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Rismon Hasiholan
RFM ANALYSIS AND K-MEANS CLUSTERING: A CASE STUDY ANALYSIS, CLUSTERING, AND PREDICTION ON RETAIL STORE TRANSACTIONS WITH PYTHON GUI
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1403469RFM ANALYSIS AND K-MEANS CLUSTERING: A CASE STUDY ANALYSIS, CLUSTERING, AND PREDICTION ON RETAIL STORE TRANSACTIONS WITH PYTHON GUIhttps://www.gandhi.com.mx/rfm-analysis-and-k-means-clustering-a-case-study-analysis-clustering-and-prediction-on-retail-store-transactions-with-python-gui/phttps://gandhi.vtexassets.com/arquivos/ids/168385/040cb13e-376c-4c95-99a3-eb550c40f895.jpg?v=638333668209400000246246MXNBALIGE PUBLISHINGInStock/Ebooks/1387903RFM ANALYSIS AND K-MEANS CLUSTERING: A CASE STUDY ANALYSIS, CLUSTERING, AND PREDICTION ON RETAIL STORE TRANSACTIONS WITH PYTHON GUI246246https://www.gandhi.com.mx/rfm-analysis-and-k-means-clustering-a-case-study-analysis-clustering-and-prediction-on-retail-store-transactions-with-python-gui/phttps://gandhi.vtexassets.com/arquivos/ids/168385/040cb13e-376c-4c95-99a3-eb550c40f895.jpg?v=638333668209400000InStockMXN99999DIEbook20221230005558407_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1230005558407_pThe dataset used in this project is the detailed data on sales of consumer goods obtained by scanning the bar codes for individual products at electronic points of sale in a retail store. The dataset provides detailed information about quantities, characteristics and values of goods sold as well as their prices. The anonymized dataset includes 64.682 transactions of 5.242 SKUs sold to 22.625 customers during one year./ppDataset Attributes are as follows: Date of Sales Transaction, Customer ID, Transaction ID, SKU Category ID, SKU ID, Quantity Sold, and Sales Amount (Unit price times quantity. For unit price, please divide Sales Amount by Quantity). This dataset can be analyzed with RFM analysis and can be clustered using K-Means algorithm./ppThe machine learning models used in this project to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy./p1230005558407_BALIGE PUBLISHINGlibro_electonico_a04a9260-97db-3be3-8c9a-137465fff300_1230005558407;1230005558407_1230005558407Rismon HasiholanInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/f2ca928b-958d-498f-bb70-f76c6615d8f6-epub-fa8884f6-f37d-4a00-8a0c-a0a013e11566.epub2022-05-07T00:00:00+00:00BALIGE PUBLISHING