Klasifikasi Tipe Konsumen Retail Supermarket Menggunakan Decision Tree Berdasarkan Data Transaksi
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Abstract
The goal of this study is to use a Decision Tree algorithm to group different types of shoppers in a retail supermarket setting. The dataset used in this study came from a public Kaggle repository and has 1,000 transaction records. These records include information about the customers, the types of products they bought, the details of the transactions, and the time of day they took place. We used Python-based libraries like pandas and scikit-learn in Google Colab to process and analyze the data. The research method includes preprocessing the data, creating new features, encoding categorical variables, splitting the data, training the model, and evaluating it. We used accuracy, precision, recall, and F1-score to measure how well the model worked. The experimental results show that the Decision Tree model was about 56.5% accurate when using all features and 61.5% accurate when using only a few key features with the tree depth set to a certain level. These results show that it's possible to group customers based on their transactional and demographic information, but the model's performance could still be better. This study helps us understand how to segment customers in retail analytics and gives us a starting point for future improvements using more advanced machine learning methods.
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