Objective: To predict sales of a product in stores refers or forecasting the future sales performance of a particular product within a retail setting.The objective of using a Random Forest Regressor for big sales prediction is to develop a predictive model that can accurately forecast sales for a given period based on various input variables or features.
To implement big sales prediction using the Random Forest Regressor, I followed the following steps:
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Data Preparation: I started by loading and preparing the dataset.
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Splitting: This involved separating the features (input variables) and the target variable (sales) from the dataset. I then split the data into training and testing sets using the train_test_split function from the sklearn.model_selection module.
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Model Creation and Training: Next, I created an instance of the RandomForestRegressor class from the sklearn.ensemble module. This class represents the Random Forest Regressor model. I then trained the model using the fit() method, passing in the training features (X_train) and the corresponding target values (y_train).
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Predicting Sales: After training the model, I made predictions on the test set (X_test) using the predict() method.These metrics provide insights into how well the model predicts the sales values compared to the actual values.
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Model Evaluation: I calculated evaluation metrics such as mean squared error percentage, mean squared error (RMSE) to assess the performance of the model. These metrics provide insights into how well the model predicts the sales values compared to the actual values.
By following these steps, I implemented big sales prediction using the Random Forest Regressor. The Random Forest Regressor algorithm is well-suited for this task as it can handle both numerical and categorical features, and it combines multiple decision trees to make accurate predictions.