two-layer pooling stride visualizing cross industry standard process for data mining (crisp-dmprocess model assessment stage attribute generation building machine intelligence business context and requirements business problem data collection data description data integration data mining lifecycle - data mining problem data preparation data quality analysis data understanding data wrangling deployment eda evaluation phase ml pipelines data preparation data processing and wrangling data retrieval deployment and monitoring feature extraction and engineering feature scaling and selection model evaluation and tuning modeling standard supervised unsupervised model assessment project plan training model tuned models cross sellingassociation rule-mining dependencies eda - - fp growth - - market basket analysis - orange table data structure transaction set cross validation (cvk-fold model building and tuning - single data point curse of dimensionality customer segmentation clustering strategy (see clustering strategyobjectives customer understanding higher revenue latent customer segments optimal product placement target marketing strategies clustering eda custom optimizer darraysee matrix data collection csv file - defined - html - json - sql web scraping (see web scrapingdata description categorical defined numeric text data-driven decisions data mining problem data mungingsee data wrangling data science - datasets data structures data summarization agg(function groupby(function quantity_purchased user_class data visualization defined matplotlib annotations axis controls - figure and subplots - global parameters graph legend - plot formatting - pylab pyplot pandas bar plots - box plots - histograms - line charts - pie charts - scatter plots - data wrangling defined downstream steps