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This Jupyter Notebook implements Bayesian modeling techniques to fit a posterior distribution and forecast demand for an e-commerce company.

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gkukish/Forecasting-Customer-Demand

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Demand Forecasting

This repository uses the PyMC library to fit a model to the provided data set and plots a posterior distribution. Additionally, I model the purchasing behavior of an e-commerce company. I model the average amount of money spent per month for each customer, set up appropriate likelihood and prior models for an average amount of money spent per customer per month, and use prior predictive and posterior predictive distributions for forecasting monthly demand.

The presentation and summary of the notebook are available in the .PDF file, and the code is showcased in the Jupyter Notebook.

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This Jupyter Notebook implements Bayesian modeling techniques to fit a posterior distribution and forecast demand for an e-commerce company.

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