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consumer-insights

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Classification ML models for predicting customer outcomes (namely, whether they're likely to opt into email / catalog marketing) depending on customer demographics (age, proximity to store, gender, customer loyalty duration) as well as sales and shopping frequencies by department

  • Updated May 4, 2021
  • Jupyter Notebook

Built upon an existing open-source project to analyze cosmetic product pricing by examining ingredients, brand influence, and customer ratings. Enhanced the project with ingredient scoring, brand analysis, and data visualizations using Python and Sephora data.

  • Updated Sep 1, 2024

This project offers an in-depth analysis of consumer behavior, logistical performance, and payment preferences within the e-commerce sector. By examining order costs, delivery times, and payment methods, businesses can uncover valuable insights into operational efficiency and customer preferences.

  • Updated Aug 23, 2024

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