Retail Refine: Enhancing Retail Transaction Data for Advanced Analytics

In the era of big data, high-quality data is essential for accurate analysis and decision-making. This paper explores the process of data cleaning and preparation for advanced analytics, focusing on techniques such as handling missing values, outlier detection, data transformation, and feature engineering. A case study is presented using a dataset to perform time series analysis, cohort segmentation, churn analysis, and customer segmentation. The goal is to enhance data reliability and usability for machine learning and predictive modeling.