Agregasi dan pemfilteran

Dalam video, kita membantu manajer toko suvenir menata seksi-seksi di lokasi ritel fisik berdasarkan aturan asosiatif. Tata letak toko mengharuskan kita mengelompokkan seksi menjadi dua pasangan jenis produk. Setelah menerapkan teknik pemfilteran lanjutan, kita mengusulkan tata letak lantai seperti di bawah ini.

Gambar menunjukkan tata letak toko yang dipilih dalam video.

Sekarang manajer toko meminta Anda membuat usulan denah lantai lain, tetapi dengan kriteria berbeda: setiap pasangan seksi harus memuat satu produk dengan dukungan tinggi dan satu produk dengan dukungan rendah. Data aggregated telah diagregasi dan di-one-hot encode untuk Anda. Selain itu, apriori() dan association_rules() telah diimpor dari mlxtend.

Latihan ini adalah bagian dari kursus

Analisis Market Basket dengan Python

Petunjuk latihan

Hasilkan himpunan item sering dengan ambang dukungan minimum 0,0001.
Identifikasi semua aturan dengan ambang dukungan minimum 0,0001.
Pilih semua aturan dengan antecedent support lebih besar dari 0,35.
Pilih semua aturan dengan consequent support maksimum lebih rendah dari 0,35.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Apply the apriori algorithm with a minimum support of 0.0001
frequent_itemsets = apriori(aggregated, ____, use_colnames = True)

# Generate the initial set of rules using a minimum support of 0.0001
rules = association_rules(frequent_itemsets, 
                          metric = "____", min_threshold = ____)

# Set minimum antecedent support to 0.35
rules = rules[____['antecedent support'] > ____]

# Set maximum consequent support to 0.35
rules = rules[____ < 0.35]

# Print the remaining rules
print(rules)

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

Latihan Saat Ini

Exercise 14: Menerapkan aturan Zhang Exercise 15: Penyaringan lanjutan dengan banyak metrik

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!