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Build regression forecast for new product

We saw in a previous exercise that regression forecasts are also worth building! Your workspace has some preloaded things to help. You have a data frame called MET_sp_train with the variables log_sales, log_price, christmas, valentine, newyear, and mother in it. Your workspace also has a validation data frame MET_sp_valid for predictions.

Este exercício faz parte do curso

Forecasting Product Demand in R

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Instruções do exercício

  • Build a regression model predicting log of sales with log of price and all the holiday and promotion variables.
  • Forecast out the model with the predict function and the MET_sp_valid data frame.
  • Exponentiate your forecast and create an xts object.
  • Calculate the MAPE using the MET_sp_v object for your validation set. Your MET_sp_valid data frame won't help here as it has all log prices and you want the MAPE on actual prices.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Build a regression model on the training data
model_MET_sp_full <- lm(___ ~ ___ + ___ + ___ + ___ + ___, data = ___)

# Forecast the regression model using the predict function
pred_MET_sp <- ___(___, newdata = ___)

# Exponentiate your predictions and create an xts object
pred_MET_sp <- ___(___)
pred_MET_sp_xts <- ___(___, order.by = ___)

# Calculate MAPE
MAPE <- mape(___, ___)
print(MAPE)
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