PLEASE SHOW ALL WORK AND COMMENT ALL CODE The Objective of this coding problem is the prediction of a proposed metro ectension construction project based on the people'es opinion. There are three alternatives to choose they are as follows: Eglington-Pickering Line Airport-Vaughn Line Airposrt-Hamilton Line Each record is represented by 16 features. Task-2: Metro-Ext-Prediction.xlsx is the dataset for prediction purpose to use. Predict the alternatives to chose based on peoples opiniongiven in this dataset for the above three (Logistic Regression, KNN, and Naive Bayes) models and compare the result Deliverables = coding files (.py and .ipynb), and prediction result for both models metro-EXT-Prediction.xlsx (Please place chart in EXCEL) Feasibility and Constructability Slopes and Gradients Urban Realm Geology and Soil Stability Land Acquisition Work Opportunities Economy in Movement of People Revenue Generation Access to the Social, Recreational and Emergency Services Neighbourhood Acceptance (Sound, Vibration, etc) Improvement of Quality of Life Convenience in Movement of People Protection of the Ecosystem Pollution (Water, Air, Soil, Visual) Control CO2 Emission Control Conservation of Vegetation and Plants 4.1 3.1 3.2 4.3 3.1 3.5 3.3 3.9 3.9 2.8 3.1 3.7 3.1 3.4 2.4 2.9 3.7 2.8 2.9 4.1 3.9 4.5 4.3 3.7 3.6 2.1 3.3 4.3 2.8 2.9 3.0 3.0 4.1 3.2 2.4 3.8 3.4 4.3 4.7 4.2 4.7 1.9 2.4 4.5 3.7 3.6 3.4 2.3 3.6 3.2 2.2 4.0 4.0 4.6 3.9 3.3 4.4 2.0 3.5 3.9 3.2 2.9 2.9 2.4 3.7 3.0 3.3 3.6 4.1 3.5 3.9 3.9 4.8 1.9 3.3 4.2 2.6 2.4 2.6 2.8 3.9 2.5 3.3 4.5 3.5 3.4 3.8 3.7 3.5 1.7 3.0 4.5 3.0 3.5 2.9 2.6 4.8 3.9 2.9 4.5 3.4 4.3 4.5 4.5 3.8 2.0 3.1 4.7 3.1 2.9 2.5 3.7 3.3 3.1 3.0 4.1 4.1 4.4 4.4 3.6 4.0 1.9 2.9 4.1 3.4 2.2 3.7 3.3 4.7 3.3 3.2 3.9 4.0 4.2 4.2 3.8 4.0 2.3 3.0 4.5 3.1 3.1 3.0 2.7 4.2 3.0 2.9 3.7 3.9 3.7 3.2 4.0 4.6 1.9 2.4 4.4 2.7 3.0 3.4 2.9 4.3 2.7 3.2 3.8 3.9 4.2 4.0 3.8 4.3 1.7 3.0 4.2 3.5 2.8 3.1 2.2 4.2 3.2 3.0 4.3 4.0 4.0 4.4 4.9 3.8 1.5 3.4 3.4 2.9 2.6 3.3 3.5 4.1 3.2 3.0 3.9 3.8 4.2 4.2 4.0 3.7 1.8 2.7 4.3 3.2 2.3 3.8 3.4 4.0 2.7 3.6 4.5 3.9 3.8 3.3 4.0 4.2 2.5 2.7 3.5 3.2 2.8 3.5 3.9 3.9 3.3 2.5 4.0 4.7 3.7 3.9 3.9 4.1 1.5 3.5 4.0 3.1 2.6 3.0 3.0 3.5 3.4 2.6 3.3 4.0 3.9 4.4 4.4 4.3 2.2 2.8 4.4 3.1 3.4 3.0 3.2 2.8 2.9 3.6 2.4 2.9 4.2 2.9 2.3 2.2 2.3 2.8 2.9 2.5 3.5 1.4 2.5 3.0 2.0 3.2 1.7 2.3 4.1 2.1 2.3 3.3 1.9 2.4 2.3 2.6 3.2 2.0 3.4 3.5 1.5 2.9 2.1 3.3 3.4 2.7 3.4 3.2 1.7 3.6 2.5 3.0 3.1 2.4 3.6 3.0 2.3 2.9 1.4 3.5 3.6 3.0 3.0 3.4 2.0 2.8 3.5 2.7 3.0 1.4 2.6 3.2 2.0 2.6 2.4 3.4 3.7 2.3 3.3 3.9 2.7 3.2 3.4 2.9 2.6 1.3 3.1 2.1 2.2 3.3 1.5 2.7 3.7 3.5 3.1 3.1 2.4 2.9 2.7 3.3 2.9 1.7 2.8 3.5 2.4 3.7 2.8 3.0 4.8 3.3 3.6 3.3 2.4 3.1 3.1 3.4 3.0 1.8 2.8 2.6 2.0 2.3 2.0 3.3 3.8 3.1 3.1 3.0 1.9 3.8 2.6 2.7 2.6 2.4 2.7 3.8 2.3 3.4 2.5 3.6 4.2 3.1 2.6 2.6 2.5 2.7 2.7 3.0 2.9 1.3 3.8 3.3 2.4 2.3 2.2 2.9 3.7 2.6 3.0 2.8 1.3 3.7 2.8 2.7 3.1 2.1 2.3 2.7 1.5 2.3 1.6 3.3 3.5 3.1 2.5 3.1 1.9 3.6 3.6 3.0 3.1 2.0 3.3 3.6 2.0 2.9 2.5 3.6 3.3 2.7 3.1 2.7 2.1 3.1 2.3 3.8 3.0 1.4 2.8 2.6 1.8 3.5 1.8 3.5 4.3 3.1 3.6 3.2 2.0 3.3 2.7 3.2 3.9 2.0 3.8 3.9 1.8 3.0 2.0 2.2 3.3 2.3 3.2 2.8 2.4 2.8 2.6 3.0 2.6 1.5 3.1 3.4 1.8 2.4 1.7 2.7 3.6 3.8 2.8 3.0 1.7 3.3 2.5 3.7 3.0 1.8 3.6 1.2 3.3 2.8 3.1 2.0 3.2 3.2 2.9 2.0 1.7 2.2 2.8 3.1 3.4 1.9 1.1 1.8 2.6 2.9 3.0 2.7 3.8 2.8 2.8 2.4 1.8 3.2 2.9 3.1 2.5 1.9 2.9 1.9 3.2 3.0 3.9 2.7 4.3 2.6 2.9 2.2 1.9 3.3 2.4 2.8 3.2 1.9 1.8 2.3 3.2 3.0 3.5 2.1 3.5 2.3 2.7 2.5 1.6 3.6 2.5 3.8 2.4 2.5 1.7 2.2 2.9 2.3 3.3 2.1 3.9 3.0 2.9 1.2 2.3 3.3 3.1 2.2 2.8 2.7 1.9 2.2 3.3 3.1 2.5 1.5 4.3 3.9 3.9 2.1 1.7 3.1 3.0 2.8 3.0 2.7 2.2 1.7 3.1 2.3 2.4 2.1 3.8 3.3 3.5 1.4 2.4 2.7 2.4 3.5 3.0 1.4 2.0 2.4 3.2 2.8 3.3 1.5 4.0 3.0 3.5 2.4 1.8 3.3 2.8 3.6 2.9 2.2 2.4 2.1 3.2 2.7 2.6 2.0 3.8 2.9 3.2 1.4 1.6 3.8 3.2 3.3 3.7 1.4 2.0 2.0 3.0 3.1 3.2 1.9 3.8 2.8 2.9 2.2 2.2 3.6 3.0 2.5 2.4 1.7 2.2 2.4 3.0 2.5 3.0 2.3 4.2 2.8 3.1 2.3 2.1 2.9 3.4 3.2 3.0 2.2 1.9 1.8 2.7 2.7 3.2 2.4 3.4 2.5 3.3 2.5 1.5 2.9 2.5 3.3 3.8 2.1 2.0 1.8 3.4 2.8 3.1 2.1 4.3 2.7 3.4 2.7 2.5 3.1 2.3 3.1 3.4 1.8 2.3 2.0 3.7 2.7 3.0 2.1 4.8 3.2 3.0 1.9 2.1 2.6 2.4 2.9 2.6 2.0 1.5 1.8 2.9 2.9 3.1 1.8 3.9 3.8 3.4 2.1 1.8 4.0 2.4 2.8 3.7 2.9 2.9
PLEASE SHOW ALL WORK AND COMMENT ALL CODE The Objective of this coding problem is the prediction of a proposed metro ectension construction project based on the people'es opinion. There are three alternatives to choose they are as follows: Eglington-Pickering Line Airport-Vaughn Line Airposrt-Hamilton Line Each record is represented by 16 features. Task-2: Metro-Ext-Prediction.xlsx is the dataset for prediction purpose to use. Predict the alternatives to chose based on peoples opiniongiven in this dataset for the above three (Logistic Regression, KNN, and Naive Bayes) models and compare the result Deliverables = coding files (.py and .ipynb), and prediction result for both models metro-EXT-Prediction.xlsx (Please place chart in EXCEL) Feasibility and Constructability Slopes and Gradients Urban Realm Geology and Soil Stability Land Acquisition Work Opportunities Economy in Movement of People Revenue Generation Access to the Social, Recreational and Emergency Services Neighbourhood Acceptance (Sound, Vibration, etc) Improvement of Quality of Life Convenience in Movement of People Protection of the Ecosystem Pollution (Water, Air, Soil, Visual) Control CO2 Emission Control Conservation of Vegetation and Plants 4.1 3.1 3.2 4.3 3.1 3.5 3.3 3.9 3.9 2.8 3.1 3.7 3.1 3.4 2.4 2.9 3.7 2.8 2.9 4.1 3.9 4.5 4.3 3.7 3.6 2.1 3.3 4.3 2.8 2.9 3.0 3.0 4.1 3.2 2.4 3.8 3.4 4.3 4.7 4.2 4.7 1.9 2.4 4.5 3.7 3.6 3.4 2.3 3.6 3.2 2.2 4.0 4.0 4.6 3.9 3.3 4.4 2.0 3.5 3.9 3.2 2.9 2.9 2.4 3.7 3.0 3.3 3.6 4.1 3.5 3.9 3.9 4.8 1.9 3.3 4.2 2.6 2.4 2.6 2.8 3.9 2.5 3.3 4.5 3.5 3.4 3.8 3.7 3.5 1.7 3.0 4.5 3.0 3.5 2.9 2.6 4.8 3.9 2.9 4.5 3.4 4.3 4.5 4.5 3.8 2.0 3.1 4.7 3.1 2.9 2.5 3.7 3.3 3.1 3.0 4.1 4.1 4.4 4.4 3.6 4.0 1.9 2.9 4.1 3.4 2.2 3.7 3.3 4.7 3.3 3.2 3.9 4.0 4.2 4.2 3.8 4.0 2.3 3.0 4.5 3.1 3.1 3.0 2.7 4.2 3.0 2.9 3.7 3.9 3.7 3.2 4.0 4.6 1.9 2.4 4.4 2.7 3.0 3.4 2.9 4.3 2.7 3.2 3.8 3.9 4.2 4.0 3.8 4.3 1.7 3.0 4.2 3.5 2.8 3.1 2.2 4.2 3.2 3.0 4.3 4.0 4.0 4.4 4.9 3.8 1.5 3.4 3.4 2.9 2.6 3.3 3.5 4.1 3.2 3.0 3.9 3.8 4.2 4.2 4.0 3.7 1.8 2.7 4.3 3.2 2.3 3.8 3.4 4.0 2.7 3.6 4.5 3.9 3.8 3.3 4.0 4.2 2.5 2.7 3.5 3.2 2.8 3.5 3.9 3.9 3.3 2.5 4.0 4.7 3.7 3.9 3.9 4.1 1.5 3.5 4.0 3.1 2.6 3.0 3.0 3.5 3.4 2.6 3.3 4.0 3.9 4.4 4.4 4.3 2.2 2.8 4.4 3.1 3.4 3.0 3.2 2.8 2.9 3.6 2.4 2.9 4.2 2.9 2.3 2.2 2.3 2.8 2.9 2.5 3.5 1.4 2.5 3.0 2.0 3.2 1.7 2.3 4.1 2.1 2.3 3.3 1.9 2.4 2.3 2.6 3.2 2.0 3.4 3.5 1.5 2.9 2.1 3.3 3.4 2.7 3.4 3.2 1.7 3.6 2.5 3.0 3.1 2.4 3.6 3.0 2.3 2.9 1.4 3.5 3.6 3.0 3.0 3.4 2.0 2.8 3.5 2.7 3.0 1.4 2.6 3.2 2.0 2.6 2.4 3.4 3.7 2.3 3.3 3.9 2.7 3.2 3.4 2.9 2.6 1.3 3.1 2.1 2.2 3.3 1.5 2.7 3.7 3.5 3.1 3.1 2.4 2.9 2.7 3.3 2.9 1.7 2.8 3.5 2.4 3.7 2.8 3.0 4.8 3.3 3.6 3.3 2.4 3.1 3.1 3.4 3.0 1.8 2.8 2.6 2.0 2.3 2.0 3.3 3.8 3.1 3.1 3.0 1.9 3.8 2.6 2.7 2.6 2.4 2.7 3.8 2.3 3.4 2.5 3.6 4.2 3.1 2.6 2.6 2.5 2.7 2.7 3.0 2.9 1.3 3.8 3.3 2.4 2.3 2.2 2.9 3.7 2.6 3.0 2.8 1.3 3.7 2.8 2.7 3.1 2.1 2.3 2.7 1.5 2.3 1.6 3.3 3.5 3.1 2.5 3.1 1.9 3.6 3.6 3.0 3.1 2.0 3.3 3.6 2.0 2.9 2.5 3.6 3.3 2.7 3.1 2.7 2.1 3.1 2.3 3.8 3.0 1.4 2.8 2.6 1.8 3.5 1.8 3.5 4.3 3.1 3.6 3.2 2.0 3.3 2.7 3.2 3.9 2.0 3.8 3.9 1.8 3.0 2.0 2.2 3.3 2.3 3.2 2.8 2.4 2.8 2.6 3.0 2.6 1.5 3.1 3.4 1.8 2.4 1.7 2.7 3.6 3.8 2.8 3.0 1.7 3.3 2.5 3.7 3.0 1.8 3.6 1.2 3.3 2.8 3.1 2.0 3.2 3.2 2.9 2.0 1.7 2.2 2.8 3.1 3.4 1.9 1.1 1.8 2.6 2.9 3.0 2.7 3.8 2.8 2.8 2.4 1.8 3.2 2.9 3.1 2.5 1.9 2.9 1.9 3.2 3.0 3.9 2.7 4.3 2.6 2.9 2.2 1.9 3.3 2.4 2.8 3.2 1.9 1.8 2.3 3.2 3.0 3.5 2.1 3.5 2.3 2.7 2.5 1.6 3.6 2.5 3.8 2.4 2.5 1.7 2.2 2.9 2.3 3.3 2.1 3.9 3.0 2.9 1.2 2.3 3.3 3.1 2.2 2.8 2.7 1.9 2.2 3.3 3.1 2.5 1.5 4.3 3.9 3.9 2.1 1.7 3.1 3.0 2.8 3.0 2.7 2.2 1.7 3.1 2.3 2.4 2.1 3.8 3.3 3.5 1.4 2.4 2.7 2.4 3.5 3.0 1.4 2.0 2.4 3.2 2.8 3.3 1.5 4.0 3.0 3.5 2.4 1.8 3.3 2.8 3.6 2.9 2.2 2.4 2.1 3.2 2.7 2.6 2.0 3.8 2.9 3.2 1.4 1.6 3.8 3.2 3.3 3.7 1.4 2.0 2.0 3.0 3.1 3.2 1.9 3.8 2.8 2.9 2.2 2.2 3.6 3.0 2.5 2.4 1.7 2.2 2.4 3.0 2.5 3.0 2.3 4.2 2.8 3.1 2.3 2.1 2.9 3.4 3.2 3.0 2.2 1.9 1.8 2.7 2.7 3.2 2.4 3.4 2.5 3.3 2.5 1.5 2.9 2.5 3.3 3.8 2.1 2.0 1.8 3.4 2.8 3.1 2.1 4.3 2.7 3.4 2.7 2.5 3.1 2.3 3.1 3.4 1.8 2.3 2.0 3.7 2.7 3.0 2.1 4.8 3.2 3.0 1.9 2.1 2.6 2.4 2.9 2.6 2.0 1.5 1.8 2.9 2.9 3.1 1.8 3.9 3.8 3.4 2.1 1.8 4.0 2.4 2.8 3.7 2.9 2.9
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PLEASE SHOW ALL WORK AND COMMENT ALL CODE
The Objective of this coding problem is the prediction of a proposed metro ectension construction project based on the people'es opinion. There are three alternatives to choose they are as follows:
- Eglington-Pickering Line
- Airport-Vaughn Line
- Airposrt-Hamilton Line
Each record is represented by 16 features.
Task-2:
Metro-Ext-Prediction.xlsx is the dataset for prediction purpose to use. Predict the alternatives to chose based on peoples opiniongiven in this dataset for the above three (Logistic Regression, KNN, and Naive Bayes) models and compare the result
Deliverables = coding files (.py and .ipynb), and prediction result for both models
metro-EXT-Prediction.xlsx (Please place chart in EXCEL)
| Feasibility and Constructability | Slopes and Gradients | Urban Realm | Geology and Soil Stability | Land Acquisition | Work Opportunities | Economy in Movement of People | Revenue Generation | Access to the Social, Recreational and Emergency Services | Neighbourhood Acceptance (Sound, Vibration, etc) | Improvement of Quality of Life | Convenience in Movement of People | Protection of the Ecosystem | Pollution (Water, Air, Soil, Visual) Control | CO2 Emission Control | Conservation of Vegetation and Plants |
| 4.1 | 3.1 | 3.2 | 4.3 | 3.1 | 3.5 | 3.3 | 3.9 | 3.9 | 2.8 | 3.1 | 3.7 | 3.1 | 3.4 | 2.4 | 2.9 |
| 3.7 | 2.8 | 2.9 | 4.1 | 3.9 | 4.5 | 4.3 | 3.7 | 3.6 | 2.1 | 3.3 | 4.3 | 2.8 | 2.9 | 3.0 | 3.0 |
| 4.1 | 3.2 | 2.4 | 3.8 | 3.4 | 4.3 | 4.7 | 4.2 | 4.7 | 1.9 | 2.4 | 4.5 | 3.7 | 3.6 | 3.4 | 2.3 |
| 3.6 | 3.2 | 2.2 | 4.0 | 4.0 | 4.6 | 3.9 | 3.3 | 4.4 | 2.0 | 3.5 | 3.9 | 3.2 | 2.9 | 2.9 | 2.4 |
| 3.7 | 3.0 | 3.3 | 3.6 | 4.1 | 3.5 | 3.9 | 3.9 | 4.8 | 1.9 | 3.3 | 4.2 | 2.6 | 2.4 | 2.6 | 2.8 |
| 3.9 | 2.5 | 3.3 | 4.5 | 3.5 | 3.4 | 3.8 | 3.7 | 3.5 | 1.7 | 3.0 | 4.5 | 3.0 | 3.5 | 2.9 | 2.6 |
| 4.8 | 3.9 | 2.9 | 4.5 | 3.4 | 4.3 | 4.5 | 4.5 | 3.8 | 2.0 | 3.1 | 4.7 | 3.1 | 2.9 | 2.5 | 3.7 |
| 3.3 | 3.1 | 3.0 | 4.1 | 4.1 | 4.4 | 4.4 | 3.6 | 4.0 | 1.9 | 2.9 | 4.1 | 3.4 | 2.2 | 3.7 | 3.3 |
| 4.7 | 3.3 | 3.2 | 3.9 | 4.0 | 4.2 | 4.2 | 3.8 | 4.0 | 2.3 | 3.0 | 4.5 | 3.1 | 3.1 | 3.0 | 2.7 |
| 4.2 | 3.0 | 2.9 | 3.7 | 3.9 | 3.7 | 3.2 | 4.0 | 4.6 | 1.9 | 2.4 | 4.4 | 2.7 | 3.0 | 3.4 | 2.9 |
| 4.3 | 2.7 | 3.2 | 3.8 | 3.9 | 4.2 | 4.0 | 3.8 | 4.3 | 1.7 | 3.0 | 4.2 | 3.5 | 2.8 | 3.1 | 2.2 |
| 4.2 | 3.2 | 3.0 | 4.3 | 4.0 | 4.0 | 4.4 | 4.9 | 3.8 | 1.5 | 3.4 | 3.4 | 2.9 | 2.6 | 3.3 | 3.5 |
| 4.1 | 3.2 | 3.0 | 3.9 | 3.8 | 4.2 | 4.2 | 4.0 | 3.7 | 1.8 | 2.7 | 4.3 | 3.2 | 2.3 | 3.8 | 3.4 |
| 4.0 | 2.7 | 3.6 | 4.5 | 3.9 | 3.8 | 3.3 | 4.0 | 4.2 | 2.5 | 2.7 | 3.5 | 3.2 | 2.8 | 3.5 | 3.9 |
| 3.9 | 3.3 | 2.5 | 4.0 | 4.7 | 3.7 | 3.9 | 3.9 | 4.1 | 1.5 | 3.5 | 4.0 | 3.1 | 2.6 | 3.0 | 3.0 |
| 3.5 | 3.4 | 2.6 | 3.3 | 4.0 | 3.9 | 4.4 | 4.4 | 4.3 | 2.2 | 2.8 | 4.4 | 3.1 | 3.4 | 3.0 | 3.2 |
| 2.8 | 2.9 | 3.6 | 2.4 | 2.9 | 4.2 | 2.9 | 2.3 | 2.2 | 2.3 | 2.8 | 2.9 | 2.5 | 3.5 | 1.4 | 2.5 |
| 3.0 | 2.0 | 3.2 | 1.7 | 2.3 | 4.1 | 2.1 | 2.3 | 3.3 | 1.9 | 2.4 | 2.3 | 2.6 | 3.2 | 2.0 | 3.4 |
| 3.5 | 1.5 | 2.9 | 2.1 | 3.3 | 3.4 | 2.7 | 3.4 | 3.2 | 1.7 | 3.6 | 2.5 | 3.0 | 3.1 | 2.4 | 3.6 |
| 3.0 | 2.3 | 2.9 | 1.4 | 3.5 | 3.6 | 3.0 | 3.0 | 3.4 | 2.0 | 2.8 | 3.5 | 2.7 | 3.0 | 1.4 | 2.6 |
| 3.2 | 2.0 | 2.6 | 2.4 | 3.4 | 3.7 | 2.3 | 3.3 | 3.9 | 2.7 | 3.2 | 3.4 | 2.9 | 2.6 | 1.3 | 3.1 |
| 2.1 | 2.2 | 3.3 | 1.5 | 2.7 | 3.7 | 3.5 | 3.1 | 3.1 | 2.4 | 2.9 | 2.7 | 3.3 | 2.9 | 1.7 | 2.8 |
| 3.5 | 2.4 | 3.7 | 2.8 | 3.0 | 4.8 | 3.3 | 3.6 | 3.3 | 2.4 | 3.1 | 3.1 | 3.4 | 3.0 | 1.8 | 2.8 |
| 2.6 | 2.0 | 2.3 | 2.0 | 3.3 | 3.8 | 3.1 | 3.1 | 3.0 | 1.9 | 3.8 | 2.6 | 2.7 | 2.6 | 2.4 | 2.7 |
| 3.8 | 2.3 | 3.4 | 2.5 | 3.6 | 4.2 | 3.1 | 2.6 | 2.6 | 2.5 | 2.7 | 2.7 | 3.0 | 2.9 | 1.3 | 3.8 |
| 3.3 | 2.4 | 2.3 | 2.2 | 2.9 | 3.7 | 2.6 | 3.0 | 2.8 | 1.3 | 3.7 | 2.8 | 2.7 | 3.1 | 2.1 | 2.3 |
| 2.7 | 1.5 | 2.3 | 1.6 | 3.3 | 3.5 | 3.1 | 2.5 | 3.1 | 1.9 | 3.6 | 3.6 | 3.0 | 3.1 | 2.0 | 3.3 |
| 3.6 | 2.0 | 2.9 | 2.5 | 3.6 | 3.3 | 2.7 | 3.1 | 2.7 | 2.1 | 3.1 | 2.3 | 3.8 | 3.0 | 1.4 | 2.8 |
| 2.6 | 1.8 | 3.5 | 1.8 | 3.5 | 4.3 | 3.1 | 3.6 | 3.2 | 2.0 | 3.3 | 2.7 | 3.2 | 3.9 | 2.0 | 3.8 |
| 3.9 | 1.8 | 3.0 | 2.0 | 2.2 | 3.3 | 2.3 | 3.2 | 2.8 | 2.4 | 2.8 | 2.6 | 3.0 | 2.6 | 1.5 | 3.1 |
| 3.4 | 1.8 | 2.4 | 1.7 | 2.7 | 3.6 | 3.8 | 2.8 | 3.0 | 1.7 | 3.3 | 2.5 | 3.7 | 3.0 | 1.8 | 3.6 |
| 1.2 | 3.3 | 2.8 | 3.1 | 2.0 | 3.2 | 3.2 | 2.9 | 2.0 | 1.7 | 2.2 | 2.8 | 3.1 | 3.4 | 1.9 | 1.1 |
| 1.8 | 2.6 | 2.9 | 3.0 | 2.7 | 3.8 | 2.8 | 2.8 | 2.4 | 1.8 | 3.2 | 2.9 | 3.1 | 2.5 | 1.9 | 2.9 |
| 1.9 | 3.2 | 3.0 | 3.9 | 2.7 | 4.3 | 2.6 | 2.9 | 2.2 | 1.9 | 3.3 | 2.4 | 2.8 | 3.2 | 1.9 | 1.8 |
| 2.3 | 3.2 | 3.0 | 3.5 | 2.1 | 3.5 | 2.3 | 2.7 | 2.5 | 1.6 | 3.6 | 2.5 | 3.8 | 2.4 | 2.5 | 1.7 |
| 2.2 | 2.9 | 2.3 | 3.3 | 2.1 | 3.9 | 3.0 | 2.9 | 1.2 | 2.3 | 3.3 | 3.1 | 2.2 | 2.8 | 2.7 | 1.9 |
| 2.2 | 3.3 | 3.1 | 2.5 | 1.5 | 4.3 | 3.9 | 3.9 | 2.1 | 1.7 | 3.1 | 3.0 | 2.8 | 3.0 | 2.7 | 2.2 |
| 1.7 | 3.1 | 2.3 | 2.4 | 2.1 | 3.8 | 3.3 | 3.5 | 1.4 | 2.4 | 2.7 | 2.4 | 3.5 | 3.0 | 1.4 | 2.0 |
| 2.4 | 3.2 | 2.8 | 3.3 | 1.5 | 4.0 | 3.0 | 3.5 | 2.4 | 1.8 | 3.3 | 2.8 | 3.6 | 2.9 | 2.2 | 2.4 |
| 2.1 | 3.2 | 2.7 | 2.6 | 2.0 | 3.8 | 2.9 | 3.2 | 1.4 | 1.6 | 3.8 | 3.2 | 3.3 | 3.7 | 1.4 | 2.0 |
| 2.0 | 3.0 | 3.1 | 3.2 | 1.9 | 3.8 | 2.8 | 2.9 | 2.2 | 2.2 | 3.6 | 3.0 | 2.5 | 2.4 | 1.7 | 2.2 |
| 2.4 | 3.0 | 2.5 | 3.0 | 2.3 | 4.2 | 2.8 | 3.1 | 2.3 | 2.1 | 2.9 | 3.4 | 3.2 | 3.0 | 2.2 | 1.9 |
| 1.8 | 2.7 | 2.7 | 3.2 | 2.4 | 3.4 | 2.5 | 3.3 | 2.5 | 1.5 | 2.9 | 2.5 | 3.3 | 3.8 | 2.1 | 2.0 |
| 1.8 | 3.4 | 2.8 | 3.1 | 2.1 | 4.3 | 2.7 | 3.4 | 2.7 | 2.5 | 3.1 | 2.3 | 3.1 | 3.4 | 1.8 | 2.3 |
| 2.0 | 3.7 | 2.7 | 3.0 | 2.1 | 4.8 | 3.2 | 3.0 | 1.9 | 2.1 | 2.6 | 2.4 | 2.9 | 2.6 | 2.0 | 1.5 |
| 1.8 | 2.9 | 2.9 | 3.1 | 1.8 | 3.9 | 3.8 | 3.4 | 2.1 | 1.8 | 4.0 | 2.4 | 2.8 | 3.7 | 2.9 | 2.9 |
