İstanbul’daki Restoranlar için Makine Öğrenmesi Algoritmaları Kullanılarak Fiyat Tahmin Modeli

Öz

Günümüzde veri bilimi kavramıyla birlikte firmalar veriye dayalı yeni ürünler ortaya çıkarmış ve işletmelerin dijitalleşme süreçlerini hızlandırmıştır. Bu çalışmada İstanbul’da bulunan yeme-içme sektöründeki işletmelerin verileri toplanarak makine öğrenmesi algoritmaları ile bir fiyat tahmin modeli önerilmiştir. Bu çalışmada restoran fiyat tahmini için XGBoost, Random Forest, Artificial Neural Network, K-Nearest Neighbor, Multi Linear Regression ve CatBoost gibi farklı makine öğrenmesi modelleme algoritmaları kullanılmıştır. Fiyat tahmini için sınıflandırma algoritmaları test edilmiş ve değerlendirme sonucunda XGBoost algoritmasının 0.023236 RMSE ve 0.0005399 MSE hata oranları ile en iyi algoritma olduğu gözlemlenmiştir. Bu çalışma sayesinde işletme sahipleri işletmelerinde yapacakları yeni geliştirmelerin fiyat ve müşteri geri bildirimi açısından ne derece fayda sağlayacağını anlayabileceklerdir. Girişimcilerin yeni kuracakları bir işletmenin hangi özelliklere sahip olması gerektiğini ve bu özelliklere göre müşterilerine sunacakları ortalama fiyat konusunda bilgi sahibi olmasını sağlayacaktır. Ayrıca restoran açmak isteyen girişimcilerin neye ne kadar maliyet koyması gerektiğini öğrenecek, fiyat performansı sağlayacak, ürünlerini makul fiyata satacağı için daha çok ürün satıp karlılığını arttıracaktır. Doğru fiyatlandırma pazarlamanın dört önemli kavramlarından biridir. Firmanın tutunması, müşteri sadakati oluşturması için doğru fiyatlandırma yapması gerekmektedir.

Anahtar Kelimeler

• Veri Bilimi
• Fiyat Stratejisi
• Veri Yönetimi
• Tahmin Modellemesi
• Sınıflandırma Algoritmaları

Price Prediction Model for Restaurants In Istanbul By Using Machine Learning Algorithms

Abstract

Today, companies have created new products based on data and accelerated the digitalization processes of businesses with the concept of data science. In this study, a price prediction model is proposed with machine learning algorithms by collecting the data of businesses in the food and beverage sector in Istanbul. In this study, different machine learning modeling algorithms such as XGBoost, Random Forest, Artificial Neural Network, K-Nearest Neighbor, Multi Linear Regression and CatBoost were used for restaurant price prediction. Classification algorithms were tested for price prediction, and as a result of the evaluation, it was observed that XGBoost algorithm achieve the highest performance with 0.023236 RMSE and 0.0005399 MSE error rates. By this study, business owners will be able to understand how new developments they will make in their businesses will benefit in terms of price and customer feedback. It will enable entrepreneurs to have information about what features a new business should have and the average price they will offer to their customers according to these features. In addition, entrepreneurs who want to open a restaurant will learn how much they should cost, provide price performance, and increase their profitability by selling more products because they will sell their products at affordable prices. Accurate pricing is one of the four important concepts of marketing. The company needs to make the right pricing in order to hold on and create customer loyalty.

Keywords

• Data Science
• Price Strategy. Data Management
• Prediction Model
• Classification Algorithms.

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