| Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente |
| dm:mains.santanna.dm4crm.2019 [09/04/2019 alle 22:02 (6 anni fa)] – [Calendar] Fosca Giannotti | dm:mains.santanna.dm4crm.2019 [17/04/2019 alle 21:57 (6 anni fa)] (versione attuale) – Fosca Giannotti |
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| ====== Data Mining for Customer Relationship Management 2019 ====== | ====== Data Mining and Machine Learning -- Master MAINS 2019 ====== |
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| * **Fosca Giannotti**\\ ISTI-CNR, Knowledge Discovery and Data Mining Lab\\ [[[email protected]]] | * **Fosca Giannotti**\\ ISTI-CNR, Knowledge Discovery and Data Mining Lab\\ [[[email protected]]] |
| |10. | Mer 17.04.2019 - 14:00-18:00 | Pattern and association rule mining: exercises with Knime |{{:dm:03_titanic_pattern.zip | 03_titanic_pattern}} {{ :dm:04_coop_pattern.zip | 04_coop_pattern}} | Giannotti, Rossetti | | |10. | Mer 17.04.2019 - 14:00-18:00 | Pattern and association rule mining: exercises with Knime |{{:dm:03_titanic_pattern.zip | 03_titanic_pattern}} {{ :dm:04_coop_pattern.zip | 04_coop_pattern}} | Giannotti, Rossetti | |
| |11. | Gio 18.04.2019 - 09:00-13:00 | Case studies. Prediction models for promotion performance and churn analysis | {{:dm:5.dml-ml-exemplarproject-churn-fraude-.pdf |}} {{:dm:5.dm_ml_exemplarprojects-shoppingbehaviour_innovators.pdf |}} | Giannotti | | |11. | Gio 18.04.2019 - 09:00-13:00 | Case studies. Prediction models for promotion performance and churn analysis | {{:dm:5.dml-ml-exemplarproject-churn-fraude-.pdf |}} {{:dm:5.dm_ml_exemplarprojects-shoppingbehaviour_innovators.pdf |}} | Giannotti | |
| |12. | Gio 18.04.2019 - 14:00-18:00 | Data Science Privacy & Ethics. Project work | {{ :dm:5.dml-ml-privacy_etica-.pdf |}}| Giannotti | | |12. | Gio 18.04.2019 - 14:00-18:00 | Hints on data science with Python. Data Science Privacy & Ethics. | {{ :dm:5.dml-ml-privacy_etica-.pdf |}}| Giannotti, Rossetti | |
| ===== Datasets ===== | ===== Datasets ===== |
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| 0. {{ :dm:data.txt.zip | Iris}}. (for details see [[https://archive.ics.uci.edu/ml/datasets/iris]]) | 0. {{ :dm:data.txt.zip | Iris}}. (for details see [[https://archive.ics.uci.edu/ml/datasets/iris]]) |
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| 1. {{ :dm:human_resources.csv.zip | Human Resources}}. (for details see [[https://www.kaggle.com/ludobenistant/hr-analytics]]) | 1. {{ :dm:titanic_train.csv.zip | Titanic}}. (for details see [[https://www.kaggle.com/c/titanic]]) |
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| 2. {{ :dm:telco_churn.csv.zip | Telco Churn}}. (for details see [[http://didawiki.di.unipi.it/doku.php/dm/mains.santanna.dm4crm.2016]]) | 2. {{ :dm:human_resources.csv.zip | Human Resources}}. (for details see [[https://www.kaggle.com/ludobenistant/hr-analytics]]) |
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| | 3. {{ :dm:telco_churn.csv.zip | Telco Churn}}. (for details see [[http://didawiki.di.unipi.it/doku.php/dm/mains.santanna.dm4crm.2016]]) |
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| | 4. {{ :dm:adult.csv.zip | Adult}}. (for details see [[https://archive.ics.uci.edu/ml/datasets/Adult]]) |
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| | 5. {{ :dm:credit_card.txt.zip | Credit Card}} (for details see [[https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients]]) |
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| 3. {{ :dm:adult.csv.zip | Adult}}. (for details see [[https://archive.ics.uci.edu/ml/datasets/Adult]]) | |
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| 4. {{ :dm:titanic_train.csv.zip | Titanic}}. (for details see [[https://www.kaggle.com/c/titanic]]) | |
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| ===== Exercises ===== | ===== Exercises ===== |
