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This project is designed for learners to Learn the basics of Machine Learning models as well as demonstrate how these models can be used to forecast Average Revenue for a Telecom client.
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Difficulty: AdvancedDesigned for those with a technical background or industry experience
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Duration: Approximately 2 hours
Course Overview
- Time series model are particularly utilized when the training dataset has a time-dependent feature or the target variable is continuous function of time.
- The term “Average Revenue Per User” probably originated in the telecommunications industry, where it is used as a benchmark metric for predicting the future of company’s revenue.
- Telecom operators use ARPU forecasting model to get a greater understanding of the customers. If ARPU is low, it indicates that telecom operators will be targeting too many low-value consumers and will need to increase the value (revenue) retrieved from the product.
- Learn to analyze the ARPU to understand if the pricing is appropriate and build and apply a robust time series model to predict the revenue.
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