An End-to-End Python Based Data Science Framework for Customer Transaction Big Data Analytics
DOI:
https://doi.org/10.47065/jimat.v6i1.964Keywords:
Data Science; Python; Big Data; Customer Transactions; Clustering; Insight AnalyticsAbstract
This study aims to address the problem of underutilized big data customer transactions by implementing a data science approach using the Python programming language. Many organizations accumulate large volumes of transaction data; however, these data often fail to generate strategic value due to the absence of systematic analytical models. The main problem examined in this research is how customer transaction big data can be processed and analyzed to extract meaningful insights that support data-driven business decision making. As a solution, this study applies a Python-based data science model that integrates data preprocessing, exploratory data analysis (EDA), and machine learning techniques to uncover patterns of customer behavior.The model used in this research is developed using Python and its data science ecosystem, including pandas and NumPy for data manipulation, matplotlib for data visualization, and scikit-learn for machine learning implementation. Customer transaction data are processed through several analytical stages, beginning with data cleaning and transformation, followed by the construction of behavioral variables using the Recency, Frequency, and Monetary (RFM) framework. Subsequently, a clustering model based on the K-Means algorithm is applied to segment customers according to their transaction characteristics. The results of the study show that the proposed data science model is effective in extracting insights from big data customer transactions. The clustering process successfully identifies distinct customer segments with different levels of activity and value contribution. The findings reveal three main customer groups: low-contribution customers, potential customers, and high-value customers. These results demonstrate that the implementation of data science using Python can transform raw transaction data into actionable knowledge that supports more targeted marketing strategies, improved customer retention, and enhanced strategic decision making.
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