Published December 7, 2023 | Version v1
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Predictive Analytics in E-Commerce: Effective Business Analysis through Machine Learning

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E-commerce follows a revolution thanks to predictive analytics technology and machine learning
because it enhances operational effectiveness and corporate performance through data-driven decision-making.
The research examines the application of individualized marketing methodologies for behavior analysis of
consumers that produces more accurate sales predictions through analytical techniques. The research presents
an analysis of three primary machine-learning techniques: logistic regression, random forests, and deep learning
models. The techniques measure their prediction abilities in setting prices detecting fraud and assessing market
demand. The market advantages for e-commerce companies include enhanced operational processes by predictive
modelling AI, lowered risks, and the creation of personalized experiences for consumers. E-commerce progress in
this field faces multiple challenges especially because of data quality issues and complex predictive model
interpretation processes as well as requirements for large computational capabilities. E-commerce businesses use
predictive analytics as their essential strategic tool to gain market advantages through data-driven operations and
make more accurate choices while the market becomes data-first

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