Filteren met support en conviction

In de video bespraken we het lopende advieswerk dat je doet voor de oprichter van een start-up die e-books verkoopt. De oprichter heeft je de DataFrame rules gegeven, met het werk van een data scientist die eerder in dienst was. Het bevat kolommen voor antecedents en consequents, plus de prestaties van elk van die regels op basis van verschillende metrieken.

Jouw doel is om multi-metriekfiltering op de gegevensset uit te voeren om mogelijk bruikbare regels te vinden. Let op: pandas is beschikbaar als pd en numpy als np. Daarnaast is rules al gedefinieerd en beschikbaar.

Deze oefening maakt deel uit van de cursus

Market Basket Analysis in Python

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Oefeninstructies

Gebruik de methode .head() met print om een preview van de gegevensset te zien.
Selecteer de subset regels met een antecedent-support groter dan 0,05.
Selecteer de subset regels met een consequent-support groter dan 0,02.
Selecteer de subset regels met een conviction groter dan 1,01.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Preview the rules DataFrame using the .head() method
print(____)

# Select the subset of rules with antecedent support greater than 0.05
rules = rules[rules['antecedent support'] > ____]

# Select the subset of rules with a consequent support greater than 0.02
rules = rules[____]

# Select the subset of rules with a conviction greater than 1.01
rules = ____

# Print remaining rules
print(rules)

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 en lift Exercise 2: Boeken aanbevelen met support Exercise 3: Support verfijnen met confidence Exercise 4: Verder verfijnen met lift Exercise 5: Leverage en conviction Exercise 6: Lift versus leverage Exercise 7: Conviction berekenen Exercise 8: Conviction berekenen met een functie Exercise 9: Ebooks promoten met conviction Exercise 10: Associatie en dissociatie Exercise 11: Associatie en dissociatie berekenen Exercise 12: Zhang's metriek definiëren Exercise 13: Zhang's metric toepassen Exercise 14: Geavanceerde regels Exercise 15: Filteren met support en conviction

Huidige oefening

Exercise 16: Meerdere metrieken gebruiken om boeken kruislinks te promoten

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: Performing aggregation Exercise 3: Defining an aggregation function Exercise 4: The Apriori algorithm Exercise 5: Pruning and Apriori Exercise 6: Identifying frequent itemsets with Apriori Exercise 7: Selecting a support threshold Exercise 8: Basic Apriori results pruning Exercise 9: Generating association rules Exercise 10: Pruning with lift Exercise 11: Pruning with confidence Exercise 12: Advanced Apriori results pruning Exercise 13: Aggregation and filtering Exercise 14: Applying Zhang's rule Exercise 15: Advanced filtering with multiple metrics

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!