Penyaringan lanjutan dengan banyak metrik

Sebelumnya, kita menggunakan data dari toko suvenir unik daring untuk menemukan anteseden yang dapat digunakan guna mempromosikan konsekuen yang ditargetkan. Karena himpunan aturan yang mungkin sangat besar, kita perlu mengandalkan algoritme Apriori dan penyaringan multi-metrik untuk mempersempitnya. Pada latihan ini, kita akan meninjau seluruh himpunan aturan dan menemukan satu aturan yang berguna, bukan menargetkan anteseden tertentu.

Perhatikan bahwa data telah dimuat, dipraproses, dan di-encode one-hot, serta tersedia sebagai onehot. Selain itu, apriori() dan association_rules() telah diimpor dari mlxtend. Dalam latihan ini, Anda akan menerapkan algoritme Apriori untuk mengidentifikasi frequent itemsets. Selanjutnya, Anda akan menurunkan himpunan association rules dari itemsets tersebut dan menerapkan penyaringan multi-metrik.

Latihan ini adalah bagian dari kursus

Analisis Market Basket dengan Python

Petunjuk latihan

Terapkan algoritme Apriori pada itemset yang di-encode one-hot dengan ambang dukungan minimum 0,001.
Ekstrak association rules menggunakan ambang dukungan minimum 0,001.
Atur antecedent_support pada 0,002 dan consequent_support ke 0,01.
Atur confidence lebih tinggi dari 0,60 dan lift lebih tinggi dari 2,50.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Apply the Apriori algorithm with a minimum support threshold of 0.001
frequent_itemsets = ____(onehot, min_support = ____, use_colnames = True)

# Recover association rules using a minium support threshold of 0.001
rules = ____(frequent_itemsets, metric = '____', min_threshold = 0.001)

# Apply a 0.002 antecedent support threshold, 0.60 confidence threshold, and 2.50 lift threshold
filtered_rules = rules[(rules['antecedent support'] > ____) &
						(____['consequent support'] > 0.01) &
						(rules['____'] > ____) &
						(____ > 2.50)]

# Print remaining rule
print(filtered_rules[['antecedents','consequents']])

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Analisis Market Basket dengan Python

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus 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: Agregasi Exercise 2: Melakukan agregasi Exercise 3: Mendefinisikan fungsi agregasi Exercise 4: Algoritma Apriori Exercise 5: Pruning dan Apriori Exercise 6: Mengidentifikasi itemset yang sering muncul dengan Apriori Exercise 7: Memilih ambang dukungan (support)Exercise 8: Pemangkasan hasil Apriori dasar Exercise 9: Membuat aturan asosiasi Exercise 10: Pruning dengan lift Exercise 11: Pemangkasan dengan confidence Exercise 12: Pemangkasan lanjutan pada hasil Apriori Exercise 13: Agregasi dan pemfilteran Exercise 14: Menerapkan aturan Zhang Exercise 15: Penyaringan lanjutan dengan banyak metrik

Latihan Saat Ini

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!