the report is targeted to a //marketing strategist//, who is interested to learn the story inferred in the various data mining analyses and to receive suggestions on how to take appropriate actions as a consequence. | Each group (2-3 people) is required to deliver a report (max 20 pages including all figures) describing the methods adopted and the discussion of the most interesting achieved results with reference to the tasks listed below. Assume that the report is targeted to a //marketing strategist//, who is interested to learn the story inferred in the various data mining analyses and to receive suggestions on how to take appropriate actions as a consequence. |
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**1. Data Understanding**: useful as a preliminary step to capture basic data property. Distribution analysis, statistical exploration, correlation analysis, suitable transformation of variables and elimination of redundant variables, management of missing values. | **1. Data Understanding**: useful as a preliminary step to capture basic data property. Distribution analysis, statistical exploration, correlation analysis, suitable transformation of variables and elimination of redundant variables, management of missing values. |
**4. Classification Analysis. ** Problem: find a high-quality decision tree for predicting a feature of a customer. The report should illustrate the adopted classification methodology and the decision tree validation and interpretation, describing also the process adopted to select the proposed tree, together with its quality evaluation. | **4. Classification Analysis. ** Problem: find a high-quality decision tree for predicting a feature of a customer. The report should illustrate the adopted classification methodology and the decision tree validation and interpretation, describing also the process adopted to select the proposed tree, together with its quality evaluation. |
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**Deadline**: send the report by email to all instructors within **4 July 2017**. Specify [MAINS] in the subject of the email. | **Deadline**: send the report by email to all instructors within **23 June 2017**. Specify [MAINS] in the subject of the email. |
====== Exams ====== | ====== Exams ====== |
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