Memuat
Memuat
A RapidMiner course built around business analytics projects, matching the Business Analytics and Data Mining courses in Management programs. You learn to build process flows visually without writing code, through ten projects: customer segmentation (RFM), churn prediction, market basket analysis, sales prediction with regression, monthly sales forecasting, customer credit scoring, review sentiment analysis, employee attrition prediction, transaction anomaly detection, and promotion campaign response. Suited to coursework, theses, and practice for work.
Getting to know the RapidMiner interface, building processes by dragging and dropping operators, cleaning data, filling missing values, and selecting attributes.
Customer segmentation with RFM and k-means, predicting customers who leave (churn) with a decision tree, and shopping patterns with FP-Growth and association rules.
Predicting sales with linear regression and forecasting monthly sales from time series data, including how to read the errors.
Customer credit scoring, sentiment analysis of text reviews (Process Documents), and predicting employees likely to leave for HR needs.
Detecting unusual transactions and estimating who will respond to promotions, so the campaign budget hits the right target.
Reading the confusion matrix, accuracy, precision-recall and cross-validation, then writing the results up as business decisions in a report or thesis.
Each lesson is built around a real business analytics case. Open a project to see its process flow, dataset and results; the price shown is for the ready-made project file.
BeginnerSegmentation (Clustering)Customer Segmentation with RFM AnalysisGroup 1,500 customers into valuable segments with K-Means. 3 viewsProject file US$17
BeginnerClassificationCustomer Churn PredictionSpot customers about to leave before they actually go. 1 viewsProject file US$17
IntermediateAssociation RulesMarket Basket Analysis with FP-GrowthFind items often bought together to arrange shelves and design promo bundles. 1 viewsProject file US$24
BeginnerRegressionSales Prediction with Linear RegressionMeasure how much advertising, price, discounts and season move sales. 1 viewsProject file US$17
IntermediateForecastingMonthly Sales Forecasting with Time Series DataForecast next-month sales from earlier months and last year's season. 1 viewsProject file US$24
IntermediateClassificationCustomer Credit ScoringPredict customers at risk of default and compare two models using the confusion matrix and ROC. 1 viewsProject file US$24
AdvancedText MiningProduct Review Sentiment AnalysisTurn Indonesian customer reviews into positive or negative sentiment. 1 viewsProject file US$31
AdvancedClassificationEmployee Attrition PredictionIdentify employees at risk of leaving and the factors that drive it. 1 viewsProject file US$31
AdvancedAnomaly DetectionSuspicious Transaction Detection for AuditSet aside unusual transactions from thousands of vendor payments so auditors can focus. 1 viewsProject file US$31
BeginnerClassificationPredicting Customer Response to a Promotion CampaignChoose who to contact so the campaign budget yields the most responses. 2 viewsProject file US$1712 available classes
No. RapidMiner is designed for analytics without coding through a visual interface; every project in the class is built from operators you drag and connect.
Yes. All ten projects use cases that commonly appear in Management coursework: marketing, sales, finance and HR, including how to interpret the results.
The workflow is visual and easy to explain to examiners; it suits those just starting to learn data mining. For more flexibility, Python is still stronger.
Yes. Each project has a RapidMiner process file, dataset and explanation that can be bought separately; the prices are listed in the projects block on this page.
Yes, after completing the final case study.
Consult your learning goals, we'll help you choose the most suitable class based on your level and needs.
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