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dm:mains.santanna.dm4crm.2019 [09/04/2019 alle 21:21 (5 anni fa)]
Fosca Giannotti [Calendar] |
dm:mains.santanna.dm4crm.2019 [17/04/2019 alle 21:57 (5 anni fa)] (versione attuale)
Fosca Giannotti |
| ====== 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\\ [[fosca.giannotti@isti.cnr.it]] | * **Fosca Giannotti**\\ ISTI-CNR, Knowledge Discovery and Data Mining Lab\\ [[fosca.giannotti@isti.cnr.it]] |
| * **Dino Pedreschi**\\ Università di Pisa, Knowledge Discovery and Data Mining Lab\\ [[dino.pedreschi@unipi.it]] | * **Dino Pedreschi**\\ Università di Pisa, Knowledge Discovery and Data Mining Lab\\ [[dino.pedreschi@unipi.it]] |
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| * Teaching Assistant: **Riccardo Guidotti** & **Giulio Rossetti**\\ ISTI-CNR, Knowledge Discovery and Data Mining Lab\\ [[riccardo.guidotti@isti.cnr.it]] [[giulio.rossetti@isti.cnr.it]] | * Teaching Assistants: **Riccardo Guidotti** & **Giulio Rossetti**\\ ISTI-CNR, Knowledge Discovery and Data Mining Lab\\ [[riccardo.guidotti@isti.cnr.it]] [[giulio.rossetti@isti.cnr.it]] |
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| ===== News ===== | ===== News ===== |
| |02. | Gio 11.04.2019 - 14:00-18:00 | Data understanding; data preparation; Knime tutorial | {{:dm:4.dm_ml_data_preparation.pdf| slides}} {{:dm:04_dataunderstanding.pdf| slides data understanding}} {{:dm:knime_slides_mains.pdf| Tutorial Knime}} {{ :dm:01_titanic_data_understanding.zip | 01_titanic_data_understanding}} | Pedreschi, Guidotti | | |02. | Gio 11.04.2019 - 14:00-18:00 | Data understanding; data preparation; Knime tutorial | {{:dm:4.dm_ml_data_preparation.pdf| slides}} {{:dm:04_dataunderstanding.pdf| slides data understanding}} {{:dm:knime_slides_mains.pdf| Tutorial Knime}} {{ :dm:01_titanic_data_understanding.zip | 01_titanic_data_understanding}} | Pedreschi, Guidotti | |
| |03. | Ven 12.04.2019 - 09:00-13:00 | Clustering analysis & customer segmentation | {{:dm:dm.pedreschi.clustering.2015.pdf| slides clustering}} {{:dm:customersegmentation.pdf| slides customer segmentation}} | Pedreschi | | |03. | Ven 12.04.2019 - 09:00-13:00 | Clustering analysis & customer segmentation | {{:dm:dm.pedreschi.clustering.2015.pdf| slides clustering}} {{:dm:customersegmentation.pdf| slides customer segmentation}} | Pedreschi | |
| |04. | Ven 12.04.2018 - 14:00-18:00 | Clustering analysis: esercizi con Knime | {{ :dm:02_titanic_clustering.zip | 02_titanic_clustering}} | Pedreschi, Guidotti | | |04. | Ven 12.04.2018 - 14:00-18:00 | Clustering analysis: exercises with Knime | {{ :dm:02_titanic_clustering.zip | 02_titanic_clustering}} | Pedreschi, Guidotti | |
| |05. | Lun 15.04.2019 - 09:00-13:00 | Classification & prediction | {{:dm:dm.giannotti.pedreschi.classification.2015.pdf| slides classification}} [[http://www.r2d3.us/visual-intro-to-machine-learning-part-1/|Visual Introduction to Classification with Decision Trees]] |Pedreschi | | |05. | Lun 15.04.2019 - 09:00-13:00 | Classification & prediction | {{:dm:dm.giannotti.pedreschi.classification.2015.pdf| slides classification}} [[http://www.r2d3.us/visual-intro-to-machine-learning-part-1/|Visual Introduction to Classification with Decision Trees]] |Pedreschi | |
| |06. | Lun 15.04.2019 - 14:00-18:00 | Classification & prediction: esercizi con Knime | {{ :dm:05_titanic_classification.zip | 05_titanic_classification}} | Pedreschi, Guidotti | | |06. | Lun 15.04.2019 - 14:00-18:00 | Classification & prediction: exercises with Knime | {{ :dm:05_titanic_classification.zip | 05_titanic_classification}} | Pedreschi, Rossetti | |
| |07. | Mar 16.04.2019 - 09:00-13:00 | More on Classification | {{ :dm:dm_ml.classification_evaluation.2017.pdf | Evaluation of classifiers }} {{ :dm:lezioneadvancedclassificationmethods1-knn_nb.pdf | KNN & Naive Bayes}} {{ :dm:lezioneadvancedclassificationmethods2-ann_svm.pdf | Neural Networks & SVM}} {{ :dm:ensemblemethod_wisdomofthecrowd.pdf | Ensemble methods & Wisdom of the crowd}} | Pedreschi | | |07. | Mar 16.04.2019 - 09:00-13:00 | More on Classification: from decision trees to deep learning | {{ :dm:dm_ml.classification_evaluation.2017.pdf | Evaluation of classifiers }} {{ :dm:lezioneadvancedclassificationmethods1-knn_nb.pdf | KNN & Naive Bayes}} {{ :dm:lezioneadvancedclassificationmethods2-ann_svm.pdf | Neural Networks & SVM}} {{ :dm:ensemblemethod_wisdomofthecrowd.pdf | Ensemble methods & Wisdom of the crowd}} | Pedreschi | |
| |08. | Mar 16.04.2019 - 14:00-18:00 | Pattern and association rule mining: esercizi con Knime |{{:dm:03_titanic_pattern.zip | 03_titanic_pattern}} {{ :dm:04_coop_pattern.zip | 04_coop_pattern}} | Giannotti, Guidotti | | |08. | Mar 16.04.2019 - 14:00-18:00 | Classification & prediction: exercises with Knime. Project work | | Giannotti, Rossetti | |
| |09. | Mer 17.04.2019 - 09:00-13:00 | Pattern and association rule mining & market basket analysis | {{ :dm:5.dm-ml_patternmining-2018.pdf |}} | Giannotti, Pedreschi, Guidotti | | |09. | Mer 17.04.2019 - 09:00-13:00 | Pattern and association rule mining & market basket analysis | {{ :dm:5.dm-ml_patternmining-2018.pdf |}} | Giannotti | |
| |10. | Mer 17.04.2019 - 14:00-18:00 | Pattern and association rule mining: esercizi con 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 | {{ :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 ===== |
| **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 **22 June 2018**. Specify [MAINS] in the subject of the email. | **Deadline**: send the report by email to all instructors within **22 June 2019**. Specify [MAINS] in the subject of the email. |
| ====== Exams ====== | ====== Exams ====== |
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