Penyaringan dengan support dan conviction

Dalam video, kita membahas kelanjutan pekerjaan konsultasi yang Anda lakukan untuk pendiri sebuah rintisan penjual ebook. Pendiri tersebut mendatangi Anda dengan DataFrame rules, yang memuat hasil kerja seorang data scientist yang sebelumnya bekerja di sana. DataFrame ini mencakup kolom untuk antecedents dan consequents, beserta kinerja setiap aturan tersebut terhadap sejumlah metrik.

Tujuan Anda adalah melakukan penyaringan multi-metrik pada himpunan data untuk mengidentifikasi aturan yang berpotensi berguna. Perhatikan bahwa pandas tersedia sebagai pd dan numpy sebagai np. Selain itu, rules telah didefinisikan dan tersedia.

Latihan ini merupakan bagian dari kursus

Analisis Market Basket dengan Python

Instruksi latihan

Gunakan metode .head() dengan print untuk meninjau pratinjau himpunan data.
Pilih subset aturan dengan antecedent support lebih besar dari 0,05.
Pilih subset aturan dengan consequent support lebih besar dari 0,02.
Pilih subset aturan dengan conviction lebih besar dari 1,01.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# 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)

Edit dan Jalankan Kode

Latihan ini merupakan 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 dan lift Exercise 2: Merekomendasikan buku dengan support Exercise 3: Menyempurnakan support dengan confidence Exercise 4: Penyempurnaan lebih lanjut dengan lift Exercise 5: Leverage dan conviction Exercise 6: Lift versus leverage Exercise 7: Menghitung conviction Exercise 8: Menghitung conviction dengan sebuah fungsi Exercise 9: Mempromosikan e-book dengan conviction Exercise 10: Asosiasi dan disosiasi Exercise 11: Menghitung asosiasi dan disosiasi Exercise 12: Mendefinisikan metrik Zhang Exercise 13: Menerapkan metrik Zhang Exercise 14: Aturan lanjutan Exercise 15: Penyaringan dengan support dan conviction

Latihan Saat Ini

Exercise 16: Menggunakan penyaringan multi-metrik untuk promosi silang buku

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!