Aggregation durchführen

Nach kleineren Beratungsaufträgen für eine Bibliothek und einen E‑Book‑Händler hast du endlich dein erstes großes Market-Basket-Analysis-Projekt: Du berätst einen Online-Händler für ausgefallene Geschenkartikel bei Cross-Promotions. Da der Händler bisher noch nie eine Data Scientist beschäftigt hat, möchte er, dass du das Projekt damit beginnst, seine Transaktionsdaten zu erkunden. Er hat dich gebeten, eine Aggregation für alle signs im Datensatz durchzuführen und außerdem den Support für diese Kategorie zu berechnen. Beachte, dass pandas bereits als pd importiert wurde. Zusätzlich wurden die Daten im One‑Hot‑Encoding-Format als onehot importiert.

Diese Übung ist Teil des Kurses

<Kurs>Market Basket Analysis in Python</Kurs>

Übungsanweisungen

Wähle die Teilmenge der DataFrame-Spalten aus, die den String sign enthalten.
Gib den Support für signs aus.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Select the column headers for sign items
sign_headers = [i for i in onehot.columns if i.lower().find('sign')>=0]

# Select columns of sign items using sign_headers
sign_columns = onehot[____]

# Perform aggregation of sign items into sign category
signs = sign_columns.sum(axis = 1) >= 1.0

# Print support for signs
print('Share of Signs: %.2f' % ____.mean())

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Diese Übung ist Teil des Kurses

<Kurs>Market Basket Analysis in Python</Kurs>

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In this chapter, you’ll learn the basics of Market Basket Analysis: association rules, metrics, and pruning. You’ll then apply these concepts to help a small grocery store improve its promotional and product placement efforts.

Exercise 1: What is market basket analysis?Exercise 2: The basics of market basket analysis Exercise 3: Cross-selling products Exercise 4: Identifying association rules Exercise 5: Multiple antecedents and consequents Exercise 6: Preparing data for market basket analysis Exercise 7: Generating association rules Exercise 8: The simplest metric Exercise 9: One-hot encoding transaction data Exercise 10: Computing the support metric

Association rules tell us that two or more items are related. Metrics allow us to quantify the usefulness of those relationships. In this chapter, you’ll apply six metrics to evaluate association rules: supply, confidence, lift, conviction, leverage, and Zhang's metric. You’ll then use association rules and metrics to assist a library and an e-book seller.

Exercise 1: Confidence and lift Exercise 2: Recommending books with support Exercise 3: Refining support with confidence Exercise 4: Further refinement with lift Exercise 5: Leverage and conviction Exercise 6: Lift versus leverage Exercise 7: Computing conviction Exercise 8: Computing conviction with a function Exercise 9: Promoting ebooks with conviction Exercise 10: Association and dissociation Exercise 11: Computing association and dissociation Exercise 12: Defining Zhang's metric Exercise 13: Applying Zhang's metric Exercise 14: Advanced rules Exercise 15: Filtering with support and conviction Exercise 16: Using multi-metric filtering to cross-promote books

The fundamental problem of Market Basket Analysis is determining how to translate vast amounts of customer decisions into a small number of useful rules. This process typically starts with the application of the Apriori algorithm and involves the use of additional strategies, such as pruning and aggregation. In this chapter, you’ll learn how to use these methods and will ultimately apply them in exercises where you assist a retailer in selecting a physical store layout and performing product cross-promotions.

Exercise 1: Aggregation Exercise 2: Aggregation durchführen

Aktuelle Übung

Exercise 3: Eine Aggregationsfunktion definieren Exercise 4: Der Apriori-Algorithmus Exercise 5: Pruning und Apriori Exercise 6: Häufige Itemsets mit Apriori identifizieren Exercise 7: Auswahl einer Support-Schwelle Exercise 8: Grundlegtes Pruning von Apriori-Ergebnissen Exercise 9: Assoziationsregeln erzeugen Exercise 10: Pruning mit Lift Exercise 11: Pruning mit Confidence Exercise 12: Fortgeschrittenes Pruning von Apriori-Ergebnissen Exercise 13: Aggregation und Filtern Exercise 14: Zhangs Regel anwenden Exercise 15: Fortgeschrittenes Filtern mit mehreren Metriken

In this final chapter, you’ll learn how visualizations are used to guide the pruning process and summarize final results, which will typically take the form of itemsets or rules. You’ll master the three most useful visualizations -- heatmaps, scatterplots, and parallel coordinates plots – and will apply them to assist a movie streaming service.

Exercise 1: Heatmaps Exercise 2: Visualizing itemset support Exercise 3: Heatmaps with lift Exercise 4: Interpreting heatmaps Exercise 5: Scatterplots Exercise 6: Pruning with scatterplots Exercise 7: Optimality of the support-confidence border Exercise 8: Parallel coordinates plot Exercise 9: Using parallel coordinates to visualize rules Exercise 10: Refining a parallel coordinates plot Exercise 11: Congratulations!