The objective of this coding problem is the prediction of a proposed metro extension construction project based on the people's opinion. There are three alternatives to choose they are as follows: Eglington-Pickering Line Airport-Vaughan Line Airport-Hamilton Line The datasets are available in the "Test_2-Part_B (Coding Data Analytics Problem)" folder under assignment. Task-1: Metro-Ext.xlsx is the training and test dataset; you will consider 80% of the data for training and 20% for test. Build a (1) Logistic regression (2) KNN and (3) Naïve Bayes model to predict on the test dataset and compute the confusion matrix for each model and compare the result. (PLEASE PLACE CHART IN EXCEL BEFORE CODING AND SAVE AS Metro-Ext.xlsx) 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 Alternatives 4.80 3.21 2.81 4.34 4.36 3.60 3.85 4.13 4.37 1.38 2.55 3.78 3.52 3.63 2.95 3.42 Eglington-Pickering 3.94 2.84 2.70 4.88 4.49 4.19 4.91 3.34 4.11 1.96 2.29 4.88 3.49 2.95 2.69 3.42 Eglington-Pickering 3.76 3.39 2.49 4.47 3.52 4.07 3.63 4.00 4.02 2.53 2.87 3.79 2.82 3.18 2.89 3.14 Eglington-Pickering 3.75 2.72 2.85 3.85 4.15 4.56 3.82 4.56 4.18 1.59 2.62 4.35 3.55 2.50 2.35 3.07 Eglington-Pickering 4.75 2.97 2.37 3.93 4.44 4.24 3.90 4.06 3.49 1.90 2.63 3.67 3.44 2.73 2.70 2.98 Eglington-Pickering 3.22 2.28 3.03 4.12 4.08 4.62 3.94 3.12 3.59 1.68 2.97 4.65 2.57 3.33 2.20 2.29 Eglington-Pickering 4.15 3.10 3.32 3.83 4.34 4.01 4.75 3.98 4.26 2.06 2.35 4.22 2.97 3.07 2.71 3.26 Eglington-Pickering 3.98 2.99 2.96 3.51 4.50 3.83 3.70 3.90 4.27 2.68 2.85 3.53 2.70 3.28 3.20 2.70 Eglington-Pickering 4.14 2.47 3.01 3.48 4.16 4.33 4.33 3.82 4.16 1.87 2.76 3.68 3.40 3.57 3.33 3.74 Eglington-Pickering 4.09 3.87 3.01 3.95 3.48 3.59 3.93 3.76 3.86 1.59 3.57 3.29 2.60 2.89 3.59 2.91 Eglington-Pickering 3.71 2.59 3.54 3.43 4.80 4.43 4.30 4.20 3.73 2.78 2.81 3.39 2.95 3.33 2.54 2.91 Eglington-Pickering 3.29 3.49 2.53 4.49 4.84 3.42 3.95 3.94 4.47 1.18 2.32 3.47 2.13 3.00 2.23 2.85 Eglington-Pickering 4.19 3.54 2.74 3.58 3.64 4.85 4.20 3.38 3.93 1.80 3.51 4.94 2.59 3.06 2.90 2.72 Eglington-Pickering 4.24 2.97 3.32 4.06 4.16 4.46 4.08 4.51 3.29 2.11 3.12 4.56 3.16 3.05 2.52 2.92 Eglington-Pickering 3.52 2.73 3.57 4.24 3.53 3.65 4.86 3.20 3.86 2.03 3.36 4.43 2.98 2.74 3.01 2.26 Eglington-Pickering 3.13 2.75 2.46 4.50 4.17 4.10 4.45 4.55 3.76 2.40 3.51 3.68 2.90 2.09 3.33 3.48 Eglington-Pickering 3.94 3.35 2.72 4.16 4.00 3.27 3.81 4.64 4.21 1.87 3.18 4.05 3.52 2.50 2.99 2.62 Eglington-Pickering 3.84 2.40 3.56 3.48 4.33 4.80 4.62 4.33 4.07 2.57 2.70 3.46 3.09 2.87 3.67 2.39 Eglington-Pickering 4.69 2.67 2.70 4.00 3.97 3.68 4.73 4.47 3.91 1.75 2.93 3.92 2.36 2.57 2.91 3.38 Eglington-Pickering 4.12 2.39 3.02 3.72 4.66 3.76 3.84 4.71 4.18 1.25 2.40 3.44 3.60 2.91 2.89 2.99 Eglington-Pickering 3.87 2.68 2.39 4.58 3.99 4.57 3.05 4.01 4.31 1.86 2.83 4.47 3.08 2.61 2.67 3.30 Eglington-Pickering 4.05 2.84 3.40 4.43 3.77 4.19 4.05 3.50 4.85 1.79 2.50 3.71 3.32 3.51 2.85 3.58 Eglington-Pickering 4.24 3.20 3.46 3.74 3.99 3.67 4.65 4.06 3.94 1.92 3.41 4.24 2.92 2.42 3.22 3.56 Eglington-Pickering 4.49 3.37 3.46 4.01 3.99 4.27 3.54 3.93 3.60 1.62 3.44 3.88 3.33 2.84 3.04 2.62 Eglington-Pickering 3.62 3.14 3.06 3.66 3.97 4.10 3.85 4.20 4.41 2.11 2.73 3.39 2.71 3.28 3.11 3.10 Eglington-Pickering 3.67 3.46 2.36 3.96 4.70 4.34 4.08 3.86 3.87 1.79 2.44 3.42 2.74 2.96 3.34 3.04 Eglington-Pickering 4.62 3.25 2.87 3.98 4.33 3.81 3.81 4.13 3.66 2.23 3.13 4.02 3.00 2.63 2.97 3.24 Eglington-Pickering 3.61 3.00 3.56 3.57 3.82 3.52 4.59 3.96 3.92 2.31 3.48 3.91 2.98 3.35 2.18 3.76 Eglington-Pickering 4.23 3.82 2.71 3.84 3.97 3.96 4.63 3.64 3.88 2.09 3.18 3.88 2.31 2.14 2.93 2.37 Eglington-Pickering 3.85 2.63 2.70 4.36 3.35 3.60 4.20 3.73 3.80 2.85 3.54 3.21 3.05 3.26 2.90 2.94 Eglington-Pickering 3.83 2.57 2.23 4.75 4.47 4.14 4.25 3.86 4.61 2.22 2.41 4.32 3.30 2.89 3.65 3.00 Eglington-Pickering 3.10 2.75 3.21 3.71 3.78 4.89 4.46 4.52 3.81 1.89 3.78 4.90 2.85 2.63 2.58 3.72 Eglington-Pickering 4.05 3.02 3.02 4.41 3.14 4.28 3.84 3.47 3.99 2.14 3.20 4.25 3.18 2.75 2.80 2.24 Eglington-Pickering 3.78 3.69 2.96 4.79 3.49 4.27 4.36 3.88 3.59 1.93 2.90 4.48 2.48 2.90 2.90 2.92 Eglington-Pickering 4.49 3.41 3.26 3.38 3.84 3.73 3.90 3.79 4.13 2.07 3.65 4.28 2.79 2.53 2.88 2.83 Eglington-Pickering 4.07 2.38 3.35 3.76 4.56 3.98 4.13 4.08 3.71 2.66 2.54 4.39 3.79 3.32 2.39 2.46 Eglington-Pickering 4.07 3.36 3.63 4.05 4.18 3.86 4.55 4.08 4.73 2.18 2.81 4.25 2.29 2.76 2.64 3.19 Eglington-Pickering 4.26 3.15 2.18 3.99 4.45 4.02 3.40 3.70 4.54 1.99 3.45 3.82 2.48 3.08 2.28 3.52 Eglington-Pickering 3.98 2.64 3.45 3.87 4.35 4.22 3.88 3.91 3.65 2.89 2.99 3.90 2.81 3.16 2.84 2.48 Eglington-Pickering 4.18 3.27 2.58 3.77 3.94 3.81 4.06 3.46 3.99 1.75 3.31 3.18 2.70 2.43 3.37 3.10 Eglington-Pickering 3.92 2.64 2.23 3.51 3.96 4.08 4.08 4.28 4.22 2.26 2.73 3.92 2.49 2.99 3.32 2.94 Eglington-Pickering 4.49 3.17 2.14 4.41 4.08 4.19 3.46 4.03 3.18 1.97 3.46 3.43 2.83 2.61 2.96 3.37 Eglington-Pickering 3.39 2.56 2.78 4.55 3.38 4.58 4.15 3.49 4.17 2.01 2.85 4.31 3.05 2.79 2.51 2.63 Eglington-Pickering 3.83 2.91 3.91 4.17 4.02 3.11 3.91 3.96 4.29 2.39 3.80 4.41 3.25 2.57 3.12 3.04 Eglington-Pickering 4.22 3.90 2.93 4.04 3.61 3.83 4.01 4.02 3.83 1.98 2.68 3.42 3.23 3.35 3.28 3.10 Eglington-Pickering

The objective of this coding problem is the prediction of a proposed metro extension construction project based on the people's opinion. There are three alternatives to choose they are as follows: Eglington-Pickering Line Airport-Vaughan Line Airport-Hamilton Line The datasets are available in the "Test_2-Part_B (Coding Data Analytics Problem)" folder under assignment. Task-1: Metro-Ext.xlsx is the training and test dataset; you will consider 80% of the data for training and 20% for test. Build a (1) Logistic regression (2) KNN and (3) Naïve Bayes model to predict on the test dataset and compute the confusion matrix for each model and compare the result. (PLEASE PLACE CHART IN EXCEL BEFORE CODING AND SAVE AS Metro-Ext.xlsx) 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 Alternatives 4.80 3.21 2.81 4.34 4.36 3.60 3.85 4.13 4.37 1.38 2.55 3.78 3.52 3.63 2.95 3.42 Eglington-Pickering 3.94 2.84 2.70 4.88 4.49 4.19 4.91 3.34 4.11 1.96 2.29 4.88 3.49 2.95 2.69 3.42 Eglington-Pickering 3.76 3.39 2.49 4.47 3.52 4.07 3.63 4.00 4.02 2.53 2.87 3.79 2.82 3.18 2.89 3.14 Eglington-Pickering 3.75 2.72 2.85 3.85 4.15 4.56 3.82 4.56 4.18 1.59 2.62 4.35 3.55 2.50 2.35 3.07 Eglington-Pickering 4.75 2.97 2.37 3.93 4.44 4.24 3.90 4.06 3.49 1.90 2.63 3.67 3.44 2.73 2.70 2.98 Eglington-Pickering 3.22 2.28 3.03 4.12 4.08 4.62 3.94 3.12 3.59 1.68 2.97 4.65 2.57 3.33 2.20 2.29 Eglington-Pickering 4.15 3.10 3.32 3.83 4.34 4.01 4.75 3.98 4.26 2.06 2.35 4.22 2.97 3.07 2.71 3.26 Eglington-Pickering 3.98 2.99 2.96 3.51 4.50 3.83 3.70 3.90 4.27 2.68 2.85 3.53 2.70 3.28 3.20 2.70 Eglington-Pickering 4.14 2.47 3.01 3.48 4.16 4.33 4.33 3.82 4.16 1.87 2.76 3.68 3.40 3.57 3.33 3.74 Eglington-Pickering 4.09 3.87 3.01 3.95 3.48 3.59 3.93 3.76 3.86 1.59 3.57 3.29 2.60 2.89 3.59 2.91 Eglington-Pickering 3.71 2.59 3.54 3.43 4.80 4.43 4.30 4.20 3.73 2.78 2.81 3.39 2.95 3.33 2.54 2.91 Eglington-Pickering 3.29 3.49 2.53 4.49 4.84 3.42 3.95 3.94 4.47 1.18 2.32 3.47 2.13 3.00 2.23 2.85 Eglington-Pickering 4.19 3.54 2.74 3.58 3.64 4.85 4.20 3.38 3.93 1.80 3.51 4.94 2.59 3.06 2.90 2.72 Eglington-Pickering 4.24 2.97 3.32 4.06 4.16 4.46 4.08 4.51 3.29 2.11 3.12 4.56 3.16 3.05 2.52 2.92 Eglington-Pickering 3.52 2.73 3.57 4.24 3.53 3.65 4.86 3.20 3.86 2.03 3.36 4.43 2.98 2.74 3.01 2.26 Eglington-Pickering 3.13 2.75 2.46 4.50 4.17 4.10 4.45 4.55 3.76 2.40 3.51 3.68 2.90 2.09 3.33 3.48 Eglington-Pickering 3.94 3.35 2.72 4.16 4.00 3.27 3.81 4.64 4.21 1.87 3.18 4.05 3.52 2.50 2.99 2.62 Eglington-Pickering 3.84 2.40 3.56 3.48 4.33 4.80 4.62 4.33 4.07 2.57 2.70 3.46 3.09 2.87 3.67 2.39 Eglington-Pickering 4.69 2.67 2.70 4.00 3.97 3.68 4.73 4.47 3.91 1.75 2.93 3.92 2.36 2.57 2.91 3.38 Eglington-Pickering 4.12 2.39 3.02 3.72 4.66 3.76 3.84 4.71 4.18 1.25 2.40 3.44 3.60 2.91 2.89 2.99 Eglington-Pickering 3.87 2.68 2.39 4.58 3.99 4.57 3.05 4.01 4.31 1.86 2.83 4.47 3.08 2.61 2.67 3.30 Eglington-Pickering 4.05 2.84 3.40 4.43 3.77 4.19 4.05 3.50 4.85 1.79 2.50 3.71 3.32 3.51 2.85 3.58 Eglington-Pickering 4.24 3.20 3.46 3.74 3.99 3.67 4.65 4.06 3.94 1.92 3.41 4.24 2.92 2.42 3.22 3.56 Eglington-Pickering 4.49 3.37 3.46 4.01 3.99 4.27 3.54 3.93 3.60 1.62 3.44 3.88 3.33 2.84 3.04 2.62 Eglington-Pickering 3.62 3.14 3.06 3.66 3.97 4.10 3.85 4.20 4.41 2.11 2.73 3.39 2.71 3.28 3.11 3.10 Eglington-Pickering 3.67 3.46 2.36 3.96 4.70 4.34 4.08 3.86 3.87 1.79 2.44 3.42 2.74 2.96 3.34 3.04 Eglington-Pickering 4.62 3.25 2.87 3.98 4.33 3.81 3.81 4.13 3.66 2.23 3.13 4.02 3.00 2.63 2.97 3.24 Eglington-Pickering 3.61 3.00 3.56 3.57 3.82 3.52 4.59 3.96 3.92 2.31 3.48 3.91 2.98 3.35 2.18 3.76 Eglington-Pickering 4.23 3.82 2.71 3.84 3.97 3.96 4.63 3.64 3.88 2.09 3.18 3.88 2.31 2.14 2.93 2.37 Eglington-Pickering 3.85 2.63 2.70 4.36 3.35 3.60 4.20 3.73 3.80 2.85 3.54 3.21 3.05 3.26 2.90 2.94 Eglington-Pickering 3.83 2.57 2.23 4.75 4.47 4.14 4.25 3.86 4.61 2.22 2.41 4.32 3.30 2.89 3.65 3.00 Eglington-Pickering 3.10 2.75 3.21 3.71 3.78 4.89 4.46 4.52 3.81 1.89 3.78 4.90 2.85 2.63 2.58 3.72 Eglington-Pickering 4.05 3.02 3.02 4.41 3.14 4.28 3.84 3.47 3.99 2.14 3.20 4.25 3.18 2.75 2.80 2.24 Eglington-Pickering 3.78 3.69 2.96 4.79 3.49 4.27 4.36 3.88 3.59 1.93 2.90 4.48 2.48 2.90 2.90 2.92 Eglington-Pickering 4.49 3.41 3.26 3.38 3.84 3.73 3.90 3.79 4.13 2.07 3.65 4.28 2.79 2.53 2.88 2.83 Eglington-Pickering 4.07 2.38 3.35 3.76 4.56 3.98 4.13 4.08 3.71 2.66 2.54 4.39 3.79 3.32 2.39 2.46 Eglington-Pickering 4.07 3.36 3.63 4.05 4.18 3.86 4.55 4.08 4.73 2.18 2.81 4.25 2.29 2.76 2.64 3.19 Eglington-Pickering 4.26 3.15 2.18 3.99 4.45 4.02 3.40 3.70 4.54 1.99 3.45 3.82 2.48 3.08 2.28 3.52 Eglington-Pickering 3.98 2.64 3.45 3.87 4.35 4.22 3.88 3.91 3.65 2.89 2.99 3.90 2.81 3.16 2.84 2.48 Eglington-Pickering 4.18 3.27 2.58 3.77 3.94 3.81 4.06 3.46 3.99 1.75 3.31 3.18 2.70 2.43 3.37 3.10 Eglington-Pickering 3.92 2.64 2.23 3.51 3.96 4.08 4.08 4.28 4.22 2.26 2.73 3.92 2.49 2.99 3.32 2.94 Eglington-Pickering 4.49 3.17 2.14 4.41 4.08 4.19 3.46 4.03 3.18 1.97 3.46 3.43 2.83 2.61 2.96 3.37 Eglington-Pickering 3.39 2.56 2.78 4.55 3.38 4.58 4.15 3.49 4.17 2.01 2.85 4.31 3.05 2.79 2.51 2.63 Eglington-Pickering 3.83 2.91 3.91 4.17 4.02 3.11 3.91 3.96 4.29 2.39 3.80 4.41 3.25 2.57 3.12 3.04 Eglington-Pickering 4.22 3.90 2.93 4.04 3.61 3.83 4.01 4.02 3.83 1.98 2.68 3.42 3.23 3.35 3.28 3.10 Eglington-Pickering

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Related questions

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Question

The objective of this coding problem is the prediction of a proposed metro extension construction project based on the people's opinion. There are three alternatives to choose they are as follows:

Eglington-Pickering Line
Airport-Vaughan Line
Airport-Hamilton Line

The datasets are available in the "Test_2-Part_B (Coding Data Analytics Problem)" folder under assignment.

Task-1:

Metro-Ext.xlsx is the training and test dataset; you will consider 80% of the data for training and 20% for test. Build a (1) Logistic regression (2) KNN and (3) Naïve Bayes model to predict on the test dataset and compute the confusion matrix for each model and compare the result.

(PLEASE PLACE CHART IN EXCEL BEFORE CODING AND SAVE AS Metro-Ext.xlsx)

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	Alternatives
4.80	3.21	2.81	4.34	4.36	3.60	3.85	4.13	4.37	1.38	2.55	3.78	3.52	3.63	2.95	3.42	Eglington-Pickering
3.94	2.84	2.70	4.88	4.49	4.19	4.91	3.34	4.11	1.96	2.29	4.88	3.49	2.95	2.69	3.42	Eglington-Pickering
3.76	3.39	2.49	4.47	3.52	4.07	3.63	4.00	4.02	2.53	2.87	3.79	2.82	3.18	2.89	3.14	Eglington-Pickering
3.75	2.72	2.85	3.85	4.15	4.56	3.82	4.56	4.18	1.59	2.62	4.35	3.55	2.50	2.35	3.07	Eglington-Pickering
4.75	2.97	2.37	3.93	4.44	4.24	3.90	4.06	3.49	1.90	2.63	3.67	3.44	2.73	2.70	2.98	Eglington-Pickering
3.22	2.28	3.03	4.12	4.08	4.62	3.94	3.12	3.59	1.68	2.97	4.65	2.57	3.33	2.20	2.29	Eglington-Pickering
4.15	3.10	3.32	3.83	4.34	4.01	4.75	3.98	4.26	2.06	2.35	4.22	2.97	3.07	2.71	3.26	Eglington-Pickering
3.98	2.99	2.96	3.51	4.50	3.83	3.70	3.90	4.27	2.68	2.85	3.53	2.70	3.28	3.20	2.70	Eglington-Pickering
4.14	2.47	3.01	3.48	4.16	4.33	4.33	3.82	4.16	1.87	2.76	3.68	3.40	3.57	3.33	3.74	Eglington-Pickering
4.09	3.87	3.01	3.95	3.48	3.59	3.93	3.76	3.86	1.59	3.57	3.29	2.60	2.89	3.59	2.91	Eglington-Pickering
3.71	2.59	3.54	3.43	4.80	4.43	4.30	4.20	3.73	2.78	2.81	3.39	2.95	3.33	2.54	2.91	Eglington-Pickering
3.29	3.49	2.53	4.49	4.84	3.42	3.95	3.94	4.47	1.18	2.32	3.47	2.13	3.00	2.23	2.85	Eglington-Pickering
4.19	3.54	2.74	3.58	3.64	4.85	4.20	3.38	3.93	1.80	3.51	4.94	2.59	3.06	2.90	2.72	Eglington-Pickering
4.24	2.97	3.32	4.06	4.16	4.46	4.08	4.51	3.29	2.11	3.12	4.56	3.16	3.05	2.52	2.92	Eglington-Pickering
3.52	2.73	3.57	4.24	3.53	3.65	4.86	3.20	3.86	2.03	3.36	4.43	2.98	2.74	3.01	2.26	Eglington-Pickering
3.13	2.75	2.46	4.50	4.17	4.10	4.45	4.55	3.76	2.40	3.51	3.68	2.90	2.09	3.33	3.48	Eglington-Pickering
3.94	3.35	2.72	4.16	4.00	3.27	3.81	4.64	4.21	1.87	3.18	4.05	3.52	2.50	2.99	2.62	Eglington-Pickering
3.84	2.40	3.56	3.48	4.33	4.80	4.62	4.33	4.07	2.57	2.70	3.46	3.09	2.87	3.67	2.39	Eglington-Pickering
4.69	2.67	2.70	4.00	3.97	3.68	4.73	4.47	3.91	1.75	2.93	3.92	2.36	2.57	2.91	3.38	Eglington-Pickering
4.12	2.39	3.02	3.72	4.66	3.76	3.84	4.71	4.18	1.25	2.40	3.44	3.60	2.91	2.89	2.99	Eglington-Pickering
3.87	2.68	2.39	4.58	3.99	4.57	3.05	4.01	4.31	1.86	2.83	4.47	3.08	2.61	2.67	3.30	Eglington-Pickering
4.05	2.84	3.40	4.43	3.77	4.19	4.05	3.50	4.85	1.79	2.50	3.71	3.32	3.51	2.85	3.58	Eglington-Pickering
4.24	3.20	3.46	3.74	3.99	3.67	4.65	4.06	3.94	1.92	3.41	4.24	2.92	2.42	3.22	3.56	Eglington-Pickering
4.49	3.37	3.46	4.01	3.99	4.27	3.54	3.93	3.60	1.62	3.44	3.88	3.33	2.84	3.04	2.62	Eglington-Pickering
3.62	3.14	3.06	3.66	3.97	4.10	3.85	4.20	4.41	2.11	2.73	3.39	2.71	3.28	3.11	3.10	Eglington-Pickering
3.67	3.46	2.36	3.96	4.70	4.34	4.08	3.86	3.87	1.79	2.44	3.42	2.74	2.96	3.34	3.04	Eglington-Pickering
4.62	3.25	2.87	3.98	4.33	3.81	3.81	4.13	3.66	2.23	3.13	4.02	3.00	2.63	2.97	3.24	Eglington-Pickering
3.61	3.00	3.56	3.57	3.82	3.52	4.59	3.96	3.92	2.31	3.48	3.91	2.98	3.35	2.18	3.76	Eglington-Pickering
4.23	3.82	2.71	3.84	3.97	3.96	4.63	3.64	3.88	2.09	3.18	3.88	2.31	2.14	2.93	2.37	Eglington-Pickering
3.85	2.63	2.70	4.36	3.35	3.60	4.20	3.73	3.80	2.85	3.54	3.21	3.05	3.26	2.90	2.94	Eglington-Pickering
3.83	2.57	2.23	4.75	4.47	4.14	4.25	3.86	4.61	2.22	2.41	4.32	3.30	2.89	3.65	3.00	Eglington-Pickering
3.10	2.75	3.21	3.71	3.78	4.89	4.46	4.52	3.81	1.89	3.78	4.90	2.85	2.63	2.58	3.72	Eglington-Pickering
4.05	3.02	3.02	4.41	3.14	4.28	3.84	3.47	3.99	2.14	3.20	4.25	3.18	2.75	2.80	2.24	Eglington-Pickering
3.78	3.69	2.96	4.79	3.49	4.27	4.36	3.88	3.59	1.93	2.90	4.48	2.48	2.90	2.90	2.92	Eglington-Pickering
4.49	3.41	3.26	3.38	3.84	3.73	3.90	3.79	4.13	2.07	3.65	4.28	2.79	2.53	2.88	2.83	Eglington-Pickering
4.07	2.38	3.35	3.76	4.56	3.98	4.13	4.08	3.71	2.66	2.54	4.39	3.79	3.32	2.39	2.46	Eglington-Pickering
4.07	3.36	3.63	4.05	4.18	3.86	4.55	4.08	4.73	2.18	2.81	4.25	2.29	2.76	2.64	3.19	Eglington-Pickering
4.26	3.15	2.18	3.99	4.45	4.02	3.40	3.70	4.54	1.99	3.45	3.82	2.48	3.08	2.28	3.52	Eglington-Pickering
3.98	2.64	3.45	3.87	4.35	4.22	3.88	3.91	3.65	2.89	2.99	3.90	2.81	3.16	2.84	2.48	Eglington-Pickering
4.18	3.27	2.58	3.77	3.94	3.81	4.06	3.46	3.99	1.75	3.31	3.18	2.70	2.43	3.37	3.10	Eglington-Pickering
3.92	2.64	2.23	3.51	3.96	4.08	4.08	4.28	4.22	2.26	2.73	3.92	2.49	2.99	3.32	2.94	Eglington-Pickering
4.49	3.17	2.14	4.41	4.08	4.19	3.46	4.03	3.18	1.97	3.46	3.43	2.83	2.61	2.96	3.37	Eglington-Pickering
3.39	2.56	2.78	4.55	3.38	4.58	4.15	3.49	4.17	2.01	2.85	4.31	3.05	2.79	2.51	2.63	Eglington-Pickering
3.83	2.91	3.91	4.17	4.02	3.11	3.91	3.96	4.29	2.39	3.80	4.41	3.25	2.57	3.12	3.04	Eglington-Pickering
4.22	3.90	2.93	4.04	3.61	3.83	4.01	4.02	3.83	1.98	2.68	3.42	3.23	3.35	3.28	3.10	Eglington-Pickering

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Follow-up Question

Task-2:

Metro-Ext-Predection.xlsx is the dataset for prediction purpose to use. Predict the alternatives to chose based on people's opinion given in this dataset for the above three (Logistic Regression, KNN, and Naïve Bayes) models and compare the result.

Deliverable:

a) Coding files (.py )

b) Report (.docx file) - Discussion of the confusion matrix for both the models& Prediction result for both the models

(PLEASE PLACE CHART IN EXCEL BEFORE CODING AND SAVE AS Metro-Ext-Predection)

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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