Authors : Samir Pandey; Ami Shah

Volume/Issue : Volume 10 - 2025, Issue 3 - March

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Google Scholar : https://tinyurl.com/yebzy7sz

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DOI : https://doi.org/10.38124/ijisrt/25mar1342

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Abstract : 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.

Keywords : Data Cleaning, Data Preparation, Time Series Analysis, Cohort Segmentation, Churn Analysis, Outlier Detection, Feature Engineering.

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