Use XGBoost to forecast ticket sales (time series data).
[Instructions]
1. 1_Prepare_Train_Data.ipynb
Prepare training data.
Setting parameters:
• x_time_seq: length of time series
• input_filename: input data file path
• output_filename: output time series data file path
After the setting is completed, it can be executed.
2. 2_Train.ipynb
Train the XGBoost model.
Setting parameters:
• train_input_filename: input data file path
• model_filename: the path of the output model file
• scaler_filename: output normalized file path
• train_output_filename: the path of the output prediction file
• modelEstimators: the number of Estimators
After the setting is completed, it can be executed.
3. 3_Prepare_Inference_Data.ipynb
Prepare inference data.
Setting parameters:
• x_time_seq: length of time series
• input_filename: input data file path
• output_filename: output time series data file path
After the setting is completed, it can be executed.
4. 4_Inference.ipynb
Use the trained model to make inferences.
Setting parameters:
• inference_input_filename: input data file path
• model_filename: input model file path
• scaler_filename: input normalized file path
• inference_output_filename: the path of the output prediction file
After the setting is completed, it can be executed.
After execution, you can see the results of XGBoost inference.
This SDK is built in AppForAI - AI Dev Tools.
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