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MSE, RMSE, R², and MAE in Airline Passenger Forecasting
- Forecasting airline passengers is crucial for airlines to plan efficiently, optimize costs, and enhance customer satisfaction.
- Machine learning is extensively utilized in predicting airline passenger numbers accurately based on historical data.
- The process of building a forecasting model involves steps like data collection, preprocessing, feature engineering, model selection, training, and evaluation.
- Data collection includes gathering historical passenger counts and incorporating external factors like weather, holidays, and economic indicators.
- Data preprocessing involves cleaning data, handling missing values, detecting outliers, and formatting dates for analysis.
- Feature engineering creates new variables to help the model understand trends, seasonality, and patterns in the data.
- Model selection is crucial, with options like ARIMA, Prophet, XGBoost, LightGBM, and LSTM, depending on the data characteristics and problem.
- Training and testing the model involve splitting the dataset, hyperparameter tuning, and cross-validation for accurate predictions.
- Evaluation metrics such as MSE, RMSE, MAE, and R² are essential for assessing the model's performance and accuracy.
- MSE penalizes large errors heavily, while RMSE gives the average error in the same unit as the data.
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