TMD9 Gates

TMD9 Gates

Week 3:

Task 1: Describe the issues associated with K-means algorithm, and explain the mitigating options.

Task 2: The dataset we are going to use has 1000 entries with unknown number of clusters . Use the Elbow Method to find the optimum value for K and show the results for clustering using the optimum k.

Data:

2.74 3.83

-1.55 -6.31

-11.54 -4.83

4.58 3.84

-3.69 -8.41

-2.48 -10.49

4.03 5.22

-2.36 -9.90

-8.26 -3.20

-9.60 -2.42

-2.26 -8.95

2.54 3.45

-3.80 -10.00

-1.35 -4.06

2.77 3.91

-1.98 -11.03

2.19 2.18

-12.04 -3.94

-1.34 -5.76

-1.36 -8.57

4.03 2.43

-2.47 -8.64

-9.99 -6.09

-10.62 -4.53

-10.43 -4.33

-1.94 -5.58

-2.53 -8.85

2.88 5.90

2.80 3.58

-2.10 -9.21

-10.88 -5.74

-10.34 -3.33

-3.81 -5.61

-8.03 -6.38

-0.92 -6.87

-2.39 -3.51

-2.00 -9.43

-9.47 -4.55

-9.00 -6.01

-2.69 -8.73

-1.33 -9.08

3.78 4.57

-9.58 -3.30

-1.22 -10.95

-8.48 -3.65

-1.60 -10.23

0.74 4.61

0.42 -4.58

3.19 3.58

-1.46 -5.72

-10.68 -5.29

-3.21 -9.12

-3.02 -7.74

-11.03 -5.27

-2.94 -8.26

-2.62 -6.29

-0.40 -9.01

-11.81 -6.11

-10.03 -5.48

5.13 4.22

-4.22 -9.44

-2.01 -9.56

-1.81 -5.85

-1.27 -6.06

-2.83 -9.47

2.55 2.56

3.27 3.65

-2.15 -4.91

-3.00 -10.06

-8.83 -4.97

-3.06 -4.15

-9.11 -4.57

-11.05 -4.62

3.63 4.96

2.06 4.10

-3.73 -9.44

-1.04 -6.32

-8.88 -3.06

-4.16 -11.18

-12.40 -3.43

-3.11 -6.57

-9.89 -3.66

4.43 3.06

-2.81 -7.58

-0.69 -6.57

-0.41 -6.67

4.98 5.85

2.53 3.43

-2.89 -7.40

4.05 3.94

-1.16 -6.42

-3.07 -9.08

-9.59 -4.47

-2.44 -5.90

-1.07 -5.90

-0.94 -4.43

-2.01 -10.95

-9.80 -4.78

-10.36 -4.97

-3.56 -7.53

2.67 6.05

-10.14 -2.52

-9.65 -6.22

-2.49 -8.97

-3.44 -7.78

-2.31 -7.95

4.23 4.46

-2.82 -5.43

1.77 3.98

-11.32 -3.85

1.73 3.62

-1.63 -6.36

-10.53 -4.92

-2.85 -9.56

-2.32 -7.65

-2.85 -8.74

-1.99 -5.19

-10.70 -2.43

2.81 4.83

-2.27 -5.76

-3.24 -8.95

4.11 5.52

2.15 3.83

-0.55 -6.83

-3.64 -8.70

-2.33 -9.05

-10.51 -5.90

-9.21 -4.87

-0.10 -6.23

-10.45 -2.85

-3.28 -9.94

-1.22 -6.26

-10.54 -5.07

-0.68 -9.71

-0.93 -5.89

3.24 4.27

3.09 3.91

-2.65 -9.96

-9.32 -4.24

0.23 -5.65

-2.68 -7.28

-1.41 -7.84

4.62 4.67

1.49 6.26

-2.04 -5.89

-2.25 -8.20

-2.97 -9.02

-7.85 -5.95

-1.71 -7.74

-2.87 -5.10

-3.52 -5.17

-2.09 -8.15

-0.97 -5.80

-1.27 -3.72

-3.20 -8.97

-10.73 -4.00

-8.70 -4.15

-10.45 -2.83

-2.58 -6.73

3.34 3.29

-12.11 -3.89

3.18 5.74

-2.14 -5.52

3.77 5.59

3.87 3.35

3.47 4.45

3.09 3.91

-2.54 -9.47

-9.38 -5.51

-2.55 -8.02

3.69 4.37

-2.06 -9.52

-0.59 -8.26

-9.16 -3.46

-0.02 -6.07

2.84 4.51

-7.77 -4.98

-2.98 -6.04

3.45 4.58

-0.56 -4.65

-2.58 -7.11

-9.26 -5.60

-8.43 -3.49

3.28 3.13

2.00 3.88

3.62 3.19

-2.53 -4.31

-3.58 -5.54

4.13 3.35

-4.14 -8.44

-2.82 -5.24

3.13 4.85

-8.33 -4.53

-2.07 -8.55

2.85 4.82

-3.30 -8.40

-10.74 -5.87

4.86 3.44

0.53 -8.82

-3.34 -7.98

-2.37 -8.52

-3.39 -6.35

-1.26 -4.75

-5.18 -9.70

-1.21 -4.53

-2.44 -7.52

-3.87 -5.97

-9.95 -4.24

-1.45 -6.02

-10.10 -4.74

2.53 3.94

-2.75 -9.95

-10.34 -5.62

-2.28 -6.53

-8.81 -4.43

3.11 5.81

-1.76 -10.15

-1.89 -10.95

1.92 5.09

-9.97 -5.08

-11.00 -4.59

-0.27 -5.28

2.04 5.54

2.96 5.30

-10.38 -4.20

-1.45 -6.35

3.52 5.03

-1.15 -8.94

-2.09 -3.88

2.41 3.10

2.74 6.12

-9.50 -5.59

-0.31 -6.97

-7.87 -4.87

-0.74 -6.01

-2.27 -9.97

-4.30 -8.04

1.30 4.62

-11.42 -4.19

2.99 4.77

-10.34 -4.02

-0.44 -7.32

3.40 4.56

-2.29 -9.57

0.04 -5.23

4.42 2.06

-2.38 -5.34

2.96 4.72

-9.31 -5.34

0.05 -3.91

3.80 3.56

-8.52 -6.77

-3.38 -5.98

2.11 4.94

-10.42 -4.47

-9.28 -5.60

-9.97 -5.62

1.41 2.65

3.50 4.84

4.64 3.64

3.79 5.26

-1.30 -4.79

-8.19 -4.43

4.59 5.05

-3.82 -10.01

2.85 3.71

-2.12 -3.99

-1.78 -10.17

-9.17 -4.86

-0.28 -4.23

-0.62 -7.14

-11.23 -5.31

0.63 2.55

-0.98 -6.22

-9.40 -4.44

-1.43 -9.22

-10.31 -3.88

-8.61 -4.40

-1.91 -4.75

-1.88 -9.20

4.92 4.25

-2.97 -5.65

-8.38 -4.44

-3.01 -8.98

-10.11 -4.09

2.44 4.84

3.84 3.54

-1.08 -9.04

-2.78 -4.43

-2.00 -7.06

3.97 5.15

-1.94 -10.04

-2.59 -10.27

-1.57 -9.16

-3.30 -8.13

-1.61 -9.05

2.81 3.99

-9.74 -4.37

2.84 3.89

3.74 5.99

-10.49 -3.98

-0.87 -8.15

-10.70 -4.47

2.36 4.34

-9.73 -5.48

1.23 5.36

2.48 2.96

-1.61 -6.27

-3.23 -10.24

-10.85 -6.07

-1.80 -5.68

-0.96 -10.44

-9.94 -4.90

-1.41 -5.81

-8.99 -4.87

-3.60 -5.28

-0.17 -5.01

-9.59 -4.10

-2.36 -4.70

1.95 3.65

-9.60 -5.50

4.05 4.97

-7.91 -5.26

4.57 4.10

-2.04 -9.95

-10.26 -5.25

-3.55 -5.16

-11.87 -4.16

2.90 4.83

-10.94 -4.46

-7.21 -5.12

-2.51 -8.06

-2.36 -10.21

-2.64 -7.21

-1.65 -9.51

-9.47 -4.83

0.22 -6.10

-2.69 -9.40

-10.68 -2.31

3.15 4.07

3.55 5.11

-1.08 -4.99

-7.92 -5.03

-2.72 -8.31

-3.06 -10.60

2.15 3.66

-8.67 -4.33

-9.47 -3.17

-2.61 -8.65

-1.75 -7.01

-1.37 -7.42

-1.17 -4.31

2.64 5.35

2.83 3.29

3.16 5.43

4.60 3.99

-9.75 -5.81

-11.92 -3.26

-10.85 -4.94

4.22 5.21

-2.85 -4.37

3.11 5.09

-1.41 -6.19

-11.79 -4.91

-1.69 -8.40

-2.80 -10.13

-1.84 -6.39

-1.38 -9.16

-0.36 -4.40

-2.33 -8.02

-2.00 -9.78

-9.35 -4.91

1.92 5.02

-0.11 -7.06

3.83 4.17

1.90 5.23

-3.19 -7.05

-10.51 -7.47

-2.47 -6.62

2.28 3.90

-10.87 -4.26

-3.32 -9.49

-0.97 -11.01

-1.89 -9.06

-9.55 -4.44

-2.56 -9.80

-2.04 -6.35

-1.28 -6.12

-9.11 -5.57

3.90 4.18

5.12 3.18

3.88 5.06

3.77 5.94

-3.52 -8.83

-10.22 -4.76

3.40 4.35

-2.32 -9.83

3.68 3.89

-2.50 -5.58

-2.84 -5.88

-0.92 -6.82

4.36 5.25

-1.74 -6.42

-2.16 -9.87

4.75 6.17

-1.81 -8.81

0.09 -4.85

-2.35 -6.31

-1.60 -4.79

3.32 6.44

-1.79 -10.27

4.30 4.94

-3.02 -8.98

-9.33 -3.47

-10.80 -6.86

-1.31 -9.07

-3.54 -9.18

-9.78 -3.64

-10.23 -3.64

-9.75 -4.74

-2.73 -4.64

-3.47 -10.14

-2.48 -6.13

-9.83 -5.46

3.47 3.89

2.89 4.35

-1.94 -11.09

-2.46 -10.73

-3.41 -11.73

-2.69 -3.58

-9.28 -4.63

2.97 5.61

-2.97 -7.02

2.84 5.44

-1.95 -6.65

3.48 4.12

-11.35 -5.19

-2.04 -5.70

-10.18 -2.64

-1.23 -5.33

-9.61 -4.01

-1.69 -9.11

-2.80 -5.18

-2.64 -9.91

2.28 3.81

-0.62 -10.83

-11.87 -6.47

1.89 4.08

-9.73 -4.09

-8.04 -5.15

-1.86 -3.94

-1.77 -9.00

-1.92 -5.44

-0.49 -10.30

-8.64 -3.87

-2.75 -4.86

-3.41 -9.61

-1.38 -6.39

-1.80 -10.13

2.35 3.38

-2.84 -6.35

-3.24 -8.63

1.79 4.24

-1.80 -8.96

4.21 3.05

1.21 4.03

-7.27 -4.88

5.47 3.83

-10.94 -3.79

-8.24 -5.28

-9.84 -4.09

-10.96 -4.17

3.58 3.05

-3.62 -9.36

3.34 5.15

-3.72 -6.40

-4.72 -10.36

-1.23 -10.76

3.26 4.11

2.44 3.48

-2.30 -5.72

-1.28 -4.98

-2.72 -10.64

-3.31 -8.48

-2.06 -5.43

3.23 5.63

-2.34 -9.38

-3.04 -3.78

-2.02 -8.80

3.84 3.96

3.36 5.22

-0.37 -4.50

3.63 4.72

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3.24 4.16

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5.00 3.81

-2.16 -3.53

-1.82 -5.48

1.71 4.82

3.14 3.44

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-8.13 -3.59

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3.34 2.95

-9.53 -5.41

2.51 3.00

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3.88 4.31

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2.74 3.88

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-9.57 -5.43

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4.14 3.09

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-2.79 -5.45

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1.59 6.65

-0.30 -5.30

3.71 4.79

-9.82 -4.27

-10.57 -5.10

3.58 5.60

2.26 5.72

-2.26 -9.28

-9.70 -4.17

-0.55 -5.95

3.20 4.81

-9.89 -3.77

-10.17 -4.11

-1.23 -9.91

-0.76 -5.65

2.06 5.04

-3.08 -10.97

-9.26 -5.37

-1.67 -9.72

4.00 3.78

-1.75 -4.54

4.71 3.59

2.50 3.68

-2.18 -4.79

-10.68 -6.11

-1.74 -4.42

-3.73 -9.72

-1.65 -6.16

-11.10 -5.65

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-0.52 -3.77

-7.61 -6.34

-3.08 -8.21

-2.39 -4.58

-10.05 -3.62

-10.18 -5.81

-1.46 -9.88

-1.44 -9.19

-1.43 -6.01

-1.49 -4.61

-1.43 -9.25

-10.48 -2.26

4.03 3.64

-3.73 -3.70

-0.88 -5.40

-2.43 -5.66

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-10.22 -4.45

3.32 5.23

-0.45 -5.73

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2.68 4.04

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3.66 4.20

3.54 3.24

4.17 5.82

-2.96 -7.35

-9.68 -2.87

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4.71 5.94

3.39 5.84

-8.11 -4.22

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3.98 5.67

-0.65 -8.37

-9.89 -4.42

-9.69 -3.76

3.12 2.72

-1.08 -9.31

-2.45 -8.55

3.38 4.64

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-1.81 -9.77

-9.98 -5.39

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2.49 4.67

-2.76 -4.18

1.14 5.87

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4.08 5.62

-3.43 -8.92

-1.50 -6.12

-9.02 -5.61

4.09 4.63

3.70 4.51

4.01 3.64

3.93 5.50

-8.71 -5.91

-9.09 -2.61

-2.07 -9.15

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-4.62 -8.70

0.73 -6.52

-3.13 -4.23

-7.72 -4.67

4.51 3.67

-9.94 -2.69

3.48 4.41

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-9.68 -3.38

-8.84 -2.73

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-10.22 -3.92

-2.30 -9.03

3.75 4.86

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-9.81 -4.88

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3.35 2.70

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-2.10 -3.37

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-9.98 -5.71

2.27 4.53

-2.24 -6.82

-10.25 -4.72

-1.18 -10.56

-2.04 -12.24

2.78 4.21

2.69 5.06

-1.42 -6.46

-2.87 -4.82

2.82 6.33

-10.11 -5.02

-10.12 -5.46

-2.10 -10.11

4.30 4.16

-2.43 -6.50

-1.03 -6.57

3.94 6.18

-0.22 -5.05

-9.45 -3.14

-3.63 -10.01

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-4.09 -6.54

1.78 5.65

-9.65 -4.55

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-0.74 -6.66

-2.76 -10.02

3.81 5.97

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-2.41 -6.94

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3.31 5.69

-3.01 -4.48

-2.75 -7.85

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0.24 -5.28

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-9.46 -4.16

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2.84 3.71

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-8.26 -3.55

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3.03 4.18

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2.76 5.39

-9.14 -4.90

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1.50 6.46

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-9.92 -4.49

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3.15 5.71

3.48 5.83

-9.72 -4.56

-10.33 -4.60

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3.06 3.34

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5.05 5.16

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3.50 3.67

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3.12 3.16

-9.02 -4.04

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-9.25 -6.57

-4.07 -6.92

4.22 6.18

2.46 4.39

-2.67 -5.78

3.55 3.63

-11.24 -3.61

-2.88 -4.73

3.63 5.51

-1.87 -7.22

-10.14 -4.71

-8.41 -4.94

2.79 4.09

2.73 3.05

3.48 4.99

-1.16 -5.08

-9.01 -3.10

-3.10 -9.41

-8.22 -4.50

2.64 3.69

-9.34 -5.88

-8.11 -5.13

-1.12 -4.13

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-3.48 -5.82

-2.28 -6.37

-2.75 -8.18

-9.92 -3.70

-2.54 -11.31

-1.52 -7.37

2.87 4.21

-9.42 -5.31

-2.68 -4.76

1.40 3.99

3.18 3.99

-2.53 -10.88

-2.12 -10.32

-1.22 -9.38

3.87 4.25

-3.14 -9.51

2.77 4.18

-3.09 -9.23

-2.44 -5.48

3.01 4.44

5.36 5.71

3.69 4.37

-8.71 -5.12

-8.52 -3.44

-1.88 -5.43

-1.03 -4.78

-0.81 -9.68

-9.77 -4.57

-10.86 -2.88

-12.05 -5.51

-9.19 -5.09

4.31 3.16

-1.96 -6.40

-2.27 -8.65

-9.14 -4.46

-2.49 -4.91

-0.92 -5.97

-2.85 -9.69

2.81 6.61

3.82 5.77

1.93 3.54

-11.50 -5.62

-3.63 -9.93

-2.20 -8.18

-10.73 -4.75

-8.29 -2.92

-10.87 -2.28

-2.52 -5.83

-3.78 -10.28

4.46 4.54

3.67 4.68

4.55 4.40

-2.00 -3.47

2.46 4.75

2.49 3.99

-10.01 -3.68

3.68 2.77

-1.30 -6.69

-2.89 -5.78

-2.74 -7.66

2.50 4.83

-3.42 -9.62

4.41 6.85

-1.49 -8.82

-8.36 -2.58

3.47 1.66

-2.39 -5.48

-9.00 -4.41

-2.65 -6.27

-10.05 -3.35

-2.38 -5.28

-0.88 -9.68

4.45 3.27

3.88 5.08

-2.05 -9.73

-1.95 -4.32

-2.52 -7.40

-2.17 -9.40

-9.70 -6.20

-2.93 -8.20

-2.59 -9.97

-4.06 -9.74

3.30 4.16

-8.93 -4.09

-2.41 -8.55

-2.93 -7.37

-11.00 -6.94

-2.45 -9.26

-9.85 -5.98

2.84 4.79

1.93 4.61

-2.73 -9.41

-10.09 -4.29

3.51 5.23

-9.05 -3.37

-0.93 -8.42

1.82 6.14

2.45 6.22

-9.66 -3.44

-2.97 -10.14

3.53 3.31

-2.59 -6.69

4.98 2.85

-2.42 -10.45

-0.63 -6.16

-8.62 -4.65

-10.28 -4.50

-2.99 -5.82

-2.04 -9.53

-2.28 -9.13

-10.70 -2.39

-2.16 -6.29

-9.91 -4.59

-1.82 -4.81

4.95 4.58

-2.78 -8.48

3.82 4.42

-1.33 -6.74

4.48 3.68

-0.98 -8.53

-3.04 -8.55

-0.07 -6.09

-2.49 -6.24

-9.85 -4.92

-9.71 -4.73

-9.93 -3.26

-1.44 -5.69

3.75 4.79

-0.19 -4.26

-0.72 -6.08

4.56 3.64

-1.99 -5.49

3.03 3.18

-2.07 -9.96

-3.54 -10.46

-10.00 -3.71

-8.59 -5.85

-2.02 -9.49

-3.29 -10.89

-3.30 -9.22

-1.89 -9.28

-1.30 -9.39

-1.82 -7.95

-0.24 -4.32

-10.41 -2.52

3.10 3.93

-8.50 -4.99

-2.66 -10.05

-4.59 -8.49

-1.14 -5.53

-10.29 -6.04

-2.93 -6.71

-4.49 -8.17

-11.39 -6.24

-3.52 -4.67

-9.99 -6.00

-9.02 -5.28

-9.73 -7.61

-1.63 -8.95

-1.72 -4.86

-2.46 -9.74

-2.22 -5.26

-9.53 -5.55

-3.40 -10.47

-8.60 -4.60

-1.94 -9.28

-9.82 -4.58

-0.76 -8.95

-8.91 -5.82

-2.01 -6.36

-9.36 -4.35

-1.94 -11.03

-10.32 -5.44

-7.94 -3.88

2.40 5.72

-9.52 -5.20

-1.54 -6.91

-0.69 -6.26

-8.21 -3.71

4.01 4.21

-9.87 -4.96

-1.74 -4.96

5.31 4.28

-2.90 -9.30

-9.69 -3.54

-9.24 -4.21

-3.38 -8.76

3.70 4.12

2.79 4.65

-9.22 -4.89

-1.83 -5.76

-2.82 -9.01

-2.86 -10.22

2.43 4.46

-2.90 -6.44

-2.57 -5.88

-2.83 -7.71

-1.66 -6.33

3.62 5.29

-3.94 -9.85

-10.16 -3.47

-3.32 -6.13

-11.94 -2.71

1.87 3.90

-8.81 -4.82

-1.80 -4.75

2.74 3.91

2.23 5.02

4.43 5.31

-11.13 -5.32

-1.31 -11.30

3.68 5.30

-3.49 -4.45

3.63 5.81

-1.96 -8.25

week 4: Let us assume we developed a machine learning model to predict which patients on dialysis will be admitted to the hospital in the next week. Given 100 patients, we have the following break-down:

• True Positives: people that are hospitalized that you predict will be hospitalized

• True Negatives: people that are NOT hospitalized that you predict will NOT be hospitalized

• False Positives: people that are NOT hospitalized that you predict will be hospitalized

• False Negatives: people that are hospitalized that you predict will NOT be hospitalized

Calculate the performance metrics in the table below:

Task 2: Try K-NN classifier for dataset provided in for different numbers of neighbours (K = 3 and K = 7). Which K provides better results? Justify your answer by using AUC of ROC curve. Also provide the relevant performance metrics such sensitivity, etc.

Task 3: The scrip also retunes a value called ‘f1_score’. Do some research to understand what this measure is. Repeat task-2 and this time justify your answer using f1_score. Do you arrive at the same conclusion?

Week 5:

Task 1

Try Random Forest Classifier for the Iris dataset example, and when creating decision trees from the bootstrapped dataset, use different number of features:

• a subset of 2 features

• a subset of 3 features

• a subset of 4 features

And compare the accuracy of your Random Forest Classifier. Which one is better? Discuss your observations and justify your answer.

What is the largest number of features you may have?

Task 2: You have been given a binary classification problem (positive/negative) where the original dataset contains 29 positive and 35 negative samples. We have 2 features of A1 and A2 which can be used for splitting the data. We would like to build a decision tree and figure below provides the resulting splits for each feature. Which feature would you use to split the data? A1 or A2? Justify your answer.

Tip: use information gain.

Week 6:

Task 1: data perception:

0.47 3.87 0.00

2.84 3.33 0.00

0.61 2.51 0.00

3.82 1.65 1.00

1.28 0.63 1.00

0.99 5.87 0.00

1.05 -0.10 1.00

0.91 4.20 0.00

0.88 3.64 0.00

0.91 -0.41 1.00

0.89 4.50 0.00

3.09 1.38 1.00

1.28 1.78 1.00

2.61 -0.17 1.00

-0.97 3.39 0.00

-0.07 2.88 0.00

4.51 0.69 1.00

0.51 1.43 1.00

3.17 0.38 1.00

1.73 0.89 1.00

1.42 6.00 0.00

-0.36 2.34 0.00

1.04 0.30 1.00

1.29 4.82 0.00

0.35 3.82 0.00

1.10 1.91 1.00

0.16 2.90 0.00

0.23 3.48 0.00

0.84 4.01 0.00

1.16 3.92 0.00

2.73 2.20 1.00

1.71 0.64 1.00

0.38 4.07 0.00

0.98 4.83 0.00

1.73 5.17 0.00

-0.30 3.72 0.00

0.78 4.40 0.00

3.34 0.01 1.00

0.58 4.67 0.00

2.28 1.10 1.00

2.98 0.77 1.00

0.26 3.49 0.00

0.51 4.79 0.00

3.45 -0.74 1.00

2.25 -0.26 1.00

1.95 -0.15 1.00

2.66 -0.73 1.00

1.30 1.20 1.00

0.16 2.58 0.00

2.27 -0.34 1.00

0.86 0.40 1.00

2.20 -0.08 1.00

2.01 1.14 1.00

2.10 4.80 0.00

3.45 1.94 1.00

-0.96 4.49 0.00

1.85 4.58 0.00

2.19 -0.51 1.00

3.62 -0.04 1.00

1.53 0.79 1.00

1.22 3.93 0.00

1.91 4.64 0.00

2.10 2.76 1.00

1.50 4.13 0.00

1.00 0.34 1.00

1.14 4.40 0.00

2.10 0.57 1.00

0.30 5.60 0.00

2.21 1.94 1.00

2.91 0.06 1.00

0.68 -0.17 1.00

1.20 4.63 0.00

2.12 0.91 1.00

2.01 0.29 1.00

2.08 1.51 1.00

1.27 5.13 0.00

2.47 4.10 0.00

-1.68 4.91 0.00

1.31 5.25 0.00

0.84 5.38 0.00

0.34 3.94 0.00

0.54 4.02 0.00

2.01 3.31 0.00

2.72 5.30 0.00

2.52 2.61 1.00

2.15 1.28 1.00

1.62 2.73 0.00

2.20 2.73 1.00

2.55 1.38 1.00

-0.47 3.09 0.00

1.75 0.24 1.00

3.14 5.64 0.00

1.01 4.23 0.00

1.70 4.33 0.00

1.75 5.13 0.00

2.12 0.43 1.00

0.71 5.11 0.00

0.33 2.70 1.00

0.24 5.85 0.00

0.57 -0.41 1.00

-1.28 3.28 0.00

2.09 1.41 1.00

1.83 3.65 0.00

-0.09 5.36 0.00

-0.08 5.12 0.00

-0.98 3.64 0.00

0.55 3.47 0.00

3.10 1.50 1.00

1.62 2.69 0.00

1.65 0.10 1.00

-0.85 4.61 0.00

1.93 0.63 1.00

0.23 0.20 1.00

1.74 4.66 0.00

0.54 6.15 0.00

3.33 0.19 1.00

1.27 5.63 0.00

1.25 3.41 0.00

2.37 1.20 1.00

4.74 0.70 1.00

1.77 -0.26 1.00

0.85 5.44 0.00

1.02 4.12 0.00

2.38 3.89 0.00

1.91 5.72 0.00

0.57 -0.37 1.00

-0.79 4.66 0.00

5.23 1.09 1.00

1.31 6.53 0.00

1.26 4.21 0.00

2.45 2.49 1.00

0.61 4.06 0.00

1.93 4.39 0.00

-0.36 2.96 0.00

4.38 1.01 1.00

-0.72 2.05 1.00

2.41 0.42 1.00

1.96 -0.15 1.00

2.93 -0.93 1.00

0.42 3.83 0.00

0.04 3.04 0.00

-0.34 3.84 0.00

-0.06 2.30 1.00

2.07 3.10 1.00

2.65 0.64 1.00

-0.07 5.51 0.00

3.10 1.06 1.00

2.83 1.33 1.00

2.44 2.07 1.00

1.76 3.84 0.00

1.72 3.11 0.00

1.92 4.15 0.00

2.52 0.50 1.00

2.34 1.31 1.00

2.62 4.47 0.00

0.21 4.84 0.00

0.21 4.86 0.00

2.37 0.76 1.00

0.26 -0.17 1.00

1.42 4.48 0.00

2.47 2.23 0.00

-0.15 3.57 0.00

1.60 2.70 0.00

-0.18 5.08 0.00

1.53 1.94 1.00

4.68 0.15 1.00

0.87 5.32 0.00

0.53 3.65 0.00

-0.10 2.22 1.00

1.65 3.60 0.00

1.85 4.19 0.00

0.39 0.77 1.00

2.66 0.30 1.00

2.02 -0.63 1.00

0.79 3.91 0.00

1.75 0.46 1.00

-1.39 5.17 0.00

-0.61 4.91 0.00

0.31 4.63 0.00

0.84 5.44 0.00

1.72 0.18 1.00

0.86 3.62 0.00

1.92 4.04 0.00

0.04 1.49 1.00

0.70 3.59 0.00

3.96 0.88 1.00

0.92 1.89 1.00

2.99 -0.49 1.00

0.41 4.57 0.00

0.71 2.16 1.00

0.08 5.62 0.00

1.72 2.93 1.00

3.67 0.07 1.00

0.67 0.36 1.00

0.58 3.14 0.00

1.08 0.31 1.00

2.46 0.89 1.00

2.70 1.49 1.00

3.66 0.71 1.00

1.50 3.73 0.00

2.47 -0.34 1.00

2.99 1.58 1.00

1.09 3.92 0.00

0.57 5.53 0.00

2.72 -1.64 1.00

3.36 0.87 1.00

2.77 0.65 1.00

3.02 0.48 1.00

2.86 2.96 0.00

-0.24 4.00 0.00

0.45 3.12 0.00

2.71 4.99 0.00

-1.03 4.68 0.00

1.40 0.98 1.00

0.96 4.68 0.00

1.75 5.33 0.00

0.77 5.18 0.00

2.10 -0.06 1.00

0.92 4.04 0.00

3.46 1.06 1.00

2.97 3.11 1.00

0.87 2.64 0.00

1.67 4.14 0.00

0.63 3.51 0.00

-0.28 5.08 0.00

-1.58 4.96 0.00

1.65 1.85 1.00

1.80 0.55 1.00

3.35 1.51 1.00

2.88 1.43 1.00

1.77 -1.10 1.00

0.27 5.25 0.00

1.59 1.77 1.00

2.06 3.67 0.00

1.41 1.07 1.00

2.30 4.20 0.00

2.07 -0.95 1.00

0.69 0.88 1.00

0.48 4.33 0.00

0.44 0.39 1.00

1.09 1.37 1.00

1.33 5.01 0.00

1.65 3.92 0.00

1.08 2.32 1.00

0.38 5.43 0.00

2.20 4.37 0.00

2.14 0.08 1.00

1.58 1.17 1.00

0.57 5.03 0.00

2.30 1.06 1.00

1.24 2.70 1.00

2.14 1.84 1.00

1.07 4.89 0.00

-0.45 6.30 0.00

0.59 4.00 0.00

0.29 4.19 0.00

1.01 0.04 1.00

1.40 4.98 0.00

-0.71 4.19 0.00

1.26 -0.01 1.00

1.87 1.79 1.00

1.38 1.53 0.00

2.65 -0.09 1.00

3.06 1.49 1.00

1.58 0.37 1.00

0.99 5.02 0.00

2.04 3.85 0.00

1.06 0.16 1.00

2.50 1.12 1.00

0.37 1.70 1.00

1.70 1.44 1.00

0.74 2.31 1.00

1.02 2.74 0.00

3.48 1.35 1.00

0.85 4.40 0.00

2.93 1.53 1.00

2.39 0.75 1.00

3.46 1.23 1.00

-0.87 3.83 0.00

1.43 4.40 0.00

1.67 0.84 1.00

2.93 4.69 0.00

1.43 0.51 1.00

2.79 0.83 1.00

0.31 5.99 0.00

1.62 2.05 1.00

0.62 2.69 0.00

2.25 1.00 1.00

2.62 1.04 1.00

2.62 0.56 1.00

0.76 4.75 0.00

1.22 3.42 0.00

0.50 4.92 0.00

3.04 0.81 1.00

2.99 4.26 0.00

3.02 0.17 1.00

2.81 1.46 1.00

2.94 1.93 1.00

1.06 -0.11 1.00

0.85 1.31 1.00

2.07 4.07 0.00

3.28 3.24 0.00

2.39 5.09 0.00

1.74 4.43 0.00

-0.29 5.27 0.00

0.02 3.96 0.00

2.59 0.76 1.00

1.58 5.20 0.00

3.20 -0.01 1.00

0.09 2.32 0.00

2.04 0.41 1.00

1.18 0.67 1.00

3.40 0.16 1.00

0.63 2.82 1.00

1.55 4.10 0.00

0.47 3.14 1.00

-0.37 3.54 0.00

2.48 1.45 1.00

2.35 3.79 0.00

0.03 3.89 0.00

2.47 0.39 1.00

2.30 2.42 1.00

-0.38 4.26 0.00

1.89 0.76 1.00

0.73 5.82 0.00

2.09 0.86 1.00

2.93 0.02 1.00

1.90 1.95 1.00

2.27 2.44 1.00

1.04 4.09 0.00

0.28 4.15 0.00

2.31 1.95 1.00

-0.33 5.96 0.00

1.28 2.93 0.00

0.06 5.42 0.00

2.14 2.32 1.00

0.32 3.79 0.00

1.20 3.29 0.00

0.96 5.13 0.00

2.52 0.80 1.00

3.32 3.02 0.00

2.05 1.74 0.00

0.94 0.86 1.00

1.19 3.10 0.00

0.57 3.44 0.00

0.76 3.22 0.00

-0.20 6.25 0.00

1.71 0.92 1.00

1.52 -0.06 1.00

2.88 0.60 1.00

1.89 0.81 1.00

0.68 3.54 0.00

0.87 4.71 0.00

1.67 5.61 0.00

0.07 3.88 0.00

1.59 2.96 0.00

1.06 2.94 1.00

1.60 0.98 1.00

3.57 3.90 0.00

2.30 2.03 1.00

3.86 1.33 1.00

1.67 5.00 0.00

0.36 1.63 1.00

0.77 4.19 0.00

2.26 1.43 1.00

1.02 -0.53 1.00

2.47 0.86 1.00

1.65 0.58 1.00

-1.04 3.76 0.00

2.09 1.52 1.00

0.13 3.98 0.00

0.32 1.49 1.00

2.13 5.38 0.00

0.12 3.40 0.00

2.12 1.68 1.00

2.16 3.67 0.00

1.57 0.62 1.00

2.06 0.13 1.00

2.36 4.00 0.00

2.22 2.46 1.00

1.85 2.47 1.00

3.11 -0.12 1.00

0.87 3.32 0.00

0.43 0.76 1.00

2.48 1.14 1.00

0.70 4.04 0.00

2.21 0.68 1.00

2.40 4.70 0.00

0.06 4.56 0.00

3.37 0.57 1.00

3.16 1.30 1.00

0.83 3.92 0.00

0.42 0.16 1.00

3.46 1.04 1.00

2.26 1.21 1.00

1.27 1.52 1.00

0.52 4.32 0.00

0.25 2.92 0.00

2.21 5.51 0.00

1.89 1.28 1.00

1.60 2.32 1.00

0.82 6.56 0.00

2.49 0.30 1.00

1.33 -0.22 1.00

1.61 -0.85 1.00

2.44 5.16 0.00

-1.05 6.37 0.00

-0.17 3.95 0.00

0.58 1.26 0.00

1.34 4.49 0.00

1.10 -0.45 1.00

1.31 0.29 1.00

1.47 0.45 1.00

2.07 0.30 1.00

1.22 -0.05 1.00

-0.72 4.69 0.00

0.57 2.39 1.00

1.91 -0.33 1.00

0.96 4.46 0.00

0.21 2.87 0.00

1.16 -1.07 1.00

2.76 -0.60 1.00

1.65 0.43 1.00

2.59 1.22 1.00

1.69 -0.06 1.00

1.55 4.65 0.00

-0.75 -0.29 1.00

1.75 3.12 0.00

1.02 6.64 0.00

2.51 1.18 1.00

1.47 4.28 0.00

1.22 1.89 1.00

1.82 0.65 1.00

2.35 1.81 1.00

4.20 0.81 1.00

1.44 4.58 0.00

0.92 3.99 0.00

1.10 4.71 0.00

2.10 3.22 0.00

3.25 2.85 0.00

1.89 1.67 1.00

0.95 0.87 1.00

-0.45 3.81 0.00

4.23 1.56 1.00

3.11 1.85 1.00

1.90 0.07 1.00

2.86 -0.30 1.00

1.57 3.94 0.00

2.31 0.49 1.00

-1.26 4.71 0.00

1.78 4.38 0.00

0.02 3.51 0.00

1.63 0.26 1.00

0.14 3.71 0.00

3.08 1.61 1.00

3.06 1.43 1.00

3.05 0.71 1.00

1.91 4.13 0.00

0.93 4.02 0.00

3.01 1.44 1.00

1.35 3.20 0.00

0.54 4.49 0.00

-0.05 3.95 0.00

0.59 4.40 0.00

1.59 5.23 0.00

0.38 5.86 0.00

2.09 0.04 1.00

2.30 3.42 0.00

2.22 -0.05 1.00

0.33 2.08 0.00

-0.16 5.09 0.00

0.19 4.19 0.00

3.31 -1.19 1.00

3.72 1.20 1.00

3.02 1.41 1.00

3.17 -0.67 1.00

2.68 1.79 1.00

0.55 0.62 1.00

3.38 0.73 1.00

0.18 3.77 0.00

1.85 0.22 1.00

3.83 0.50 1.00

2.40 1.56 1.00

-1.39 4.32 0.00

2.93 2.10 1.00

1.82 4.05 0.00

2.75 0.60 1.00

0.16 2.84 0.00

1.20 5.69 0.00

0.61 0.45 1.00

2.06 1.78 1.00

2.03 0.78 1.00

1.84 3.56 0.00

2.90 0.22 1.00

1.26 1.98 0.00

2.07 1.95 1.00

2.96 2.70 1.00

1.26 0.68 1.00

1.20 4.70 0.00

1.45 -0.25 1.00

0.75 3.44 0.00

1.54 4.08 0.00

1.46 -0.62 1.00

-0.20 5.61 0.00

1.44 6.04 0.00

1.40 1.11 1.00

0.16 0.72 1.00

-0.32 5.58 0.00

1.14 4.94 0.00

1.25 3.50 0.00

2.10 1.49 1.00

3.88 0.82 1.00

1.22 1.07 1.00

0.29 5.96 0.00

2.09 0.32 1.00

1.90 4.62 0.00

-0.64 4.09 0.00

2.82 0.48 1.00

1.34 5.60 0.00

1.26 3.31 0.00

1.38 4.11 0.00

0.30 4.34 0.00

3.04 1.66 1.00

3.28 0.70 1.00

3.64 1.55 1.00

1.75 0.54 1.00

0.74 1.44 1.00

0.80 0.93 1.00

1.60 1.27 1.00

0.95 0.87 1.00

0.34 4.98 0.00

2.83 0.61 1.00

0.61 5.24 0.00

2.73 4.17 0.00

0.32 4.48 0.00

-0.01 2.83 0.00

1.13 4.54 0.00

1.03 2.51 0.00

1.04 4.61 0.00

2.60 1.61 1.00

0.42 -0.88 1.00

1.59 0.04 1.00

0.75 3.39 0.00

3.70 -0.65 1.00

0.95 4.73 0.00

2.26 2.06 1.00

1.99 1.18 1.00

2.91 0.09 1.00

0.61 3.36 0.00

1.04 0.98 1.00

2.73 0.95 1.00

0.84 4.71 0.00

0.63 3.72 0.00

0.51 2.10 0.00

2.24 2.60 1.00

3.63 0.10 1.00

3.66 0.34 1.00

0.51 4.09 0.00

2.49 4.86 0.00

0.12 4.33 0.00

2.83 0.77 1.00

1.64 -0.20 1.00

2.02 1.23 1.00

0.60 1.28 1.00

2.07 0.82 1.00

1.79 4.36 0.00

1.01 -0.19 1.00

3.27 -0.51 1.00

2.44 1.91 1.00

0.04 5.32 0.00

0.86 4.61 0.00

0.47 6.72 0.00

3.47 2.06 0.00

1.43 2.47 0.00

1.65 4.71 0.00

0.12 2.76 0.00

1.38 0.33 1.00

2.27 -0.11 1.00

2.50 0.28 1.00

0.43 2.42 0.00

1.71 4.43 0.00

0.07 4.36 0.00

2.87 0.62 1.00

1.88 0.39 1.00

1.29 3.45 0.00

2.09 0.81 1.00

0.67 4.07 0.00

3.40 1.81 1.00

2.08 4.96 0.00

1.24 0.38 1.00

1.71 -0.37 1.00

2.25 1.59 1.00

1.44 0.07 1.00

1.56 2.11 1.00

1.65 -0.57 1.00

0.18 4.54 0.00

1.54 3.02 0.00

0.94 3.14 0.00

0.92 -0.32 1.00

1.80 4.31 0.00

3.41 0.50 1.00

2.94 2.37 1.00

1.33 4.68 0.00

1.79 4.89 0.00

1.13 4.68 0.00

1.01 0.58 1.00

3.49 2.36 1.00

2.51 5.77 0.00

1.68 3.24 0.00

1.20 1.23 1.00

1.05 2.57 1.00

0.95 3.57 0.00

3.67 4.23 0.00

0.95 5.38 0.00

1.30 -0.30 1.00

1.47 1.23 1.00

4.02 0.70 1.00

2.01 2.26 0.00

-0.12 0.27 1.00

0.46 0.90 1.00

2.50 1.00 1.00

1.91 4.27 0.00

-0.25 5.15 0.00

3.92 1.66 1.00

2.46 2.10 1.00

0.45 -0.48 1.00

1.08 2.24 1.00

1.69 1.51 1.00

1.93 4.15 0.00

-0.32 4.57 0.00

-0.25 5.27 0.00

0.55 4.41 0.00

0.03 4.55 0.00

1.24 -0.55 1.00

4.58 2.67 1.00

0.87 4.32 0.00

3.24 4.26 0.00

0.51 2.89 0.00

-0.13 4.36 0.00

1.50 4.39 0.00

3.36 5.25 0.00

4.21 1.37 1.00

2.05 2.14 1.00

-0.94 1.78 1.00

0.29 3.09 0.00

1.49 0.59 1.00

1.44 3.93 0.00

1.90 0.96 1.00

2.28 2.51 1.00

0.48 6.23 0.00

3.07 1.60 1.00

1.66 1.68 1.00

1.64 3.84 0.00

2.35 0.94 1.00

1.70 3.44 1.00

1.98 -0.40 1.00

0.62 2.93 0.00

0.48 4.23 0.00

1.73 0.19 1.00

-0.65 4.77 0.00

2.66 -0.15 1.00

0.91 0.94 1.00

1.85 -0.82 1.00

0.67 2.63 0.00

2.21 0.64 1.00

3.20 1.24 1.00

0.52 4.57 0.00

0.01 4.62 0.00

1.34 4.16 0.00

1.91 0.99 1.00

0.12 6.13 0.00

2.02 -0.05 1.00

1.42 4.64 0.00

1.45 4.45 0.00

1.22 -0.47 1.00

1.32 6.32 0.00

0.03 4.54 0.00

0.71 4.12 0.00

1.97 0.33 1.00

2.28 0.81 1.00

1.03 4.80 0.00

2.68 0.27 1.00

0.80 0.31 1.00

-0.38 2.01 1.00

2.08 5.60 0.00

2.16 1.44 1.00

1.32 -0.74 1.00

2.99 4.00 0.00

0.73 3.43 0.00

1.47 5.35 0.00

0.59 3.86 0.00

1.84 3.50 0.00

2.55 0.16 1.00

2.56 1.26 1.00

0.99 6.09 0.00

1.83 5.44 0.00

2.74 0.95 1.00

1.34 5.78 0.00

0.41 2.30 1.00

-0.17 3.87 0.00

1.17 5.39 0.00

0.90 4.56 0.00

2.92 1.08 1.00

0.93 4.52 0.00

-0.66 2.74 0.00

1.33 4.92 0.00

2.50 6.02 0.00

1.05 3.23 0.00

2.25 1.78 1.00

1.07 -0.00 1.00

0.60 3.08 0.00

1.99 2.16 1.00

2.16 4.62 0.00

3.64 0.25 1.00

0.21 3.42 0.00

0.28 4.01 0.00

2.12 0.63 1.00

0.44 5.39 0.00

2.98 1.46 1.00

1.45 4.62 0.00

1.12 5.76 0.00

1.99 0.19 1.00

0.17 3.19 0.00

0.66 3.46 0.00

1.30 1.04 1.00

0.66 4.36 0.00

-0.02 2.76 0.00

2.25 2.89 1.00

1.01 5.07 0.00

1.38 3.62 0.00

1.67 4.31 0.00

1.92 4.71 0.00

1.76 -0.84 1.00

0.05 4.13 0.00

0.32 3.91 0.00

3.74 -0.75 1.00

3.69 1.28 1.00

1.25 5.74 0.00

2.03 0.57 1.00

0.25 1.31 1.00

1.75 2.64 0.00

2.87 1.14 1.00

0.22 4.25 0.00

-0.43 3.71 0.00

0.43 4.72 0.00

2.57 0.57 1.00

0.44 4.95 0.00

2.56 0.04 1.00

1.80 5.66 0.00

2.59 1.30 1.00

-0.63 3.42 0.00

-0.53 2.53 0.00

0.25 4.50 0.00

0.27 4.98 0.00

0.63 4.46 0.00

1.93 -0.58 1.00

2.39 0.31 1.00

0.32 1.47 0.00

0.97 4.32 0.00

0.40 1.05 1.00

2.08 0.94 1.00

2.84 5.21 0.00

1.99 3.65 0.00

0.04 4.74 0.00

-0.39 5.17 0.00

2.19 -1.08 1.00

0.67 4.40 0.00

0.19 5.40 0.00

-1.31 4.56 0.00

1.00 4.40 0.00

2.26 2.80 0.00

1.72 0.88 1.00

2.25 2.20 1.00

-0.03 5.99 0.00

1.21 6.44 0.00

0.56 3.56 0.00

1.05 0.08 1.00

1.95 2.27 1.00

1.89 4.62 0.00

0.47 3.49 0.00

1.62 1.47 1.00

1.14 3.54 1.00

1.26 6.05 0.00

1.51 4.55 0.00

2.15 1.09 1.00

1.20 4.75 0.00

2.07 1.22 1.00

1.53 1.07 1.00

1.37 3.21 0.00

0.75 0.09 1.00

0.28 5.84 0.00

1.19 1.55 1.00

2.57 -1.64 1.00

2.86 -0.95 1.00

0.94 6.68 0.00

2.00 3.75 0.00

2.27 0.99 1.00

2.24 4.52 0.00

3.32 0.80 1.00

2.83 1.14 1.00

-0.73 6.25 0.00

0.91 2.99 0.00

2.08 1.08 1.00

-0.06 4.99 0.00

0.92 4.30 0.00

1.36 4.27 0.00

2.66 2.01 1.00

-0.77 3.00 0.00

2.89 1.22 1.00

2.16 0.81 1.00

1.16 5.97 0.00

1.71 2.92 1.00

0.95 0.35 1.00

1.87 5.68 0.00

1.58 1.83 1.00

2.34 3.61 0.00

1.26 2.45 1.00

3.49 1.32 1.00

2.39 1.55 1.00

1.30 5.30 0.00

0.89 5.67 0.00

2.60 -0.92 1.00

0.75 4.00 0.00

1.18 4.25 0.00

1.92 1.56 0.00

1.51 4.73 0.00

2.34 0.59 1.00

1.25 -0.21 1.00

1.99 -0.54 1.00

2.15 2.47 1.00

1.18 5.28 0.00

2.29 2.30 1.00

-0.49 3.62 0.00

0.44 4.33 0.00

-0.20 3.16 0.00

2.49 1.44 1.00

3.32 1.40 1.00

1.51 -0.20 1.00

2.17 0.24 1.00

2.79 0.52 1.00

1.29 -0.55 1.00

2.79 2.00 1.00

1.49 1.37 1.00

0.26 0.10 1.00

-0.67 3.90 0.00

0.79 3.50 0.00

0.47 3.25 0.00

1.27 0.90 1.00

-0.57 4.72 0.00

2.60 3.19 1.00

0.32 3.78 0.00

2.07 0.78 1.00

-0.18 3.99 0.00

1.99 0.45 1.00

2.30 4.51 0.00

1.54 1.67 1.00

1.01 2.65 0.00

0.98 0.78 1.00

1.43 3.34 0.00

0.52 -0.81 1.00

1.20 1.03 1.00

0.47 1.74 1.00

2.47 4.83 0.00

1.13 -0.55 1.00

0.23 4.34 0.00

2.98 1.36 1.00

0.61 5.37 0.00

3.15 0.76 1.00

1.57 0.90 1.00

0.04 5.55 0.00

0.17 3.84 0.00

-1.12 4.43 0.00

2.90 5.78 0.00

2.13 4.48 0.00

1.43 1.89 1.00

1.41 1.05 1.00

-0.21 3.80 0.00

-0.04 4.23 0.00

0.61 -0.69 1.00

0.84 1.18 1.00

2.30 3.34 0.00

-0.78 4.75 0.00

3.54 -1.23 1.00

-1.61 3.15 0.00

0.68 1.40 1.00

1.59 0.04 1.00

1.91 4.89 0.00

-0.47 5.10 0.00

-0.51 4.74 0.00

0.38 3.19 0.00

2.11 1.20 1.00

1.05 1.00 1.00

1.12 3.98 0.00

0.08 4.69 0.00

1.53 5.20 0.00

3.57 -0.27 1.00

0.17 3.61 0.00

1.03 5.17 0.00

2.08 3.13 1.00

2.51 1.86 1.00

0.34 3.91 0.00

2.46 2.83 1.00

0.71 3.18 0.00

2.83 4.09 0.00

1.13 2.31 1.00

2.57 4.05 0.00

2.16 4.12 0.00

0.61 4.46 0.00

2.54 -0.07 1.00

2.21 -1.06 1.00

0.11 3.72 0.00

3.72 -0.52 1.00

0.92 3.91 0.00

0.30 3.94 0.00

1.27 4.72 0.00

0.46 3.32 0.00

0.91 6.02 0.00

1.35 4.45 0.00

0.52 4.71 0.00

0.06 4.50 0.00

-0.19 5.20 0.00

1.16 1.45 1.00

1.94 5.63 0.00

1.78 -0.42 1.00

1.84 1.65 0.00

2.46 -0.88 1.00

2.94 0.63 1.00

2.16 1.03 1.00

1.33 4.15 0.00

0.59 3.79 0.00

1.91 3.78 0.00

3.02 1.12 1.00

2.12 3.07 0.00

0.47 3.12 0.00

1.69 1.68 1.00

1.41 -0.24 1.00

2.61 0.23 1.00

2.83 0.12 1.00

1.78 2.02 1.00

3.09 2.69 0.00

1.15 3.90 0.00

2.01 0.98 1.00

1.67 5.10 0.00

2.01 4.23 0.00

3.51 0.95 1.00

2.34 1.88 1.00

1.22 -0.04 1.00

1.27 1.17 1.00

0.12 6.21 0.00

2.27 0.98 1.00

2.97 0.84 1.00

2.22 2.23 1.00

2.33 0.19 1.00

4.54 2.59 1.00

1.26 4.91 0.00

0.69 2.84 1.00

3.77 0.11 1.00

-0.56 4.37 0.00

2.51 0.54 1.00

1.34 0.00 1.00

2.81 1.96 1.00

2.58 0.36 1.00

0.32 5.27 0.00

2.82 0.63 1.00

0.63 2.95 0.00

-0.70 3.41 0.00

2.28 -0.78 1.00

1.73 3.76 0.00

2.32 1.42 1.00

-0.66 4.24 0.00

1.89 2.69 1.00

1.95 6.44 0.00

3.59 0.99 1.00

3.80 0.60 1.00

1.20 2.15 1.00

2.31 0.51 1.00

2.89 1.39 1.00

1.64 0.49 1.00

0.33 4.78 0.00

1.87 1.72 1.00

1.52 4.74 0.00

1.46 7.06 0.00

1.28 2.63 1.00

2.14 3.09 1.00

-0.07 -0.94 1.00

0.45 2.36 1.00

2.27 1.47 1.00

1.70 3.20 0.00

0.50 0.39 1.00

-0.06 3.87 0.00

2.49 0.75 1.00

1.48 4.19 0.00

2.30 1.51 1.00

0.38 4.25 0.00

2.46 6.20 0.00

2.17 1.55 1.00

1.17 2.52 0.00

2.08 0.47 1.00

1.44 2.77 0.00

1.47 4.19 0.00

This is a simple data set that contains 1000 entries with 2 features and corresponding class labels (0 or 1) for each entry.

Snapshot of data (first two columns are features, and last column is the class label).

Apply the Perceptron algorithm to obtain a linear classifier and plot the results.

• Is the data linearly separable? Justify your answer.

• If linearly inseparable, what is the percentage of misclassified points using your linear classifier??

Use the Python script for this task, but it needs to be modified. You can load the data as follows:

Tip: compute the total number of misclassified points (classified as 0 where it should be 1, or classified as 1 where it should be zero), and then divide by total number of points (i.e. 1000).

Week 7:

data SMVtxt

0.47 3.87 0.00

2.84 3.33 0.00

0.61 2.51 0.00

3.82 1.65 1.00

1.28 0.63 1.00

0.99 5.87 0.00

1.05 -0.10 1.00

0.91 4.20 0.00

0.88 3.64 0.00

0.91 -0.41 1.00

0.89 4.50 0.00

3.09 1.38 1.00

1.28 1.78 1.00

2.61 -0.17 1.00

-0.97 3.39 0.00

-0.07 2.88 0.00

4.51 0.69 1.00

0.51 1.43 1.00

3.17 0.38 1.00

1.73 0.89 1.00

1.42 6.00 0.00

-0.36 2.34 0.00

1.04 0.30 1.00

1.29 4.82 0.00

0.35 3.82 0.00

1.10 1.91 1.00

0.16 2.90 0.00

0.23 3.48 0.00

0.84 4.01 0.00

1.16 3.92 0.00

2.73 2.20 1.00

1.71 0.64 1.00

0.38 4.07 0.00

0.98 4.83 0.00

1.73 5.17 0.00

-0.30 3.72 0.00

0.78 4.40 0.00

3.34 0.01 1.00

0.58 4.67 0.00

2.28 1.10 1.00

2.98 0.77 1.00

0.26 3.49 0.00

0.51 4.79 0.00

3.45 -0.74 1.00

2.25 -0.26 1.00

1.95 -0.15 1.00

2.66 -0.73 1.00

1.30 1.20 1.00

0.16 2.58 0.00

2.27 -0.34 1.00

0.86 0.40 1.00

2.20 -0.08 1.00

2.01 1.14 1.00

2.10 4.80 0.00

3.45 1.94 1.00

-0.96 4.49 0.00

1.85 4.58 0.00

2.19 -0.51 1.00

3.62 -0.04 1.00

1.53 0.79 1.00

1.22 3.93 0.00

1.91 4.64 0.00

2.10 2.76 1.00

1.50 4.13 0.00

1.00 0.34 1.00

1.14 4.40 0.00

2.10 0.57 1.00

0.30 5.60 0.00

2.21 1.94 1.00

2.91 0.06 1.00

0.68 -0.17 1.00

1.20 4.63 0.00

2.12 0.91 1.00

2.01 0.29 1.00

2.08 1.51 1.00

1.27 5.13 0.00

2.47 4.10 0.00

-1.68 4.91 0.00

1.31 5.25 0.00

0.84 5.38 0.00

0.34 3.94 0.00

0.54 4.02 0.00

2.01 3.31 0.00

2.72 5.30 0.00

2.52 2.61 1.00

2.15 1.28 1.00

1.62 2.73 0.00

2.20 2.73 1.00

2.55 1.38 1.00

-0.47 3.09 0.00

1.75 0.24 1.00

3.14 5.64 0.00

1.01 4.23 0.00

1.70 4.33 0.00

1.75 5.13 0.00

2.12 0.43 1.00

0.71 5.11 0.00

0.33 2.70 1.00

0.24 5.85 0.00

0.57 -0.41 1.00

-1.28 3.28 0.00

2.09 1.41 1.00

1.83 3.65 0.00

-0.09 5.36 0.00

-0.08 5.12 0.00

-0.98 3.64 0.00

0.55 3.47 0.00

3.10 1.50 1.00

1.62 2.69 0.00

1.65 0.10 1.00

-0.85 4.61 0.00

1.93 0.63 1.00

0.23 0.20 1.00

1.74 4.66 0.00

0.54 6.15 0.00

3.33 0.19 1.00

1.27 5.63 0.00

1.25 3.41 0.00

2.37 1.20 1.00

4.74 0.70 1.00

1.77 -0.26 1.00

0.85 5.44 0.00

1.02 4.12 0.00

2.38 3.89 0.00

1.91 5.72 0.00

0.57 -0.37 1.00

-0.79 4.66 0.00

5.23 1.09 1.00

1.31 6.53 0.00

1.26 4.21 0.00

2.45 2.49 1.00

0.61 4.06 0.00

1.93 4.39 0.00

-0.36 2.96 0.00

4.38 1.01 1.00

-0.72 2.05 1.00

2.41 0.42 1.00

1.96 -0.15 1.00

2.93 -0.93 1.00

0.42 3.83 0.00

0.04 3.04 0.00

-0.34 3.84 0.00

-0.06 2.30 1.00

2.07 3.10 1.00

2.65 0.64 1.00

-0.07 5.51 0.00

3.10 1.06 1.00

2.83 1.33 1.00

2.44 2.07 1.00

1.76 3.84 0.00

1.72 3.11 0.00

1.92 4.15 0.00

2.52 0.50 1.00

2.34 1.31 1.00

2.62 4.47 0.00

0.21 4.84 0.00

0.21 4.86 0.00

2.37 0.76 1.00

0.26 -0.17 1.00

1.42 4.48 0.00

2.47 2.23 0.00

-0.15 3.57 0.00

1.60 2.70 0.00

-0.18 5.08 0.00

1.53 1.94 1.00

4.68 0.15 1.00

0.87 5.32 0.00

0.53 3.65 0.00

-0.10 2.22 1.00

1.65 3.60 0.00

1.85 4.19 0.00

0.39 0.77 1.00

2.66 0.30 1.00

2.02 -0.63 1.00

0.79 3.91 0.00

1.75 0.46 1.00

-1.39 5.17 0.00

-0.61 4.91 0.00

0.31 4.63 0.00

0.84 5.44 0.00

1.72 0.18 1.00

0.86 3.62 0.00

1.92 4.04 0.00

0.04 1.49 1.00

0.70 3.59 0.00

3.96 0.88 1.00

0.92 1.89 1.00

2.99 -0.49 1.00

0.41 4.57 0.00

0.71 2.16 1.00

0.08 5.62 0.00

1.72 2.93 1.00

3.67 0.07 1.00

0.67 0.36 1.00

0.58 3.14 0.00

1.08 0.31 1.00

2.46 0.89 1.00

2.70 1.49 1.00

3.66 0.71 1.00

1.50 3.73 0.00

2.47 -0.34 1.00

2.99 1.58 1.00

1.09 3.92 0.00

0.57 5.53 0.00

2.72 -1.64 1.00

3.36 0.87 1.00

2.77 0.65 1.00

3.02 0.48 1.00

2.86 2.96 0.00

-0.24 4.00 0.00

0.45 3.12 0.00

2.71 4.99 0.00

-1.03 4.68 0.00

1.40 0.98 1.00

0.96 4.68 0.00

1.75 5.33 0.00

0.77 5.18 0.00

2.10 -0.06 1.00

0.92 4.04 0.00

3.46 1.06 1.00

2.97 3.11 1.00

0.87 2.64 0.00

1.67 4.14 0.00

0.63 3.51 0.00

-0.28 5.08 0.00

-1.58 4.96 0.00

1.65 1.85 1.00

1.80 0.55 1.00

3.35 1.51 1.00

2.88 1.43 1.00

1.77 -1.10 1.00

0.27 5.25 0.00

1.59 1.77 1.00

2.06 3.67 0.00

1.41 1.07 1.00

2.30 4.20 0.00

2.07 -0.95 1.00

0.69 0.88 1.00

0.48 4.33 0.00

0.44 0.39 1.00

1.09 1.37 1.00

1.33 5.01 0.00

1.65 3.92 0.00

1.08 2.32 1.00

0.38 5.43 0.00

2.20 4.37 0.00

2.14 0.08 1.00

1.58 1.17 1.00

0.57 5.03 0.00

2.30 1.06 1.00

1.24 2.70 1.00

2.14 1.84 1.00

1.07 4.89 0.00

-0.45 6.30 0.00

0.59 4.00 0.00

0.29 4.19 0.00

1.01 0.04 1.00

1.40 4.98 0.00

-0.71 4.19 0.00

1.26 -0.01 1.00

1.87 1.79 1.00

1.38 1.53 0.00

2.65 -0.09 1.00

3.06 1.49 1.00

1.58 0.37 1.00

0.99 5.02 0.00

2.04 3.85 0.00

1.06 0.16 1.00

2.50 1.12 1.00

0.37 1.70 1.00

1.70 1.44 1.00

0.74 2.31 1.00

1.02 2.74 0.00

3.48 1.35 1.00

0.85 4.40 0.00

2.93 1.53 1.00

2.39 0.75 1.00

3.46 1.23 1.00

-0.87 3.83 0.00

1.43 4.40 0.00

1.67 0.84 1.00

2.93 4.69 0.00

1.43 0.51 1.00

2.79 0.83 1.00

0.31 5.99 0.00

1.62 2.05 1.00

0.62 2.69 0.00

2.25 1.00 1.00

2.62 1.04 1.00

2.62 0.56 1.00

0.76 4.75 0.00

1.22 3.42 0.00

0.50 4.92 0.00

3.04 0.81 1.00

2.99 4.26 0.00

3.02 0.17 1.00

2.81 1.46 1.00

2.94 1.93 1.00

1.06 -0.11 1.00

0.85 1.31 1.00

2.07 4.07 0.00

3.28 3.24 0.00

2.39 5.09 0.00

1.74 4.43 0.00

-0.29 5.27 0.00

0.02 3.96 0.00

2.59 0.76 1.00

1.58 5.20 0.00

3.20 -0.01 1.00

0.09 2.32 0.00

2.04 0.41 1.00

1.18 0.67 1.00

3.40 0.16 1.00

0.63 2.82 1.00

1.55 4.10 0.00

0.47 3.14 1.00

-0.37 3.54 0.00

2.48 1.45 1.00

2.35 3.79 0.00

0.03 3.89 0.00

2.47 0.39 1.00

2.30 2.42 1.00

-0.38 4.26 0.00

1.89 0.76 1.00

0.73 5.82 0.00

2.09 0.86 1.00

2.93 0.02 1.00

1.90 1.95 1.00

2.27 2.44 1.00

1.04 4.09 0.00

0.28 4.15 0.00

2.31 1.95 1.00

-0.33 5.96 0.00

1.28 2.93 0.00

0.06 5.42 0.00

2.14 2.32 1.00

0.32 3.79 0.00

1.20 3.29 0.00

0.96 5.13 0.00

2.52 0.80 1.00

3.32 3.02 0.00

2.05 1.74 0.00

0.94 0.86 1.00

1.19 3.10 0.00

0.57 3.44 0.00

0.76 3.22 0.00

-0.20 6.25 0.00

1.71 0.92 1.00

1.52 -0.06 1.00

2.88 0.60 1.00

1.89 0.81 1.00

0.68 3.54 0.00

0.87 4.71 0.00

1.67 5.61 0.00

0.07 3.88 0.00

1.59 2.96 0.00

1.06 2.94 1.00

1.60 0.98 1.00

3.57 3.90 0.00

2.30 2.03 1.00

3.86 1.33 1.00

1.67 5.00 0.00

0.36 1.63 1.00

0.77 4.19 0.00

2.26 1.43 1.00

1.02 -0.53 1.00

2.47 0.86 1.00

1.65 0.58 1.00

-1.04 3.76 0.00

2.09 1.52 1.00

0.13 3.98 0.00

0.32 1.49 1.00

2.13 5.38 0.00

0.12 3.40 0.00

2.12 1.68 1.00

2.16 3.67 0.00

1.57 0.62 1.00

2.06 0.13 1.00

2.36 4.00 0.00

2.22 2.46 1.00

1.85 2.47 1.00

3.11 -0.12 1.00

0.87 3.32 0.00

0.43 0.76 1.00

2.48 1.14 1.00

0.70 4.04 0.00

2.21 0.68 1.00

2.40 4.70 0.00

0.06 4.56 0.00

3.37 0.57 1.00

3.16 1.30 1.00

0.83 3.92 0.00

0.42 0.16 1.00

3.46 1.04 1.00

2.26 1.21 1.00

1.27 1.52 1.00

0.52 4.32 0.00

0.25 2.92 0.00

2.21 5.51 0.00

1.89 1.28 1.00

1.60 2.32 1.00

0.82 6.56 0.00

2.49 0.30 1.00

1.33 -0.22 1.00

1.61 -0.85 1.00

2.44 5.16 0.00

-1.05 6.37 0.00

-0.17 3.95 0.00

0.58 1.26 0.00

1.34 4.49 0.00

1.10 -0.45 1.00

1.31 0.29 1.00

1.47 0.45 1.00

2.07 0.30 1.00

1.22 -0.05 1.00

-0.72 4.69 0.00

0.57 2.39 1.00

1.91 -0.33 1.00

0.96 4.46 0.00

0.21 2.87 0.00

1.16 -1.07 1.00

2.76 -0.60 1.00

1.65 0.43 1.00

2.59 1.22 1.00

1.69 -0.06 1.00

1.55 4.65 0.00

-0.75 -0.29 1.00

1.75 3.12 0.00

1.02 6.64 0.00

2.51 1.18 1.00

1.47 4.28 0.00

1.22 1.89 1.00

1.82 0.65 1.00

2.35 1.81 1.00

4.20 0.81 1.00

1.44 4.58 0.00

0.92 3.99 0.00

1.10 4.71 0.00

2.10 3.22 0.00

3.25 2.85 0.00

1.89 1.67 1.00

0.95 0.87 1.00

-0.45 3.81 0.00

4.23 1.56 1.00

3.11 1.85 1.00

1.90 0.07 1.00

2.86 -0.30 1.00

1.57 3.94 0.00

2.31 0.49 1.00

-1.26 4.71 0.00

1.78 4.38 0.00

0.02 3.51 0.00

1.63 0.26 1.00

0.14 3.71 0.00

3.08 1.61 1.00

3.06 1.43 1.00

3.05 0.71 1.00

1.91 4.13 0.00

0.93 4.02 0.00

3.01 1.44 1.00

1.35 3.20 0.00

0.54 4.49 0.00

-0.05 3.95 0.00

0.59 4.40 0.00

1.59 5.23 0.00

0.38 5.86 0.00

2.09 0.04 1.00

2.30 3.42 0.00

2.22 -0.05 1.00

0.33 2.08 0.00

-0.16 5.09 0.00

0.19 4.19 0.00

3.31 -1.19 1.00

3.72 1.20 1.00

3.02 1.41 1.00

3.17 -0.67 1.00

2.68 1.79 1.00

0.55 0.62 1.00

3.38 0.73 1.00

0.18 3.77 0.00

1.85 0.22 1.00

3.83 0.50 1.00

2.40 1.56 1.00

-1.39 4.32 0.00

2.93 2.10 1.00

1.82 4.05 0.00

2.75 0.60 1.00

0.16 2.84 0.00

1.20 5.69 0.00

0.61 0.45 1.00

2.06 1.78 1.00

2.03 0.78 1.00

1.84 3.56 0.00

2.90 0.22 1.00

1.26 1.98 0.00

2.07 1.95 1.00

2.96 2.70 1.00

1.26 0.68 1.00

1.20 4.70 0.00

1.45 -0.25 1.00

0.75 3.44 0.00

1.54 4.08 0.00

1.46 -0.62 1.00

-0.20 5.61 0.00

1.44 6.04 0.00

1.40 1.11 1.00

0.16 0.72 1.00

-0.32 5.58 0.00

1.14 4.94 0.00

1.25 3.50 0.00

2.10 1.49 1.00

3.88 0.82 1.00

1.22 1.07 1.00

0.29 5.96 0.00

2.09 0.32 1.00

1.90 4.62 0.00

-0.64 4.09 0.00

2.82 0.48 1.00

1.34 5.60 0.00

1.26 3.31 0.00

1.38 4.11 0.00

0.30 4.34 0.00

3.04 1.66 1.00

3.28 0.70 1.00

3.64 1.55 1.00

1.75 0.54 1.00

0.74 1.44 1.00

0.80 0.93 1.00

1.60 1.27 1.00

0.95 0.87 1.00

0.34 4.98 0.00

2.83 0.61 1.00

0.61 5.24 0.00

2.73 4.17 0.00

0.32 4.48 0.00

-0.01 2.83 0.00

1.13 4.54 0.00

1.03 2.51 0.00

1.04 4.61 0.00

2.60 1.61 1.00

0.42 -0.88 1.00

1.59 0.04 1.00

0.75 3.39 0.00

3.70 -0.65 1.00

0.95 4.73 0.00

2.26 2.06 1.00

1.99 1.18 1.00

2.91 0.09 1.00

0.61 3.36 0.00

1.04 0.98 1.00

2.73 0.95 1.00

0.84 4.71 0.00

0.63 3.72 0.00

0.51 2.10 0.00

2.24 2.60 1.00

3.63 0.10 1.00

3.66 0.34 1.00

0.51 4.09 0.00

2.49 4.86 0.00

0.12 4.33 0.00

2.83 0.77 1.00

1.64 -0.20 1.00

2.02 1.23 1.00

0.60 1.28 1.00

2.07 0.82 1.00

1.79 4.36 0.00

1.01 -0.19 1.00

3.27 -0.51 1.00

2.44 1.91 1.00

0.04 5.32 0.00

0.86 4.61 0.00

0.47 6.72 0.00

3.47 2.06 0.00

1.43 2.47 0.00

1.65 4.71 0.00

0.12 2.76 0.00

1.38 0.33 1.00

2.27 -0.11 1.00

2.50 0.28 1.00

0.43 2.42 0.00

1.71 4.43 0.00

0.07 4.36 0.00

2.87 0.62 1.00

1.88 0.39 1.00

1.29 3.45 0.00

2.09 0.81 1.00

0.67 4.07 0.00

3.40 1.81 1.00

2.08 4.96 0.00

1.24 0.38 1.00

1.71 -0.37 1.00

2.25 1.59 1.00

1.44 0.07 1.00

1.56 2.11 1.00

1.65 -0.57 1.00

0.18 4.54 0.00

1.54 3.02 0.00

0.94 3.14 0.00

0.92 -0.32 1.00

1.80 4.31 0.00

3.41 0.50 1.00

2.94 2.37 1.00

1.33 4.68 0.00

1.79 4.89 0.00

1.13 4.68 0.00

1.01 0.58 1.00

3.49 2.36 1.00

2.51 5.77 0.00

1.68 3.24 0.00

1.20 1.23 1.00

1.05 2.57 1.00

0.95 3.57 0.00

3.67 4.23 0.00

0.95 5.38 0.00

1.30 -0.30 1.00

1.47 1.23 1.00

4.02 0.70 1.00

2.01 2.26 0.00

-0.12 0.27 1.00

0.46 0.90 1.00

2.50 1.00 1.00

1.91 4.27 0.00

-0.25 5.15 0.00

3.92 1.66 1.00

2.46 2.10 1.00

0.45 -0.48 1.00

1.08 2.24 1.00

1.69 1.51 1.00

1.93 4.15 0.00

-0.32 4.57 0.00

-0.25 5.27 0.00

0.55 4.41 0.00

0.03 4.55 0.00

1.24 -0.55 1.00

4.58 2.67 1.00

0.87 4.32 0.00

3.24 4.26 0.00

0.51 2.89 0.00

-0.13 4.36 0.00

1.50 4.39 0.00

3.36 5.25 0.00

4.21 1.37 1.00

2.05 2.14 1.00

-0.94 1.78 1.00

0.29 3.09 0.00

1.49 0.59 1.00

1.44 3.93 0.00

1.90 0.96 1.00

2.28 2.51 1.00

0.48 6.23 0.00

3.07 1.60 1.00

1.66 1.68 1.00

1.64 3.84 0.00

2.35 0.94 1.00

1.70 3.44 1.00

1.98 -0.40 1.00

0.62 2.93 0.00

0.48 4.23 0.00

1.73 0.19 1.00

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2.66 -0.15 1.00

0.91 0.94 1.00

1.85 -0.82 1.00

0.67 2.63 0.00

2.21 0.64 1.00

3.20 1.24 1.00

0.52 4.57 0.00

0.01 4.62 0.00

1.34 4.16 0.00

1.91 0.99 1.00

0.12 6.13 0.00

2.02 -0.05 1.00

1.42 4.64 0.00

1.45 4.45 0.00

1.22 -0.47 1.00

1.32 6.32 0.00

0.03 4.54 0.00

0.71 4.12 0.00

1.97 0.33 1.00

2.28 0.81 1.00

1.03 4.80 0.00

2.68 0.27 1.00

0.80 0.31 1.00

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2.08 5.60 0.00

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1.32 -0.74 1.00

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0.73 3.43 0.00

1.47 5.35 0.00

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1.84 3.50 0.00

2.55 0.16 1.00

2.56 1.26 1.00

0.99 6.09 0.00

1.83 5.44 0.00

2.74 0.95 1.00

1.34 5.78 0.00

0.41 2.30 1.00

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0.90 4.56 0.00

2.92 1.08 1.00

0.93 4.52 0.00

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1.33 4.92 0.00

2.50 6.02 0.00

1.05 3.23 0.00

2.25 1.78 1.00

1.07 -0.00 1.00

0.60 3.08 0.00

1.99 2.16 1.00

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3.64 0.25 1.00

0.21 3.42 0.00

0.28 4.01 0.00

2.12 0.63 1.00

0.44 5.39 0.00

2.98 1.46 1.00

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1.99 0.19 1.00

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1.30 1.04 1.00

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1.76 -0.84 1.00

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3.74 -0.75 1.00

3.69 1.28 1.00

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2.03 0.57 1.00

0.25 1.31 1.00

1.75 2.64 0.00

2.87 1.14 1.00

0.22 4.25 0.00

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0.43 4.72 0.00

2.57 0.57 1.00

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2.56 0.04 1.00

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0.40 1.05 1.00

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1.99 3.65 0.00

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2.25 2.20 1.00

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1.05 0.08 1.00

1.95 2.27 1.00

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0.47 3.49 0.00

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2.15 1.09 1.00

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2.08 1.08 1.00

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0.92 4.30 0.00

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2.66 2.01 1.00

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2.89 1.22 1.00

2.16 0.81 1.00

1.16 5.97 0.00

1.71 2.92 1.00

0.95 0.35 1.00

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1.58 1.83 1.00

2.34 3.61 0.00

1.26 2.45 1.00

3.49 1.32 1.00

2.39 1.55 1.00

1.30 5.30 0.00

0.89 5.67 0.00

2.60 -0.92 1.00

0.75 4.00 0.00

1.18 4.25 0.00

1.92 1.56 0.00

1.51 4.73 0.00

2.34 0.59 1.00

1.25 -0.21 1.00

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2.15 2.47 1.00

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2.29 2.30 1.00

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2.49 1.44 1.00

3.32 1.40 1.00

1.51 -0.20 1.00

2.17 0.24 1.00

2.79 0.52 1.00

1.29 -0.55 1.00

2.79 2.00 1.00

1.49 1.37 1.00

0.26 0.10 1.00

-0.67 3.90 0.00

0.79 3.50 0.00

0.47 3.25 0.00

1.27 0.90 1.00

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2.60 3.19 1.00

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2.07 0.78 1.00

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1.99 0.45 1.00

2.30 4.51 0.00

1.54 1.67 1.00

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0.52 -0.81 1.00

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2.98 1.36 1.00

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3.15 0.76 1.00

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1.43 1.89 1.00

1.41 1.05 1.00

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0.61 -0.69 1.00

0.84 1.18 1.00

2.30 3.34 0.00

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3.54 -1.23 1.00

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0.68 1.40 1.00

1.59 0.04 1.00

1.91 4.89 0.00

-0.47 5.10 0.00

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2.51 1.86 1.00

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2.46 2.83 1.00

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1.13 2.31 1.00

2.57 4.05 0.00

2.16 4.12 0.00

0.61 4.46 0.00

2.54 -0.07 1.00

2.21 -1.06 1.00

0.11 3.72 0.00

3.72 -0.52 1.00

0.92 3.91 0.00

0.30 3.94 0.00

1.27 4.72 0.00

0.46 3.32 0.00

0.91 6.02 0.00

1.35 4.45 0.00

0.52 4.71 0.00

0.06 4.50 0.00

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1.16 1.45 1.00

1.94 5.63 0.00

1.78 -0.42 1.00

1.84 1.65 0.00

2.46 -0.88 1.00

2.94 0.63 1.00

2.16 1.03 1.00

1.33 4.15 0.00

0.59 3.79 0.00

1.91 3.78 0.00

3.02 1.12 1.00

2.12 3.07 0.00

0.47 3.12 0.00

1.69 1.68 1.00

1.41 -0.24 1.00

2.61 0.23 1.00

2.83 0.12 1.00

1.78 2.02 1.00

3.09 2.69 0.00

1.15 3.90 0.00

2.01 0.98 1.00

1.67 5.10 0.00

2.01 4.23 0.00

3.51 0.95 1.00

2.34 1.88 1.00

1.22 -0.04 1.00

1.27 1.17 1.00

0.12 6.21 0.00

2.27 0.98 1.00

2.97 0.84 1.00

2.22 2.23 1.00

2.33 0.19 1.00

4.54 2.59 1.00

1.26 4.91 0.00

0.69 2.84 1.00

3.77 0.11 1.00

-0.56 4.37 0.00

2.51 0.54 1.00

1.34 0.00 1.00

2.81 1.96 1.00

2.58 0.36 1.00

0.32 5.27 0.00

2.82 0.63 1.00

0.63 2.95 0.00

-0.70 3.41 0.00

2.28 -0.78 1.00

1.73 3.76 0.00

2.32 1.42 1.00

-0.66 4.24 0.00

1.89 2.69 1.00

1.95 6.44 0.00

3.59 0.99 1.00

3.80 0.60 1.00

1.20 2.15 1.00

2.31 0.51 1.00

2.89 1.39 1.00

1.64 0.49 1.00

0.33 4.78 0.00

1.87 1.72 1.00

1.52 4.74 0.00

1.46 7.06 0.00

1.28 2.63 1.00

2.14 3.09 1.00

-0.07 -0.94 1.00

0.45 2.36 1.00

2.27 1.47 1.00

1.70 3.20 0.00

0.50 0.39 1.00

-0.06 3.87 0.00

2.49 0.75 1.00

1.48 4.19 0.00

2.30 1.51 1.00

0.38 4.25 0.00

2.46 6.20 0.00

2.17 1.55 1.00

1.17 2.52 0.00

2.08 0.47 1.00

1.44 2.77 0.00

1.47 4.19 0.00

. This is a simple data set that contains 1000 entries with 2 features and corresponding class labels (0 or 1) for each entry.

Snapshot of data (first two columns are features, and last column is the class label).

Use the Python script for this task, but it needs to be modified to load the data. You can load the data as follows

Apply the SVM algorithm to obtain a classifier and plot the results.

• What is the percentage of misclassified points using your SVM classifier?

• Try the algorithm for a range of different values of C parameter [0.01, 0.1, 1.0], and provide the results together with your understanding of the influence of the C parameter.

• Compare your SVM results with those from the Perceptron in week-6.

Task 2: Consider we are learning a linear SVM for a training dataset shown in a table below, described with two features x1 and x2, and binary target y.

The classifier takes the form f(x; w1; w2; b) = sign(w1x1 + w2x2 + b)

• Draw the decision boundary of the trained SVM, and identify the support vectors.

• Calculate the parameters of the trained SVM (i.e., w1, w2, b).

week 8:

task 1: Explain how a Multi-layer Perceptron can address the limitation of a single-layer Perceptron.

Task 2: The Phyton implementation of the MLP can be found here: https://colab.research.google.com/drive/1hjFrLBz6k_q3nOeHDzEYJjhZ8w3XfYFm?usp=sharing

Before running the code, make sure you are using free GPU processing power provided by Google:

• Click on Edit/Notebook settings

• Select GPU

• Save

The script applies Multi-layer Perceptron Classifier (Neural Network) to the Iris dataset. Search for theparameter hidden_layer_sizes in the code. You can use this parameter to define the number of

neurons and layers in the network architecture:

ith element represents the number of neurons in the ith hidden layer.

For example:

• hidden_layer_sizes=(2,4) defines a network with 2 hidden layers; first layer with 2 neurons and second layer with 4 neurons

• hidden_layer_sizes=(20,4,2) defines a network with 3 hidden layers; first layer with 20 neurons, second layer with 4 neurons, and third layer with 2 neurons

Try a few different configurations for the network architecture and discuss your observations in the

logbook.

How does the number of neurons and hidden layers affect the performance of the network?

Can you find any architecture that yields 100% accuracy on test set?

Week 9:

Task 1

Describe one method for setting Hyperparameters in k-NN classification method.

Task 2

Based on the cross-validation results in the Introduction exercises above, choose the best value for k

in k-NN. Justify your choice.

Then retrain the image classifier using half the CIFAR10 training data, and test it on all the test data.

What is the level accuracy?

Tip: make sure your Colab use GPU to speed up the process.

Week 10:

Task 1

For the convolution operation given below, compute the destination pixel value in the output image by applying the 3×3 filter to the source (input) image. Provide the details of your calculations.

Task 2

A CNN model has been provided for you here:

Please answer the questions provided in the Python script

Week 11:

Task 1

Table below shows some data from the early days of the Italian clothing company Benetton. Each row in the table shows Benetton’s sales for a year and the amount spent on advertising that year. In this case, our outcome of interest is sales—it is what we want to predict. If we use advertising as the predictor variable, we can find a linear regression model which would give estimates of the sales.

Which one of the following will be the best regression model? Justify your answer.

• Sales = 22.60 × Advertising + 169.2

• Sales = 23.42 × Advertising + 167.7

• Sales = 23.10 × Advertising + 168.1


Tip: Calculate the Least Squares Error.

Task 2

Regression_Advertising.csv:

1000,9914

4000,40487

5000,54324

4500,50044

3000,34719

4000,42551

9000,94871

11000,118914

15000,158484

12000,131348

7000,78504

3000,36284

This is a simple dataset that contains, in unit of £, the marketing spend along with the company sales for each month for a Software company. Using the advertising data, model the linear relationship between the marketing budget and sales. Then answer the following questions:

• How much sale can we expect when the marketing budget is zero?

• For a marketing spend of £7,500, how much sale can we expect?

Tip: use the Python script.

Task 3

Modify the Python script to compute the cost function J for the flitted line in Task 2. Provide a snapshot of your additions to the script. Also provide the results.

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