One-hot encoding data transaksi

Sepanjang kursus, kita akan menggunakan pipeline umum untuk prapemrosesan data dalam market basket analysis. Langkah pertama adalah mengimpor DataFrame pandas dan memilih kolom yang berisi transaksi. Setiap transaksi dalam kolom tersebut berupa string yang terdiri atas sejumlah item yang dipisahkan dengan koma. Langkah berikutnya adalah menggunakan fungsi lambda untuk membagi setiap string transaksi menjadi sebuah list, sehingga mengubah kolom menjadi list of lists.

Pada latihan ini, Anda akan mulai dengan list of lists dari himpunan data grocery, yang tersedia sebagai transactions. Anda kemudian akan mengubah transactions menjadi DataFrame one-hot encoded, di mana setiap kolom berisi nilai TRUE dan FALSE yang menunjukkan apakah sebuah item disertakan dalam suatu transaksi.

Latihan ini adalah bagian dari kursus

Analisis Market Basket dengan Python

Petunjuk latihan

Dari mlxtend.preprocessing, impor TransactionEncoder
Buat instance transaction encoder dan identifikasi item unik dalam transactions.
Lakukan one-hot encoding pada transactions ke dalam sebuah array dan tetapkan nilainya ke onehot.
Konversikan array tersebut menjadi DataFrame pandas dengan menggunakan nama item sebagai header kolom.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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)

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: Apa itu Market Basket Analysis?Exercise 2: Dasar-dasar market basket analysis Exercise 3: Penjualan silang produk Exercise 4: Mengidentifikasi aturan asosiasi Exercise 5: Anteseden dan konsekuen berganda Exercise 6: Menyiapkan data untuk market basket analysis Exercise 7: Menghasilkan association rules Exercise 8: Metrik yang paling sederhana Exercise 9: One-hot encoding data transaksi

Latihan Saat Ini

Exercise 10: Menghitung metrik support

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!