support_vector_machine.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":[],"machine_shape":"hm","authorship_tag":"ABX9TyNgBlhF8ifu7z3J4PG8bEHX"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"0MRC0e0KhQ0S"},"source":["# Support Vector Machine (SVM)"]},{"cell_type":"markdown","metadata":{"id":"LWd1UlMnhT2s"},"source":["## Importing the libraries"]},{"cell_type":"code","metadata":{"id":"YvGPUQaHhXfL","executionInfo":{"status":"ok","timestamp":1667110184127,"user_tz":-240,"elapsed":25,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}}},"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"K1VMqkGvhc3-"},"source":["## Importing the dataset"]},{"cell_type":"code","metadata":{"id":"M52QDmyzhh9s","executionInfo":{"status":"ok","timestamp":1667110184127,"user_tz":-240,"elapsed":24,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}}},"source":["dataset = pd.read_csv('Social_Network_Ads.csv')\n","X = dataset.iloc[:, :-1].values\n","y = dataset.iloc[:, -1].values"],"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"YvxIPVyMhmKp"},"source":["## Splitting the dataset into the Training set and Test set"]},{"cell_type":"code","metadata":{"id":"AVzJWAXIhxoC","executionInfo":{"status":"ok","timestamp":1667110184128,"user_tz":-240,"elapsed":24,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}}},"source":["from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"P3nS3-6r1i2B","outputId":"070fb62f-0573-48d1-9c29-b7376b04b35c","executionInfo":{"status":"ok","timestamp":1667110184128,"user_tz":-240,"elapsed":23,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["print(X_train)"],"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["[[    44  39000]\n"," [    32 120000]\n"," [    38  50000]\n"," [    32 135000]\n"," [    52  21000]\n"," [    53 104000]\n"," [    39  42000]\n"," [    38  61000]\n"," [    36  50000]\n"," [    36  63000]\n"," [    35  25000]\n"," [    35  50000]\n"," [    42  73000]\n"," [    47  49000]\n"," [    59  29000]\n"," [    49  65000]\n"," [    45 131000]\n"," [    31  89000]\n"," [    46  82000]\n"," [    47  51000]\n"," [    26  15000]\n"," [    60 102000]\n"," [    38 112000]\n"," [    40 107000]\n"," [    42  53000]\n"," [    35  59000]\n"," [    48  41000]\n"," [    48 134000]\n"," [    38 113000]\n"," [    29 148000]\n"," [    26  15000]\n"," [    60  42000]\n"," [    24  19000]\n"," [    42 149000]\n"," [    46  96000]\n"," [    28  59000]\n"," [    39  96000]\n"," [    28  89000]\n"," [    41  72000]\n"," [    45  26000]\n"," [    33  69000]\n"," [    20  82000]\n"," [    31  74000]\n"," [    42  80000]\n"," [    35  72000]\n"," [    33 149000]\n"," [    40  71000]\n"," [    51 146000]\n"," [    46  79000]\n"," [    35  75000]\n"," [    38  51000]\n"," [    36  75000]\n"," [    37  78000]\n"," [    38  61000]\n"," [    60 108000]\n"," [    20  82000]\n"," [    57  74000]\n"," [    42  65000]\n"," [    26  80000]\n"," [    46 117000]\n"," [    35  61000]\n"," [    21  68000]\n"," [    28  44000]\n"," [    41  87000]\n"," [    37  33000]\n"," [    27  90000]\n"," [    39  42000]\n"," [    28 123000]\n"," [    31 118000]\n"," [    25  87000]\n"," [    35  71000]\n"," [    37  70000]\n"," [    35  39000]\n"," [    47  23000]\n"," [    35 147000]\n"," [    48 138000]\n"," [    26  86000]\n"," [    25  79000]\n"," [    52 138000]\n"," [    51  23000]\n"," [    35  60000]\n"," [    33 113000]\n"," [    30 107000]\n"," [    48  33000]\n"," [    41  80000]\n"," [    48  96000]\n"," [    31  18000]\n"," [    31  71000]\n"," [    43 129000]\n"," [    59  76000]\n"," [    18  44000]\n"," [    36 118000]\n"," [    42  90000]\n"," [    47  30000]\n"," [    26  43000]\n"," [    40  78000]\n"," [    46  59000]\n"," [    59  42000]\n"," [    46  74000]\n"," [    35  91000]\n"," [    28  59000]\n"," [    40  57000]\n"," [    59 143000]\n"," [    57  26000]\n"," [    52  38000]\n"," [    47 113000]\n"," [    53 143000]\n"," [    35  27000]\n"," [    58 101000]\n"," [    45  45000]\n"," [    23  82000]\n"," [    46  23000]\n"," [    42  65000]\n"," [    28  84000]\n"," [    38  59000]\n"," [    26  84000]\n"," [    29  28000]\n"," [    37  71000]\n"," [    22  55000]\n"," [    48  35000]\n"," [    49  28000]\n"," [    38  65000]\n"," [    27  17000]\n"," [    46  28000]\n"," [    48 141000]\n"," [    26  17000]\n"," [    35  97000]\n"," [    39  59000]\n"," [    24  27000]\n"," [    32  18000]\n"," [    46  88000]\n"," [    35  58000]\n"," [    56  60000]\n"," [    47  34000]\n"," [    40  72000]\n"," [    32 100000]\n"," [    19  21000]\n"," [    25  90000]\n"," [    35  88000]\n"," [    28  32000]\n"," [    50  20000]\n"," [    40  59000]\n"," [    50  44000]\n"," [    35  72000]\n"," [    40 142000]\n"," [    46  32000]\n"," [    39  71000]\n"," [    20  74000]\n"," [    29  75000]\n"," [    31  76000]\n"," [    47  25000]\n"," [    40  61000]\n"," [    34 112000]\n"," [    38  80000]\n"," [    42  75000]\n"," [    47  47000]\n"," [    39  75000]\n"," [    19  25000]\n"," [    37  80000]\n"," [    36  60000]\n"," [    41  52000]\n"," [    36 125000]\n"," [    48  29000]\n"," [    36 126000]\n"," [    51 134000]\n"," [    27  57000]\n"," [    38  71000]\n"," [    39  61000]\n"," [    22  27000]\n"," [    33  60000]\n"," [    48  74000]\n"," [    58  23000]\n"," [    53  72000]\n"," [    32 117000]\n"," [    54  70000]\n"," [    30  80000]\n"," [    58  95000]\n"," [    26  52000]\n"," [    45  79000]\n"," [    24  55000]\n"," [    40  75000]\n"," [    33  28000]\n"," [    44 139000]\n"," [    22  18000]\n"," [    33  51000]\n"," [    43 133000]\n"," [    24  32000]\n"," [    46  22000]\n"," [    35  55000]\n"," [    54 104000]\n"," [    48 119000]\n"," [    35  53000]\n"," [    37 144000]\n"," [    23  66000]\n"," [    37 137000]\n"," [    31  58000]\n"," [    33  41000]\n"," [    45  22000]\n"," [    30  15000]\n"," [    19  19000]\n"," [    49  74000]\n"," [    39 122000]\n"," [    35  73000]\n"," [    39  71000]\n"," [    24  23000]\n"," [    41  72000]\n"," [    29  83000]\n"," [    54  26000]\n"," [    35  44000]\n"," [    37  75000]\n"," [    29  47000]\n"," [    31  68000]\n"," [    42  54000]\n"," [    30 135000]\n"," [    52 114000]\n"," [    50  36000]\n"," [    56 133000]\n"," [    29  61000]\n"," [    30  89000]\n"," [    26  16000]\n"," [    33  31000]\n"," [    41  72000]\n"," [    36  33000]\n"," [    55 125000]\n"," [    48 131000]\n"," [    41  71000]\n"," [    30  62000]\n"," [    37  72000]\n"," [    41  63000]\n"," [    58  47000]\n"," [    30 116000]\n"," [    20  49000]\n"," [    37  74000]\n"," [    41  59000]\n"," [    49  89000]\n"," [    28  79000]\n"," [    53  82000]\n"," [    40  57000]\n"," [    60  34000]\n"," [    35 108000]\n"," [    21  72000]\n"," [    38  71000]\n"," [    39 106000]\n"," [    37  57000]\n"," [    26  72000]\n"," [    35  23000]\n"," [    54 108000]\n"," [    30  17000]\n"," [    39 134000]\n"," [    29  43000]\n"," [    33  43000]\n"," [    35  38000]\n"," [    41  45000]\n"," [    41  72000]\n"," [    39 134000]\n"," [    27 137000]\n"," [    21  16000]\n"," [    26  32000]\n"," [    31  66000]\n"," [    39  73000]\n"," [    41  79000]\n"," [    47  50000]\n"," [    41  30000]\n"," [    37  93000]\n"," [    60  46000]\n"," [    25  22000]\n"," [    28  37000]\n"," [    38  55000]\n"," [    36  54000]\n"," [    20  36000]\n"," [    56 104000]\n"," [    40  57000]\n"," [    42 108000]\n"," [    20  23000]\n"," [    40  65000]\n"," [    47  20000]\n"," [    18  86000]\n"," [    35  79000]\n"," [    57  33000]\n"," [    34  72000]\n"," [    49  39000]\n"," [    27  31000]\n"," [    19  70000]\n"," [    39  79000]\n"," [    26  81000]\n"," [    25  80000]\n"," [    28  85000]\n"," [    55  39000]\n"," [    50  88000]\n"," [    49  88000]\n"," [    52 150000]\n"," [    35  65000]\n"," [    42  54000]\n"," [    34  43000]\n"," [    37  52000]\n"," [    48  30000]\n"," [    29  43000]\n"," [    36  52000]\n"," [    27  54000]\n"," [    26 118000]]\n"]}]},{"cell_type":"code","metadata":{"id":"8dpDLojm1mVG","outputId":"81c91f31-de79-46a3-8220-443b26de930a","executionInfo":{"status":"ok","timestamp":1667110184129,"user_tz":-240,"elapsed":21,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["print(y_train)"],"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["[0 1 0 1 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 1 0 0 1\n"," 0 0 1 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 1\n"," 1 1 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0\n"," 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0\n"," 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0\n"," 0 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0\n"," 0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0\n"," 0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1\n"," 0 0 0 0]\n"]}]},{"cell_type":"code","metadata":{"id":"qbb7i0DH1qui","outputId":"bea1086c-4fda-4534-d1f1-3cc46dcc67ce","executionInfo":{"status":"ok","timestamp":1667110184129,"user_tz":-240,"elapsed":16,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["print(X_test)"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["[[    30  87000]\n"," [    38  50000]\n"," [    35  75000]\n"," [    30  79000]\n"," [    35  50000]\n"," [    27  20000]\n"," [    31  15000]\n"," [    36 144000]\n"," [    18  68000]\n"," [    47  43000]\n"," [    30  49000]\n"," [    28  55000]\n"," [    37  55000]\n"," [    39  77000]\n"," [    20  86000]\n"," [    32 117000]\n"," [    37  77000]\n"," [    19  85000]\n"," [    55 130000]\n"," [    35  22000]\n"," [    35  47000]\n"," [    47 144000]\n"," [    41  51000]\n"," [    47 105000]\n"," [    23  28000]\n"," [    49 141000]\n"," [    28  87000]\n"," [    29  80000]\n"," [    37  62000]\n"," [    32  86000]\n"," [    21  88000]\n"," [    37  79000]\n"," [    57  60000]\n"," [    37  53000]\n"," [    24  58000]\n"," [    18  52000]\n"," [    22  81000]\n"," [    34  43000]\n"," [    31  34000]\n"," [    49  36000]\n"," [    27  88000]\n"," [    41  52000]\n"," [    27  84000]\n"," [    35  20000]\n"," [    43 112000]\n"," [    27  58000]\n"," [    37  80000]\n"," [    52  90000]\n"," [    26  30000]\n"," [    49  86000]\n"," [    57 122000]\n"," [    34  25000]\n"," [    35  57000]\n"," [    34 115000]\n"," [    59  88000]\n"," [    45  32000]\n"," [    29  83000]\n"," [    26  80000]\n"," [    49  28000]\n"," [    23  20000]\n"," [    32  18000]\n"," [    60  42000]\n"," [    19  76000]\n"," [    36  99000]\n"," [    19  26000]\n"," [    60  83000]\n"," [    24  89000]\n"," [    27  58000]\n"," [    40  47000]\n"," [    42  70000]\n"," [    32 150000]\n"," [    35  77000]\n"," [    22  63000]\n"," [    45  22000]\n"," [    27  89000]\n"," [    18  82000]\n"," [    42  79000]\n"," [    40  60000]\n"," [    53  34000]\n"," [    47 107000]\n"," [    58 144000]\n"," [    59  83000]\n"," [    24  55000]\n"," [    26  35000]\n"," [    58  38000]\n"," [    42  80000]\n"," [    40  75000]\n"," [    59 130000]\n"," [    46  41000]\n"," [    41  60000]\n"," [    42  64000]\n"," [    37 146000]\n"," [    23  48000]\n"," [    25  33000]\n"," [    24  84000]\n"," [    27  96000]\n"," [    23  63000]\n"," [    48  33000]\n"," [    48  90000]\n"," [    42 104000]]\n"]}]},{"cell_type":"code","metadata":{"id":"kj1hnFAR1s5w","outputId":"c763b88c-be8b-406c-f832-4339476c548e","executionInfo":{"status":"ok","timestamp":1667110184130,"user_tz":-240,"elapsed":16,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["print(y_test)"],"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0\n"," 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1\n"," 0 0 0 0 1 1 1 0 0 0 1 1 0 1 1 0 0 1 0 0 0 1 0 1 1 1]\n"]}]},{"cell_type":"markdown","metadata":{"id":"kW3c7UYih0hT"},"source":["## Feature Scaling"]},{"cell_type":"code","metadata":{"id":"9fQlDPKCh8sc","executionInfo":{"status":"ok","timestamp":1667110184130,"user_tz":-240,"elapsed":14,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}}},"source":["from sklearn.preprocessing import StandardScaler\n","sc = StandardScaler()\n","X_train = sc.fit_transform(X_train)\n","X_test = sc.transform(X_test)"],"execution_count":8,"outputs":[]},{"cell_type":"code","metadata":{"id":"syrnD1Op2BSR","outputId":"1dbf7749-0233-4c3c-ede3-3df90f4b5743","executionInfo":{"status":"ok","timestamp":1667110184130,"user_tz":-240,"elapsed":15,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["print(X_train)"],"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 0.58164944 -0.88670699]\n"," [-0.60673761  1.46173768]\n"," [-0.01254409 -0.5677824 ]\n"," [-0.60673761  1.89663484]\n"," [ 1.37390747 -1.40858358]\n"," [ 1.47293972  0.99784738]\n"," [ 0.08648817 -0.79972756]\n"," [-0.01254409 -0.24885782]\n"," [-0.21060859 -0.5677824 ]\n"," [-0.21060859 -0.19087153]\n"," [-0.30964085 -1.29261101]\n"," [-0.30964085 -0.5677824 ]\n"," [ 0.38358493  0.09905991]\n"," [ 0.8787462  -0.59677555]\n"," [ 2.06713324 -1.17663843]\n"," [ 1.07681071 -0.13288524]\n"," [ 0.68068169  1.78066227]\n"," [-0.70576986  0.56295021]\n"," [ 0.77971394  0.35999821]\n"," [ 0.8787462  -0.53878926]\n"," [-1.20093113 -1.58254245]\n"," [ 2.1661655   0.93986109]\n"," [-0.01254409  1.22979253]\n"," [ 0.18552042  1.08482681]\n"," [ 0.38358493 -0.48080297]\n"," [-0.30964085 -0.30684411]\n"," [ 0.97777845 -0.8287207 ]\n"," [ 0.97777845  1.8676417 ]\n"," [-0.01254409  1.25878567]\n"," [-0.90383437  2.27354572]\n"," [-1.20093113 -1.58254245]\n"," [ 2.1661655  -0.79972756]\n"," [-1.39899564 -1.46656987]\n"," [ 0.38358493  2.30253886]\n"," [ 0.77971394  0.76590222]\n"," [-1.00286662 -0.30684411]\n"," [ 0.08648817  0.76590222]\n"," [-1.00286662  0.56295021]\n"," [ 0.28455268  0.07006676]\n"," [ 0.68068169 -1.26361786]\n"," [-0.50770535 -0.01691267]\n"," [-1.79512465  0.35999821]\n"," [-0.70576986  0.12805305]\n"," [ 0.38358493  0.30201192]\n"," [-0.30964085  0.07006676]\n"," [-0.50770535  2.30253886]\n"," [ 0.18552042  0.04107362]\n"," [ 1.27487521  2.21555943]\n"," [ 0.77971394  0.27301877]\n"," [-0.30964085  0.1570462 ]\n"," [-0.01254409 -0.53878926]\n"," [-0.21060859  0.1570462 ]\n"," [-0.11157634  0.24402563]\n"," [-0.01254409 -0.24885782]\n"," [ 2.1661655   1.11381995]\n"," [-1.79512465  0.35999821]\n"," [ 1.86906873  0.12805305]\n"," [ 0.38358493 -0.13288524]\n"," [-1.20093113  0.30201192]\n"," [ 0.77971394  1.37475825]\n"," [-0.30964085 -0.24885782]\n"," [-1.6960924  -0.04590581]\n"," [-1.00286662 -0.74174127]\n"," [ 0.28455268  0.50496393]\n"," [-0.11157634 -1.06066585]\n"," [-1.10189888  0.59194336]\n"," [ 0.08648817 -0.79972756]\n"," [-1.00286662  1.54871711]\n"," [-0.70576986  1.40375139]\n"," [-1.29996338  0.50496393]\n"," [-0.30964085  0.04107362]\n"," [-0.11157634  0.01208048]\n"," [-0.30964085 -0.88670699]\n"," [ 0.8787462  -1.3505973 ]\n"," [-0.30964085  2.24455257]\n"," [ 0.97777845  1.98361427]\n"," [-1.20093113  0.47597078]\n"," [-1.29996338  0.27301877]\n"," [ 1.37390747  1.98361427]\n"," [ 1.27487521 -1.3505973 ]\n"," [-0.30964085 -0.27785096]\n"," [-0.50770535  1.25878567]\n"," [-0.80480212  1.08482681]\n"," [ 0.97777845 -1.06066585]\n"," [ 0.28455268  0.30201192]\n"," [ 0.97777845  0.76590222]\n"," [-0.70576986 -1.49556302]\n"," [-0.70576986  0.04107362]\n"," [ 0.48261718  1.72267598]\n"," [ 2.06713324  0.18603934]\n"," [-1.99318916 -0.74174127]\n"," [-0.21060859  1.40375139]\n"," [ 0.38358493  0.59194336]\n"," [ 0.8787462  -1.14764529]\n"," [-1.20093113 -0.77073441]\n"," [ 0.18552042  0.24402563]\n"," [ 0.77971394 -0.30684411]\n"," [ 2.06713324 -0.79972756]\n"," [ 0.77971394  0.12805305]\n"," [-0.30964085  0.6209365 ]\n"," [-1.00286662 -0.30684411]\n"," [ 0.18552042 -0.3648304 ]\n"," [ 2.06713324  2.12857999]\n"," [ 1.86906873 -1.26361786]\n"," [ 1.37390747 -0.91570013]\n"," [ 0.8787462   1.25878567]\n"," [ 1.47293972  2.12857999]\n"," [-0.30964085 -1.23462472]\n"," [ 1.96810099  0.91086794]\n"," [ 0.68068169 -0.71274813]\n"," [-1.49802789  0.35999821]\n"," [ 0.77971394 -1.3505973 ]\n"," [ 0.38358493 -0.13288524]\n"," [-1.00286662  0.41798449]\n"," [-0.01254409 -0.30684411]\n"," [-1.20093113  0.41798449]\n"," [-0.90383437 -1.20563157]\n"," [-0.11157634  0.04107362]\n"," [-1.59706014 -0.42281668]\n"," [ 0.97777845 -1.00267957]\n"," [ 1.07681071 -1.20563157]\n"," [-0.01254409 -0.13288524]\n"," [-1.10189888 -1.52455616]\n"," [ 0.77971394 -1.20563157]\n"," [ 0.97777845  2.07059371]\n"," [-1.20093113 -1.52455616]\n"," [-0.30964085  0.79489537]\n"," [ 0.08648817 -0.30684411]\n"," [-1.39899564 -1.23462472]\n"," [-0.60673761 -1.49556302]\n"," [ 0.77971394  0.53395707]\n"," [-0.30964085 -0.33583725]\n"," [ 1.77003648 -0.27785096]\n"," [ 0.8787462  -1.03167271]\n"," [ 0.18552042  0.07006676]\n"," [-0.60673761  0.8818748 ]\n"," [-1.89415691 -1.40858358]\n"," [-1.29996338  0.59194336]\n"," [-0.30964085  0.53395707]\n"," [-1.00286662 -1.089659  ]\n"," [ 1.17584296 -1.43757673]\n"," [ 0.18552042 -0.30684411]\n"," [ 1.17584296 -0.74174127]\n"," [-0.30964085  0.07006676]\n"," [ 0.18552042  2.09958685]\n"," [ 0.77971394 -1.089659  ]\n"," [ 0.08648817  0.04107362]\n"," [-1.79512465  0.12805305]\n"," [-0.90383437  0.1570462 ]\n"," [-0.70576986  0.18603934]\n"," [ 0.8787462  -1.29261101]\n"," [ 0.18552042 -0.24885782]\n"," [-0.4086731   1.22979253]\n"," [-0.01254409  0.30201192]\n"," [ 0.38358493  0.1570462 ]\n"," [ 0.8787462  -0.65476184]\n"," [ 0.08648817  0.1570462 ]\n"," [-1.89415691 -1.29261101]\n"," [-0.11157634  0.30201192]\n"," [-0.21060859 -0.27785096]\n"," [ 0.28455268 -0.50979612]\n"," [-0.21060859  1.6067034 ]\n"," [ 0.97777845 -1.17663843]\n"," [-0.21060859  1.63569655]\n"," [ 1.27487521  1.8676417 ]\n"," [-1.10189888 -0.3648304 ]\n"," [-0.01254409  0.04107362]\n"," [ 0.08648817 -0.24885782]\n"," [-1.59706014 -1.23462472]\n"," [-0.50770535 -0.27785096]\n"," [ 0.97777845  0.12805305]\n"," [ 1.96810099 -1.3505973 ]\n"," [ 1.47293972  0.07006676]\n"," [-0.60673761  1.37475825]\n"," [ 1.57197197  0.01208048]\n"," [-0.80480212  0.30201192]\n"," [ 1.96810099  0.73690908]\n"," [-1.20093113 -0.50979612]\n"," [ 0.68068169  0.27301877]\n"," [-1.39899564 -0.42281668]\n"," [ 0.18552042  0.1570462 ]\n"," [-0.50770535 -1.20563157]\n"," [ 0.58164944  2.01260742]\n"," [-1.59706014 -1.49556302]\n"," [-0.50770535 -0.53878926]\n"," [ 0.48261718  1.83864855]\n"," [-1.39899564 -1.089659  ]\n"," [ 0.77971394 -1.37959044]\n"," [-0.30964085 -0.42281668]\n"," [ 1.57197197  0.99784738]\n"," [ 0.97777845  1.43274454]\n"," [-0.30964085 -0.48080297]\n"," [-0.11157634  2.15757314]\n"," [-1.49802789 -0.1038921 ]\n"," [-0.11157634  1.95462113]\n"," [-0.70576986 -0.33583725]\n"," [-0.50770535 -0.8287207 ]\n"," [ 0.68068169 -1.37959044]\n"," [-0.80480212 -1.58254245]\n"," [-1.89415691 -1.46656987]\n"," [ 1.07681071  0.12805305]\n"," [ 0.08648817  1.51972397]\n"," [-0.30964085  0.09905991]\n"," [ 0.08648817  0.04107362]\n"," [-1.39899564 -1.3505973 ]\n"," [ 0.28455268  0.07006676]\n"," [-0.90383437  0.38899135]\n"," [ 1.57197197 -1.26361786]\n"," [-0.30964085 -0.74174127]\n"," [-0.11157634  0.1570462 ]\n"," [-0.90383437 -0.65476184]\n"," [-0.70576986 -0.04590581]\n"," [ 0.38358493 -0.45180983]\n"," [-0.80480212  1.89663484]\n"," [ 1.37390747  1.28777882]\n"," [ 1.17584296 -0.97368642]\n"," [ 1.77003648  1.83864855]\n"," [-0.90383437 -0.24885782]\n"," [-0.80480212  0.56295021]\n"," [-1.20093113 -1.5535493 ]\n"," [-0.50770535 -1.11865214]\n"," [ 0.28455268  0.07006676]\n"," [-0.21060859 -1.06066585]\n"," [ 1.67100423  1.6067034 ]\n"," [ 0.97777845  1.78066227]\n"," [ 0.28455268  0.04107362]\n"," [-0.80480212 -0.21986468]\n"," [-0.11157634  0.07006676]\n"," [ 0.28455268 -0.19087153]\n"," [ 1.96810099 -0.65476184]\n"," [-0.80480212  1.3457651 ]\n"," [-1.79512465 -0.59677555]\n"," [-0.11157634  0.12805305]\n"," [ 0.28455268 -0.30684411]\n"," [ 1.07681071  0.56295021]\n"," [-1.00286662  0.27301877]\n"," [ 1.47293972  0.35999821]\n"," [ 0.18552042 -0.3648304 ]\n"," [ 2.1661655  -1.03167271]\n"," [-0.30964085  1.11381995]\n"," [-1.6960924   0.07006676]\n"," [-0.01254409  0.04107362]\n"," [ 0.08648817  1.05583366]\n"," [-0.11157634 -0.3648304 ]\n"," [-1.20093113  0.07006676]\n"," [-0.30964085 -1.3505973 ]\n"," [ 1.57197197  1.11381995]\n"," [-0.80480212 -1.52455616]\n"," [ 0.08648817  1.8676417 ]\n"," [-0.90383437 -0.77073441]\n"," [-0.50770535 -0.77073441]\n"," [-0.30964085 -0.91570013]\n"," [ 0.28455268 -0.71274813]\n"," [ 0.28455268  0.07006676]\n"," [ 0.08648817  1.8676417 ]\n"," [-1.10189888  1.95462113]\n"," [-1.6960924  -1.5535493 ]\n"," [-1.20093113 -1.089659  ]\n"," [-0.70576986 -0.1038921 ]\n"," [ 0.08648817  0.09905991]\n"," [ 0.28455268  0.27301877]\n"," [ 0.8787462  -0.5677824 ]\n"," [ 0.28455268 -1.14764529]\n"," [-0.11157634  0.67892279]\n"," [ 2.1661655  -0.68375498]\n"," [-1.29996338 -1.37959044]\n"," [-1.00286662 -0.94469328]\n"," [-0.01254409 -0.42281668]\n"," [-0.21060859 -0.45180983]\n"," [-1.79512465 -0.97368642]\n"," [ 1.77003648  0.99784738]\n"," [ 0.18552042 -0.3648304 ]\n"," [ 0.38358493  1.11381995]\n"," [-1.79512465 -1.3505973 ]\n"," [ 0.18552042 -0.13288524]\n"," [ 0.8787462  -1.43757673]\n"," [-1.99318916  0.47597078]\n"," [-0.30964085  0.27301877]\n"," [ 1.86906873 -1.06066585]\n"," [-0.4086731   0.07006676]\n"," [ 1.07681071 -0.88670699]\n"," [-1.10189888 -1.11865214]\n"," [-1.89415691  0.01208048]\n"," [ 0.08648817  0.27301877]\n"," [-1.20093113  0.33100506]\n"," [-1.29996338  0.30201192]\n"," [-1.00286662  0.44697764]\n"," [ 1.67100423 -0.88670699]\n"," [ 1.17584296  0.53395707]\n"," [ 1.07681071  0.53395707]\n"," [ 1.37390747  2.331532  ]\n"," [-0.30964085 -0.13288524]\n"," [ 0.38358493 -0.45180983]\n"," [-0.4086731  -0.77073441]\n"," [-0.11157634 -0.50979612]\n"," [ 0.97777845 -1.14764529]\n"," [-0.90383437 -0.77073441]\n"," [-0.21060859 -0.50979612]\n"," [-1.10189888 -0.45180983]\n"," [-1.20093113  1.40375139]]\n"]}]},{"cell_type":"code","metadata":{"id":"JUd6iBRp2C3L","outputId":"77cac6ce-cf97-468c-f3f2-f7b621e118d4","executionInfo":{"status":"ok","timestamp":1667110184131,"user_tz":-240,"elapsed":13,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["print(X_test)"],"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["[[-0.80480212  0.50496393]\n"," [-0.01254409 -0.5677824 ]\n"," [-0.30964085  0.1570462 ]\n"," [-0.80480212  0.27301877]\n"," [-0.30964085 -0.5677824 ]\n"," [-1.10189888 -1.43757673]\n"," [-0.70576986 -1.58254245]\n"," [-0.21060859  2.15757314]\n"," [-1.99318916 -0.04590581]\n"," [ 0.8787462  -0.77073441]\n"," [-0.80480212 -0.59677555]\n"," [-1.00286662 -0.42281668]\n"," [-0.11157634 -0.42281668]\n"," [ 0.08648817  0.21503249]\n"," [-1.79512465  0.47597078]\n"," [-0.60673761  1.37475825]\n"," [-0.11157634  0.21503249]\n"," [-1.89415691  0.44697764]\n"," [ 1.67100423  1.75166912]\n"," [-0.30964085 -1.37959044]\n"," [-0.30964085 -0.65476184]\n"," [ 0.8787462   2.15757314]\n"," [ 0.28455268 -0.53878926]\n"," [ 0.8787462   1.02684052]\n"," [-1.49802789 -1.20563157]\n"," [ 1.07681071  2.07059371]\n"," [-1.00286662  0.50496393]\n"," [-0.90383437  0.30201192]\n"," [-0.11157634 -0.21986468]\n"," [-0.60673761  0.47597078]\n"," [-1.6960924   0.53395707]\n"," [-0.11157634  0.27301877]\n"," [ 1.86906873 -0.27785096]\n"," [-0.11157634 -0.48080297]\n"," [-1.39899564 -0.33583725]\n"," [-1.99318916 -0.50979612]\n"," [-1.59706014  0.33100506]\n"," [-0.4086731  -0.77073441]\n"," [-0.70576986 -1.03167271]\n"," [ 1.07681071 -0.97368642]\n"," [-1.10189888  0.53395707]\n"," [ 0.28455268 -0.50979612]\n"," [-1.10189888  0.41798449]\n"," [-0.30964085 -1.43757673]\n"," [ 0.48261718  1.22979253]\n"," [-1.10189888 -0.33583725]\n"," [-0.11157634  0.30201192]\n"," [ 1.37390747  0.59194336]\n"," [-1.20093113 -1.14764529]\n"," [ 1.07681071  0.47597078]\n"," [ 1.86906873  1.51972397]\n"," [-0.4086731  -1.29261101]\n"," [-0.30964085 -0.3648304 ]\n"," [-0.4086731   1.31677196]\n"," [ 2.06713324  0.53395707]\n"," [ 0.68068169 -1.089659  ]\n"," [-0.90383437  0.38899135]\n"," [-1.20093113  0.30201192]\n"," [ 1.07681071 -1.20563157]\n"," [-1.49802789 -1.43757673]\n"," [-0.60673761 -1.49556302]\n"," [ 2.1661655  -0.79972756]\n"," [-1.89415691  0.18603934]\n"," [-0.21060859  0.85288166]\n"," [-1.89415691 -1.26361786]\n"," [ 2.1661655   0.38899135]\n"," [-1.39899564  0.56295021]\n"," [-1.10189888 -0.33583725]\n"," [ 0.18552042 -0.65476184]\n"," [ 0.38358493  0.01208048]\n"," [-0.60673761  2.331532  ]\n"," [-0.30964085  0.21503249]\n"," [-1.59706014 -0.19087153]\n"," [ 0.68068169 -1.37959044]\n"," [-1.10189888  0.56295021]\n"," [-1.99318916  0.35999821]\n"," [ 0.38358493  0.27301877]\n"," [ 0.18552042 -0.27785096]\n"," [ 1.47293972 -1.03167271]\n"," [ 0.8787462   1.08482681]\n"," [ 1.96810099  2.15757314]\n"," [ 2.06713324  0.38899135]\n"," [-1.39899564 -0.42281668]\n"," [-1.20093113 -1.00267957]\n"," [ 1.96810099 -0.91570013]\n"," [ 0.38358493  0.30201192]\n"," [ 0.18552042  0.1570462 ]\n"," [ 2.06713324  1.75166912]\n"," [ 0.77971394 -0.8287207 ]\n"," [ 0.28455268 -0.27785096]\n"," [ 0.38358493 -0.16187839]\n"," [-0.11157634  2.21555943]\n"," [-1.49802789 -0.62576869]\n"," [-1.29996338 -1.06066585]\n"," [-1.39899564  0.41798449]\n"," [-1.10189888  0.76590222]\n"," [-1.49802789 -0.19087153]\n"," [ 0.97777845 -1.06066585]\n"," [ 0.97777845  0.59194336]\n"," [ 0.38358493  0.99784738]]\n"]}]},{"cell_type":"markdown","metadata":{"id":"bb6jCOCQiAmP"},"source":["## Training the SVM model on the Training set"]},{"cell_type":"code","metadata":{"id":"e0pFVAmciHQs","colab":{"base_uri":"https://localhost:8080/"},"outputId":"1d998bbf-71bb-4946-9d92-ebaaa68f45bc","executionInfo":{"status":"ok","timestamp":1667110184605,"user_tz":-240,"elapsed":483,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}}},"source":["from sklearn.svm import SVC\n","classifier = SVC(kernel = 'linear', random_state = 0)\n","classifier.fit(X_train, y_train)"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVC(kernel='linear', random_state=0)"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"yyxW5b395mR2"},"source":["## Predicting a new result"]},{"cell_type":"code","metadata":{"id":"f8YOXsQy58rP","outputId":"58a9f8ef-b4cf-40fe-efcd-fdec5623168c","executionInfo":{"status":"ok","timestamp":1667110184605,"user_tz":-240,"elapsed":10,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["print(classifier.predict(sc.transform([[30,87000]])))"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["[0]\n"]}]},{"cell_type":"markdown","metadata":{"id":"vKYVQH-l5NpE"},"source":["## Predicting the Test set results"]},{"cell_type":"code","metadata":{"id":"p6VMTb2O4hwM","outputId":"d7fcdfa8-f762-46e5-ce50-4f5db4b50ea2","executionInfo":{"status":"ok","timestamp":1667110184606,"user_tz":-240,"elapsed":9,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["y_pred = classifier.predict(X_test)\n","print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"],"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["[[0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 1]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [1 1]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 1]\n"," [0 0]\n"," [0 0]\n"," [0 1]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 1]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 1]\n"," [0 0]\n"," [0 0]\n"," [1 0]\n"," [0 0]\n"," [1 1]\n"," [1 1]\n"," [1 1]\n"," [1 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [1 1]\n"," [0 0]\n"," [1 1]\n"," [0 1]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 1]\n"," [0 0]\n"," [0 1]\n"," [1 1]\n"," [1 1]]\n"]}]},{"cell_type":"markdown","metadata":{"id":"h4Hwj34ziWQW"},"source":["## Making the Confusion Matrix"]},{"cell_type":"code","metadata":{"id":"D6bpZwUiiXic","outputId":"b3b58bcb-c4b2-4260-bb59-c422917054c2","executionInfo":{"status":"ok","timestamp":1667110184606,"user_tz":-240,"elapsed":7,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["from sklearn.metrics import confusion_matrix, accuracy_score\n","cm = confusion_matrix(y_test, y_pred)\n","print(cm)\n","accuracy_score(y_test, y_pred)"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["[[66  2]\n"," [ 8 24]]\n"]},{"output_type":"execute_result","data":{"text/plain":["0.9"]},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"6OMC_P0diaoD"},"source":["## Visualising the Training set results"]},{"cell_type":"code","metadata":{"id":"_NOjKvZRid5l","outputId":"26d11466-0268-4a6f-eab1-5723f5517f27","executionInfo":{"status":"ok","timestamp":1667110433299,"user_tz":-240,"elapsed":248697,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/","height":351}},"source":["from matplotlib.colors import ListedColormap\n","X_set, y_set = sc.inverse_transform(X_train), y_train\n","X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n","                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n","plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n","             alpha = 0.75, cmap = ListedColormap(('salmon', 'dodgerblue')))\n","plt.xlim(X1.min(), X1.max())\n","plt.ylim(X2.min(), X2.max())\n","for i, j in enumerate(np.unique(y_set)):\n","    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('salmon', 'dodgerblue'))(i), label = j)\n","plt.title('SVM (Training set)')\n","plt.xlabel('Age')\n","plt.ylabel('Estimated Salary')\n","plt.legend()\n","plt.show()"],"execution_count":15,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n","WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"SZ-j28aPihZx"},"source":["## Visualising the Test set results"]},{"cell_type":"code","metadata":{"id":"qeTjz2vDilAC","outputId":"687ad65a-add6-4253-d6a0-80b20bba5283","executionInfo":{"status":"ok","timestamp":1667110683068,"user_tz":-240,"elapsed":249775,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/","height":351}},"source":["from matplotlib.colors import ListedColormap\n","X_set, y_set = sc.inverse_transform(X_test), y_test\n","X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 0.25),\n","                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 0.25))\n","plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),\n","             alpha = 0.75, cmap = ListedColormap(('salmon', 'dodgerblue')))\n","plt.xlim(X1.min(), X1.max())\n","plt.ylim(X2.min(), X2.max())\n","for i, j in enumerate(np.unique(y_set)):\n","    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('salmon', 'dodgerblue'))(i), label = j)\n","plt.title('SVM (Test set)')\n","plt.xlabel('Age')\n","plt.ylabel('Estimated Salary')\n","plt.legend()\n","plt.show()"],"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n","WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}