Alt Kümeleme

Unutma, şirket işini büyütmek ve Hoppiness’i ülke çapında sunmak istiyor. Bu nedenle modelin, yeni mağazalardan elde edilen verilerde de çalışmalı. Modelinin yeni verilerde nasıl performans göstereceğini kontrol etmenin bir yolu, önce verinin bir kısmı üzerinde modeli eğitmek ve ardından kalan kısmı tahmin etmeye çalışmaktır.

Her müşteri için kaydedilen son satın alımı ayırarak bir eğitim veri kümesi oluşturacaksın. Bunu, choice.data üzerinde subset() fonksiyonunu kullanıp gösterge değişkeni LASTPURCHASE değeri 0 olan tüm gözlemleri seçerek yapabilirsin. Benzer şekilde, gösterge değişkeni LASTPURCHASE değeri 1 olan tüm gözlemleri seçerek bir test veri kümesi oluşturursun.

Bu egzersiz

R ile Tepki Modelleri Oluşturma

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Son satın alımı çıkararak bir eğitim veri kümesi oluştur. choice.data üzerinde subset() fonksiyonunu kullan. subset argümanı olarak LASTPURCHASE == 0 belirt. Sonucu train.data’ya ata.
choice.data üzerinde subset() fonksiyonunu kullanarak bir test veri kümesi oluştur. subset argümanı olarak LASTPURCHASE == 1 belirt. Sonucu test.data’ya ata.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Create the training data


# Create the test data

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile Tepki Modelleri Oluşturma

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

The first chapter introduces you to the basic principles and concepts of market response models. Here, you will learn how to build simple response models for product sales. In addition, you will learn about the theoretical and practical differences between linear and non-linear models for sales responses.

Exercise 1: Fundamentals of market response models Exercise 2: Retail sales Exercise 3: Understanding sales Exercise 4: Linear response models Exercise 5: A linear response model for sales Exercise 6: Making predictions Exercise 7: Predictive performance Exercise 8: Nonlinear response models Exercise 9: Linearizing nonlinear functions Exercise 10: What 's the value added?

An effective marketing strategy combines all the tools available to communicate the benefits of a product. The key is crafting the right mix of these tools to achieve sales increases and market share goals. In the second chapter, you will learn how to incorporate the effects of advertising and promotion in your sales-response model and how to identify the marketing strategy that is most likely to succeed.

Exercise 1: Model extension part 1: Dummy variables Exercise 2: Understanding dummy variables Exercise 3: The effect of display on sales Exercise 4: The effect of multiple dummies on sales Exercise 5: What about price?Exercise 6: Model extensions part 2: Dynamic variables Exercise 7: How to lag?Exercise 8: Adding lagged price effects Exercise 9: More lags Exercise 10: What's the value added?Exercise 11: How many extensions are needed?Exercise 12: Summarizing the model Exercise 13: Unnecessary predictors Exercise 14: Dropping predictors Exercise 15: Eliminating predictors

A company can only be successful in the market if its products have a competitive advantage over those of its rivals. To develop an effective marketing strategy in a competitive environment, it is essential to understand the interrelationship between marketing activity and customer behavior. In this chapter, you will learn how to explain the effects of temporary price changes on customer brand choice by employing logistic and probit response models.

Exercise 1: Models for individual demand Exercise 2: Customer purchases Exercise 3: Summarizing the data Exercise 4: Competition Exercise 5: A linear probability model for beer demand Exercise 6: Logistic response models Exercise 7: A logistic model for beer demand Exercise 8: Bounded predictions Exercise 9: Average marginal effects Exercise 10: Effect plots Exercise 11: Probit response models Exercise 12: A probit model for beer demand Exercise 13: Logistic vs. probit Exercise 14: Model comparison

The main goal of response modeling is to enable marketers to not only see a payoff for their actions today, but also tomorrow. In order to view this future payoff, a simple but reliable statistical model is required. In this last chapter, you will learn how to evaluate the predictive performance of logistic response models.

Exercise 1: Model seçimi Exercise 2: Lojistik yanıt modelini genişletme Exercise 3: Modeli özetleme Exercise 4: Sapma ilkesi Exercise 5: Yordayıcıları elemek Exercise 6: Öngörü performansı Exercise 7: Sınıflandırmalar Exercise 8: Model karmaşası Exercise 9: ROC eğrileri Exercise 10: Model doğrulama Exercise 11: Alt Kümeleme

Geçerli Egzersiz

Exercise 12: Model eğitimi Exercise 13: Örnek dışı test Exercise 14: Kapanış