Frequent itemsets identificeren met Apriori

De aggregatie-oefening die je voor de online retailer deed, was nuttig. Die gaf een startpunt om te begrijpen welke categorieën items vaak in transacties voorkomen. De retailer wil nu de individuele items zelf verkennen om te zien welke vaak voorkomen.

In deze oefening pas je het Apriori-algoritme toe op de online retail-gegevensset zonder eerst te aggregeren. Je doel is om de itemsets te snoeien met een minimale waarde voor support en een maximale drempel voor het aantal items. Let op: pandas is geïmporteerd als pd en de one-hot-gecodeerde data is beschikbaar als onehot.

Deze oefening maakt deel uit van de cursus

Market Basket Analysis in Python

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Oefeninstructies

Geef onehot door aan het Apriori-algoritme.
Stel de minimale support in op 0,006.
Stel de maximale lengte van de itemset in op 3.
Print een preview van de eerste vijf itemsets.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import apriori from mlxtend
from mlxtend.frequent_patterns import apriori

# Compute frequent itemsets using the Apriori algorithm
frequent_itemsets = apriori(____, 
                            ____ = ____, 
                            max_len = ____, 
                            use_colnames = True)

# Print a preview of the frequent itemsets
print(____.head())

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Market Basket Analysis in Python

SkillTag.level.intermediateSkillTag.label

4.9+

Begin de cursus gratis

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: Aggregatie Exercise 2: Aggregatie uitvoeren Exercise 3: Een aggregatiefunctie definiëren Exercise 4: Het Apriori-algoritme Exercise 5: Pruning en Apriori Exercise 6: Frequent itemsets identificeren met Apriori

Huidige oefening

Exercise 7: Een supportdrempel selecteren Exercise 8: Basisresultaten van Apriori snoeien Exercise 9: Associatieregels genereren Exercise 10: Prunen met lift Exercise 11: Prunen met confidence Exercise 12: Geavanceerd snoeien van Apriori-resultaten Exercise 13: Aggregatie en filteren Exercise 14: Zhangs regel toepassen Exercise 15: Geavanceerd filteren met meerdere metrieken

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!