Passenger Capacity: Passenger capacity is the most important feature in predicting flight prices, as it is directly related to the number of passengers that can be accommodated on the flight. This is important because the more passengers that can be accommodated, the higher the potential revenue for the flight. Engines: The number of engines installed on the aircraft is also an important factor in predicting flight prices. The more engines an aircraft has, the more powerful it is, and the faster it can fly. This is important because faster flights can be more expensive, as they can cover more distance in a shorter amount of time. Crew: The size of the crew required to operate the aircraft is also an important factor in predicting flight prices. The larger the crew, the more expensive the flight will be, as more personnel will need to be paid for their services. D-Check Complete: A binary indicator (1 or 0) representing whether a D-check maintenance procedure has been completed for the aircraft is also an important factor in predicting flight prices. A D-check is a comprehensive maintenance procedure that is required to be completed on an aircraft every few years. If the aircraft has not had a D-check completed,
The feature importance of the model is determined by the SHAP values, which measure the contribution of each feature to the model's prediction. The higher the SHAP value, the more important the feature is in the model's prediction. Passenger capacity is the most important feature, with a SHAP value of 50.768%. This is because the capacity of an aircraft is a key factor in determining its performance and overall quality. Engines is the second most important feature, with a SHAP value of 37.455%. This is because the number of engines installed on an aircraft affects its performance and efficiency. Crew is the third most important feature, with a SHAP value of 5.212%. This is because the size of the crew required to operate the aircraft affects its performance and efficiency. D-check complete is the fourth most important feature, with a SHAP value of 2.915%. This is because the completion of a D-check maintenance procedure is necessary for the aircraft to be in optimal condition. Review scores rating is the fifth most important feature, with a SHAP value of 2.17%. This is because the overall review or rating of Prediction inference number 0 question1
Shap Values Explanation The SHAP values of the features provide insights into the prediction made by the machine learning model. The base value of 2204.082 indicates the average prediction of the model. The engines feature has the highest SHAP value of 299.433, indicating that it has the most influence on the prediction. This is likely due to the fact that the number of engines installed on an aircraft is a key factor in determining its performance and reliability. The passenger capacity feature has a SHAP value of 233.495, indicating that it is also an important factor in the prediction. This is likely due to the fact that the passenger capacity of an aircraft is a key factor in determining its profitability. The crew feature has a negative SHAP value of -12.029, indicating that it has a negative influence on the prediction. This is likely due to the fact that the size of the crew required to operate the aircraft is a key factor in determining its operational costs. The D-check complete feature has a positive SHAP value of 16.101, indicating that it has a positive influence on the prediction. This is likely due to the fact that the completion of a D-check maintenance procedure is a key factor in ensuring the safety and reliability of Prediction inference number 1 question1
Shap Values Explanation The SHAP values of the features provide insights into the prediction made by the machine learning model. The base value of 2204.082 indicates the average prediction of the model. The engines feature has the highest SHAP value of -155.579, indicating that it has the most negative influence on the prediction. This suggests that the model is predicting a lower value when the number of engines is higher. The passenger capacity feature has the second highest SHAP value of 293.645, indicating that it has the most positive influence on the prediction. This suggests that the model is predicting a higher value when the passenger capacity is higher. The crew feature has the third highest SHAP value of -12.029, indicating that it has a negative influence on the prediction. This suggests that the model is predicting a lower value when the size of the crew is higher. The D-check complete feature has a SHAP value of 16.101, indicating that it has a positive influence on the prediction. This suggests that the model is predicting a higher value when the D-check maintenance procedure has been completed. The moon clearance complete feature has a SHAP value of 0.0, indicating that it has no influence on the prediction. The I Prediction inference number 2 question1
Shap Values Explanation The SHAP values of the features provide insights into the prediction made by the machine learning model. The base value of 2204.082 indicates the average prediction of the model. The engines feature has the highest SHAP value of -164.524, indicating that it has the most negative influence on the prediction. This is likely due to the fact that the number of engines installed on the aircraft is a key factor in determining its performance and efficiency. The passenger capacity feature has a SHAP value of -15.8, indicating that it has a negative influence on the prediction. This is likely due to the fact that the passenger capacity of an aircraft is a key factor in determining its overall cost and efficiency. The crew feature has a SHAP value of -12.029, indicating that it has a negative influence on the prediction. This is likely due to the fact that the size of the crew required to operate the aircraft is a key factor in determining its overall cost and efficiency. The D-check complete feature has a SHAP value of 16.101, indicating that it has a positive influence on the prediction. This is likely due to the fact that the completion of a D-check maintenance procedure is a key factor in ensuring Prediction inference number 3 question1
Shap Values Explanation The SHAP values of the features in the prediction model provide insights into the importance of each feature in the prediction. The base value of 2204.082 indicates the average prediction value of the model. The engines feature has the highest SHAP value of -180.13, indicating that it has the most negative influence on the prediction. This is likely due to the fact that the number of engines installed on the aircraft is a key factor in determining its performance and efficiency. The passenger capacity feature has the second highest SHAP value of -304.644, indicating that it has the second most negative influence on the prediction. This is likely due to the fact that the passenger capacity of an aircraft is a key factor in determining its overall performance and efficiency. The crew feature has the third highest SHAP value of 32.522, indicating that it has the most positive influence on the prediction. This is likely due to the fact that the size of the crew required to operate the aircraft is a key factor in determining its overall performance and efficiency. The D-check complete feature has the fourth highest SHAP value of 16.101, indicating that it has a positive influence on the prediction. This is likely due to the fact that the completion Prediction inference number 4 question1
Shap Values Explanation The SHAP values of the features provide insights into the prediction made by the machine learning model. The base value of 2204.082 indicates the average prediction of the model. The engines feature has the highest SHAP value of -181.85, indicating that it has the most negative influence on the prediction. This is likely due to the fact that the number of engines installed on the aircraft is a key factor in determining its performance and efficiency. The passenger capacity feature has the second highest SHAP value of 721.64, indicating that it has the most positive influence on the prediction. This is likely due to the fact that the passenger capacity of an aircraft is a key factor in determining its profitability. The crew feature has the third highest SHAP value of -101.13, indicating that it has a negative influence on the prediction. This is likely due to the fact that the size of the crew required to operate the aircraft is a key factor in determining its operational costs. The D-check complete feature has a SHAP value of -20.492, indicating that it has a negative influence on the prediction. This is likely due to the fact that the completion of a D-check maintenance procedure is a key factor in Prediction inference number 5 question1
Shap Values Explanation The SHAP values of the features provide insights into the prediction made by the machine learning model. The base value of 2204.082 indicates the average prediction of the model. The engines feature has the highest SHAP value of -164.524, indicating that it has the most negative influence on the prediction. This is likely due to the fact that the number of engines installed on the aircraft is a key factor in determining its performance and efficiency. The passenger capacity feature has a SHAP value of -15.8, indicating that it has a slightly negative influence on the prediction. This is likely due to the fact that the passenger capacity of an aircraft is a key factor in determining its overall cost and efficiency. The crew feature has a SHAP value of 32.522, indicating that it has a positive influence on the prediction. This is likely due to the fact that the size of the crew required to operate the aircraft is a key factor in determining its performance and efficiency. The D-check complete feature has a SHAP value of -20.492, indicating that it has a negative influence on the prediction. This is likely due to the fact that the completion of a D-check maintenance procedure is a key factor in determining