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Predictive Model of Steel Prices for Decision-Making
The goal of this project is to create a statistical model to forecast the future price of steel, which will rely on sector indexes and material prices. We will identify which variable has the most explanatory power. Multiple models will be created to identify the one that performs best. In order to increase the accuracy of the information generated by the model, risk forecasting will be added. The resulting model is meant to aid internal buyers in decision making. As our partner buys over 100 M USD worth of steel annually, an improvement in profits will be of great benefit to him.
Faculty Supervisor:
Richard Labib
Student:
Gabriel Laprise
Partner:
Acier AGF Inc
Discipline:
Mathematics
Sector:
Mining and quarrying
University:
Program:
Accelerate
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