One-hot encoden van transactiegegevens

In de hele cursus gebruiken we een vaste pijplijn om gegevens voor te bereiden voor market basket-analyse. De eerste stap is om een pandas DataFrame te importeren en de kolom te selecteren die de transacties bevat. Elke transactie in die kolom is een string met meerdere items, gescheiden door komma’s. De volgende stap is om met een lambda-functie elke transactiestring te splitsen in een lijst, zodat de kolom verandert in een lijst-van-lijsten.

In deze oefening begin je met de lijst-van-lijsten uit de supermarktgegevensset, die beschikbaar is als transactions. Vervolgens zet je transactions om naar een one-hot geencodeerd DataFrame, waarbij elke kolom uit TRUE- en FALSE-waarden bestaat die aangeven of een item in een transactie voorkwam.

Deze oefening maakt deel uit van de cursus

Market Basket Analysis in Python

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Oefeninstructies

Importeer uit mlxtend.preprocessing de TransactionEncoder.
Instantieer een transaction encoder en identificeer de unieke items in transactions.
One-hot-encode transactions naar een array en wijs de waarden toe aan onehot.
Zet de array om naar een pandas DataFrame met de itemnamen als kolomkoppen.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import the transaction encoder function from mlxtend
from ____.____ import ____
import pandas as pd

# Instantiate transaction encoder and identify unique items in transactions
encoder = TransactionEncoder().____(____)

# One-hot encode transactions
onehot = encoder.____(transactions)

# Convert one-hot encoded data to DataFrame
onehot = pd.DataFrame(____, columns = encoder.columns_)

# Print the one-hot encoded transaction dataset
print(onehot)

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Deze oefening maakt deel uit van de cursus

Market Basket Analysis in Python

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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: Wat is market basket analysis?Exercise 2: De basis van market basket analysis Exercise 3: Cross-selling van producten Exercise 4: Associatieregels identificeren Exercise 5: Meerdere antecedenten en consequenten Exercise 6: Data voorbereiden voor market-basketanalyse Exercise 7: Associatieregels genereren Exercise 8: De eenvoudigste maatstaf Exercise 9: One-hot encoden van transactiegegevens

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Exercise 10: De support-metriek berekenen

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: 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!