Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785

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Department: search.filters.department.Lisansüstü Eğitim Enstitüsü, Büyük Veri Analitiği Yüksek Lisans ProgramıInstitution Author: search.filters.institutionAuthor.Şimşek, Yıldırım Murat

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  • Master Term Project
    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.