Yüksek Lisans, Proje Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/215

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Association Rule Mining on Ticket Sales Data
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Genç, Özge; Küçükaydın, Hande
This study aims to analyze the ticket sales data of a cultural institution and define association rules between the festivals/event group and festival/event group venues by market basket analysis. Market basket analysis is a well-known data mining method that is used to discover similarities between products or product groups. For market basket analysis, the apriori algorithm is applied which is considered as a popular data mining algorithm and helps to discover frequent item sets and forms association rules within the dataset. In this project, the apriori algorithm is applied using Python to discover the association rule: between the venues (implementation 1), between the venues excluding the venues used for a specific festival type (implementation 2), between festivals and event groups if any (implementation 3). According to the results of implementation 1, the associations are mostly between the venues of a specific festival type. According to the implementation 2, when we exclude this specific festival type from the dataset, it is seen the rules are mostly between the venues of another festival type. In implementation 3, when the festival venues variable is excluded and only the event names are considered, it is seen that the support, lift and confidence values are lower than the previous implementations.
Market Analysis - Aydınlı Group
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Öney, Çağlayan Özgür; Çakar, Tuna
In this paper, we have analyzed the purchase transaction data of Aydınlı Group. Aydınlı offers their customers diverse set of products by providing Polo, Cacharel and Pierre Cardin brands on both retail and online store. The million dollar question that we seek an answer in our research is "can we determine the purchase pattern of customers?".
Market Basket Analysis on Retail Stores of Electronic Devices
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Topçu, Feray Ece; Kırbız, Serap
Market basket analysis is a technique that discovers the relationship between the pairs of products purchased together. It simply analyses the purchase coincidence with the products purchased among the sales transactions and explain what is purchased with what. This study presents a market basket analysis to discover the association rules between products within a dataset that is extracted from one of a leading retail company of electronic devices. The aim is to understand the purchasing behavior trends by examining which products are purchased together. The Apriori algorithm provides the opportunity to discover association rules. Market basket analysis is developed with R Language. R community has a library for association rules that’s called “arules”. Additionally, “arulesViz” and “plotly” libraries are used to visualize the output of analysis. Further steps to this study could be diversification of stores groups through machine learning algorithms and according to this classification, market basket analysis may apply on generated classes of the stores separately. In addition, the output of market basket analysis can be input for a recommendation engine.
Market Basket Analysis Using Apriori Algorithm
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Şimşek, Yıldırım Murat; Çakar, Tuna
Predictive analysis is a branch of data engineering that predicts some occurrence or probabilities depend on the data. To make predictions about future events, predictive analytics uses data mining techniques. The process of these techniques involves an analysis of historic data and predicts the future events based on that analysis. Also using predictive analytics modelling techniques, a model can be created to predict. Depending on the data that they are using these predictive models can be varied. Predictive analytics is made of various statistical and analytical techniques used to develop models that will predict future occurrence, events or probabilities. Market basket analysis is one of the data mining techniques that focusing on discovering purchasing pattern by extracting associations from a store’s transactional data. The electronic commerce point-of-sale expanded the utilization and application of transactional data in Market Basket Analysis. The needs of the customers have to be known and adapted to them from the retailers. The retailers collect information about their customers and what they purchase with the help of the advanced technology. Analysing this information is extremely valuable for understanding purchasing behaviour in retail commerce. Market basket analysis is one possible way to discover which items can be sold together. This analysis gives retailer valuable information about related sales on a group of goods basis customers who buy bread often also buy several products related to bread like milk or butter. It makes sense that these groups are placed side by side in a store so that customers can reach them quickly. Market basket analysis is very useful technique for the related group of products that are bought together, and to reorganize the supermarket layout, and also to design promotional campaigns such that products’ purchase can be improved. The main aim of this capstone project is to find the co-occurring items in consumer shopping baskets in the data set that provided by GittiGidiyor E-Commerce Company with the help of the association rule mining algorithm; apriori. Mining association rules from transactional data will provide us with valuable information about co-occurrences and copurchases of products. Such information can be used as a basis for decisions about marketing activity such as promotional support, inventory control and cross-sale campaigns.

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Browsing Yüksek Lisans, Proje Koleksiyonu by Subject "Apriori Algorithm"