k_nearest_neighbors.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"collapsed_sections":[],"machine_shape":"hm","authorship_tag":"ABX9TyMgnogy4MthjceNfhB196rJ"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"0MRC0e0KhQ0S"},"source":["# K-Nearest Neighbors (K-NN)"]},{"cell_type":"markdown","metadata":{"id":"LWd1UlMnhT2s"},"source":["## Importing the libraries"]},{"cell_type":"code","metadata":{"id":"YvGPUQaHhXfL","executionInfo":{"status":"ok","timestamp":1667109530318,"user_tz":-240,"elapsed":419,"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":1667109530319,"user_tz":-240,"elapsed":5,"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":1667109531221,"user_tz":-240,"elapsed":906,"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":"217a8f45-0ecf-4fe7-cc76-b71ec703a045","executionInfo":{"status":"ok","timestamp":1667109531221,"user_tz":-240,"elapsed":22,"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  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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 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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":1667109531223,"user_tz":-240,"elapsed":12,"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":"9d2de9c6-73d0-49cb-cc16-a90832a19f44","executionInfo":{"status":"ok","timestamp":1667109531223,"user_tz":-240,"elapsed":11,"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 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-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":"56275cad-c159-40e3-cdd6-f011be4b5185","executionInfo":{"status":"ok","timestamp":1667109531224,"user_tz":-240,"elapsed":11,"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 K-NN model on the Training set"]},{"cell_type":"code","metadata":{"id":"e0pFVAmciHQs","outputId":"0cf96ad7-26a2-4c41-c18e-1aa12f6208a3","executionInfo":{"status":"ok","timestamp":1667109531612,"user_tz":-240,"elapsed":395,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}},"colab":{"base_uri":"https://localhost:8080/"}},"source":["from sklearn.neighbors import KNeighborsClassifier\n","classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n","classifier.fit(X_train, y_train)"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["KNeighborsClassifier()"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"yyxW5b395mR2"},"source":["## Predicting a new result"]},{"cell_type":"code","metadata":{"id":"f8YOXsQy58rP","outputId":"51d7801c-5632-4cbb-cffd-026e3e2cbb70","executionInfo":{"status":"ok","timestamp":1667109531613,"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":"0433f0f8-4c64-404f-bc1a-dcbf20ba27ce","executionInfo":{"status":"ok","timestamp":1667109531613,"user_tz":-240,"elapsed":8,"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"," [1 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [0 0]\n"," [1 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"," [1 0]\n"," [1 1]\n"," [1 1]\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"," [1 1]\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"," [1 1]\n"," [1 1]\n"," [1 0]\n"," [0 0]\n"," [0 0]\n"," [1 1]\n"," [0 1]\n"," [0 0]\n"," [1 1]\n"," [1 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"," [1 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":"4e20400b-e72e-4c00-eb66-3531f314c68d","executionInfo":{"status":"ok","timestamp":1667109531614,"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":["[[64  4]\n"," [ 3 29]]\n"]},{"output_type":"execute_result","data":{"text/plain":["0.93"]},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"6OMC_P0diaoD"},"source":["## Visualising the Training set results"]},{"cell_type":"code","metadata":{"id":"_NOjKvZRid5l","colab":{"base_uri":"https://localhost:8080/","height":351},"outputId":"9abcc2c7-9a62-4420-e965-e90a86d8340a","executionInfo":{"status":"ok","timestamp":1667109758288,"user_tz":-240,"elapsed":226678,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}}},"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 = 1),\n","                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 1))\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('K-NN (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 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5I33sSdBHVGoTaVD8qlNHrVqdBCZcykRR53/zx3kis9BK/xuHOW5u/JFhYTSBqA757fsnCxZwjsNG4j3avjyx3StzSWv+TEebYwrzak8bJlwMo0Z5tH05W0bm8PTgXHbKESzQfbyxdS/97YX2lahmorSamfxIq4n04hMdH0uuaay53mmPwicu/hCP/d9H6NvTy1tft4grrvocaz5crKjK1JhwSRuu839GwzCaAd3Ra5n/RiLsPgS/yhzHuBvWs0Pa+XGmnaUeuSdRzURpNTMVI40m0qxfJe5osX+49XvRJ5eHCZcUYc5/o5SEMXVFNROl1cxUibz7uPQ5770w4WIYNUoYU1VUM1FazUxGcphwqRSKRJYd0GZmN5upzAhHWFNVVDNRGs1MaUAVVJW83XlTh6qivlsvFmLCpQKYMrJszHYqSztpLCVipqp0sDvTwrz9e2iZPS+1AkZVGdq/h92ZlsDnmHAxjIRJaykRM1Wlgx/tXAR00dG7m5TKFlQdIejMNRgmXCoZN7KM+nE0MzIRWdawkAGcJEyLLCs/ad6K10xVDuXULAfGG7mnZ2lpLlZCTLhUKL6RZXtf5Py2S9nc4HyuGfPPlJtKy/GoNdKqWVY6JlyqDDlmaaHA2dENmQznt13KE/VLTMsxjBzSrFlWMiZcaoCiWs7cT7ClvvICAtLoIDcqE9Msk8GEi1FxVJoZI67tmk2YJkMlVg+oBGw/F6PiSOteG35E3ScmK0yzN8CsME1jJdxKpNb28SkVJlyMiqPSzBgdbbC0feJJuKneOQ6qeVSaMK00ov5+DG8SM4uJyO3AOcAuVX292/b/AX8IZIDngQtVtd/97GrgImAM+EtVfdBtXwHcBNQDt6rqF932pcDdwDxgE/BhVc2ISDNwJ7Ac2AN8UFVfSmqdFU1Tk7NDZkNO5v9L20HqUh1ZVolmjCghv5UmTCsRC8mOnyR9Lt8BvoFzo8+yAbhaVUdF5EvA1cCnRWQZcB7wOuBo4Cci8ir3nG8CZwJdwOMisk5VNwNfAr6mqneLyLdwBNPN7mufqh4vIue5/T6Y4DorFifz3yOyjHRn/ieZWZ5G30YlClPDSEy4qOrPRWRJXltuDZONwLnu+9XA3ao6DLwoIluBN7ufbVXVFwBE5G5gtYhsAd4F/LHb5w7gszjCZbX7HuBe4BsiIqphquLUOB7JmZszcxioa2GMOuqlvGHLSWWWpzVQoFrKtKRRcBvJUc5osT8F/s19vxBH2GTpctsAtue1n4JjCutX1VGP/guz57ga0j63f2/cC6hGfMOWX05X2HISZoy05jtUQ5mWtApuIznKIlxE5FpgFLirHNfPmcclwCUAx8yLeQ9ZIzSn9W9izc71zB/pozfB/dHB+yk6Sd9G1Kf2SvcJpFVwG8lRcuEiIh/FcfSfkWOq6gYW53Rb5Lbh074HaBeRBld7ye2fHatLRBqAI9z+BajqLcAtAMuXHmtmszJyWv+mSfu5d4z0cXH3PQCxCxi/p+h6gTGPv4Kovo1qf2oPIjgtKKH2KGkoshv59SlglaoO5Hy0DjhPRJrdKLATgF8BjwMniMhSEWnCcfqvc4XSw0z4bC4A7ssZ6wL3/bnAT83fkn7W7Fx/WLBkadYR1uxcH/u1/J6i89uytDcnc71qCCUOmoPjJ6AtKKF6STIU+XvA6cB8EekCrsOJDmsGNrj7FmxU1T9X1WdFZC2wGcdcdpmqjrnjXA48iBOKfLuqPute4tPA3SLyBeAp4Da3/TbgX9yggL04AsmIg/yw5RgLYs4f6fNsn+fTHoZ8c9tpbdcChbXN/Z5A+od9PghINT+1BzV3VUtQghGcJKPFPuTRfJtHW7b/DcANHu33A/d7tL/ARERZbvsQ8IFQkzWmxLcgJvGELfc2zqHDQ5DsaZwTaVwvc1vneD89dcHHjSoEqjmUOKjgrIagBCMclqFvpIK1C1YyLI2T2oalkbULVkYa18vc9jeDP6JFM5Pa6gQafP4bogqBai4vEsbc1dEGJ3XCqYucVxMs1Y0VrjRSQdZpv2bneuaN9LEnpmgxL3PbqpGnGB+Aa2b/yaSnaEjGdFPNT+1pMHeVMsrQCI4JFyMaXrthDg2yuXFx6H1iHm1fHvtNwc/c9lZe4qRO73OSEAKVHkrsR7kFZymjDI1wmHAxpk0l7BOzdsHKSTcfKG5u8xICpX4yrrRMdq/vrFRrKBZlaMKlvJhwMZIhwciyMEQ1txV7Ml7XuDyREjTb+sYYxXFaZMacY6hPtYDJpZR5PUlGGRrRMOFixE7SkWVhiWJu83syPqL3ZV5sXR77DXRX/zCjTE6sGaWeXf3DdLRFTLgpEaXMxk8qytCIjgkXwyiC35PxLY3v8L6B7h1hfu9EObzNDQtD+Z4Ojjd5peA47RVCKfN6wpo9jdJhwsUwiuD3ZOyXJzNCA3eNTKRznd/88VC+p87xPl6pL6xz1zneB1RG/btS5vUkFWVoRMeEi1E63MiyGQ3DaIZIkWWlwu/JeGZdhgNaaKbq1P5I17tk5Gd8se49DMmEptKiGS4Z+RkP8L5IY5eKUocnJxFlaETHhItREiohsswLvyfjIxubOZR/A2WcK0c3RLrevvnH8LldP+Cm5rPoqZtD53gfVwz/mB1HnhBp3FJS7vBkIx2YcDGMKfB6Ms4qV9kbKMBS6WfV+K8jX+s04Ps7/6mizTzVmtdjBMeEi2FMk+wNdPNuOJSB+fWDMDrq/Li8pWELW9rCaWVJmXkqLX8mCdLwHaRhDqXAhItRXpqaJuXELKvvRbc57+PKiSnVP/NbhrZMEiwAFx/cQHfdHB5oLd2mZ15rq/Y9ZYKQhu8gDXMoFVMKFxH5C+C7qmpZSUbsyFELC/0wB+PLiSnVP/OYwvsGHi1ob2aUyw6u5/v1y2mLOZo4zNpsJ8h0fAdpmEOpCKK5LAAeF5EngduBB23zLSNxYqpZVsp/5qPGvSPFnDDiaHhpKGHWVs17ygQlDd9BGuZQKqYULqr6tyLyGeAs4ELgG+7GXrep6vNJT9CoPeKMLCvVP3O9wJ66WXSMHyj4bEfdnEhai5+G4rdzpl+OSbXuKROUNHwHaZhDqQi0n4urqexwf0aBOcC9IvLlBOdmGJEp1fa6YwpfbVnBcN7z2iCNfKVlJYcyPicGwE9D8cNrbdW8p0xQ0vAdpGEOpSKIz+UK4CNAL3Ar8DeqOiIidcBzwKeSnaJh5BCyIGYpEvqy19vOqdza31iQE7OzfTknRxi/mJZVJ8HWVou5J16mxPmtsGtgos/8Vv/vIIlAkFr6PQTxucwB3q+q23IbVXVcRM5JZlqGUch0CmKW+p85iTDiYqaUrO8lyNpqKffEy5T4fF9h2bbeQZh1yHvLgKQCQWrl91BUuIhIPXCeqn7W63NV3ZLEpAwjFAGd/36bg6WdYtpXrdyowuJlSgTIb/ILgKilqK6kKCpcVHVMRH4rIseo6sulmpRhBKWo87/tUjY3OJ9rZsKE9kT9EpC61NUx86OWTClxESZgw6tvkoEglkQ5wRzgWRH5FXAo26iqqxKblWFExNeElslw4qzrQeomimcOD4cujV9qTEMJh58p0a9v0POjBoJYEuVkPpP4LAyjBGS1nKf59qT2tBfPNMLjZUoEx+eS2+QXABE2ECSoNlJL5rYpQ5FV9WdeP1OdJyK3i8guEflNTttcEdkgIs+5r3PcdhGRr4vIVhH5bxE5KeecC9z+z4nIBTnty0XkGfecr4uIFLuGYQQhMwoHhmFjFzzZ49w0jMqjow2Wtk9oGk318Dtz4Lg5k9uWtnvf1L3O9+ub1Uaymk5WG/H626mlJMophYuInCoij4vIQRHJiMiYiOwPMPZ3gBV5bVcBD6nqCcBD7jHASuAE9+cS4Gb32nOB64BTgDcD1+UIi5uBj+Wct2KKaxhGUTKjMDQ28WRb7CZhpJ+ONieI49RFzmvWtJjfFuZ8L4ppI/mUKu8qDQQxi30DOA+4B3gTTs7Lq6Y6SVV/LiJL8ppXA6e77+8AHgE+7bbf6SZrbhSRdhHpdPtuUNW9ACKyAVghIo8As1V1o9t+J/BeYH2Raxg1ziSfS53zXLWsvhfahtk8Np8hj+iiajVZGPERRhsp9UZq5SRohv5WoF5Vx1T12xRqJEFZoKo97vsdOHXLABYC23P6dbltxdq7PNqLXaMAEblERJ4QkSd6DxycxnKMiqOpCerqkKZm5+eohcixx/G6hYW7SmapRpOFER9htJEw5rZKJ4jmMiAiTcDTbrmXHgIKpWKoqopIogUwp7qGqt4C3AKwfOmxVoyzxqmluk9GfITVRmol8i+IkPgwUA9cjhOKvBj4o2leb6dr7sJ93eW2d7vjZlnkthVrX+TRXuwahlGUOOo+7T7kBAJYQEDtUEvaSBiCRIttU9VBVd2vqp9T1b9yzWTTYR2Qjfi6ALgvp/0jbtTYqcA+17T1IHCWiMxxHfln4ZT87wH2u8EGguMHum+KaxgGZDIsG+1GM8POz45u9KWtPNs9zEv9oDpRIiTsTSJM1JBRXYQJFKgVfM1iIvIMhdUSDqOqv1dsYBH5Ho5jfb6IdOFEfX0RWCsiFwHbgDVu9/uBs4GtwABOaX9Uda+IfB543O13fda5D3wcJyKtFceRv95t97uGUeOEKeU/nSTKJHMY0pzVfVr/JtbsXM/8kT563WKdXvXV0rwGI36K+VwiFaVU1Q/5fHSGR18FLvMZ53acTcry258AXu/RvsfrGoaRNEnlMKQ5q/u0/k1c3H0PzTrizGekj4u77wGYJGDSvIY4MMFZiK9wya+CbBhGcZIKCEhzVveanesPC5YszTrCmp3rJwmXNK8hKtUuOKdLkkmUhlFTJLURVJqzuuePeG/hPC+vPc1riEqYJMpaIrEkSsOoNcJWLw5qSklziHRv4xw6PATMnsbJVZfSvIaoVLPgjEKpkygNo6oJGjUUJrIszVvjrl2wkmFpnNQ2LI2sXbByUlua1xCVWirpEoayJVEaRi0TxgeR5v1csn6V/K2d86PF0ryGqNRSSZcwBBEuH8YRJpcDnyRaEqVhGIQ3paQ5qzvo1s5pXkMUqllwRmFK4ZKNGhORMZwExW5Vtax3o7oYHkYbhg8fTnfXymrwoxjhqVbBGYViSZTfAv5RVZ8VkSOAR4ExYK6IXKmq3yvVJI3qQg8egL69MDYK9Q0wZy4yc1bkvtPFd9dK4MSGv0Oa/Ita5hImJLUcphTLxTBKSTHfydtU9Vn3/YXA/6rq7wLLgU8lPjOjKtGDB2DPbkdYgPO6Z7fTHqFvGggTklrqelRWmsYoNcXMYpmc92fihCKjqjvcTR8NIzx9e50CXrmoOu35GkmRvpr9fJraD0HOz2SgeRzNOOYy3dELw8NsbljIAI42k2suS5sfZfNuOOT+F4+Pj6My+VlyXOH5Ptg94G/ye9wtBzuDYZaNdk/6LPd7ODnCDtGmUVUnxYRLv4icg1Nt+K3ARQAi0oBTz8swwpPVQoK0F+u7Z/eE4MlqNFAgIA5rP7l9e3d5jpd7frYO2dN8e2Is11R2fvPHJ9UhyzJdP0quEDh8E29uhmFHqG2ZcRyHMgT2++TS1uS87h+a/gNhWxO8dqyXuwZvm9Se/R4ODDsVoKcjHHYfcgRclsyYc3xgGPqHTeBUMsWEy58BXweOAj6hqjvc9jOAHyU9MaNKqW/wFhr1Hn+Kfn0hmvbjN57X+TnIUQvRl1/0df6/fhw2NyxiSJoOf9aiGU5v6KKf44AJQZKrCWjDQma4/ZeNbOeu0W8j8xaiB12/T+bvaJvC7+M3bpYm6sjQWHBeVBtEZtSpbptvboNgwuClfu/2XQM517ByKhVJsdpi/4tHsqSqPohTCt8wwjNnbqHmkG33asvVOgBE/IVFGO0n6Pl5FHP+v5Jp4Yn6pdzYcjY9dXPoHO/jyqH7OW3gJT7Z8beH++drAjIvXwsKb2PKajavHevlg8NPcGPDmRzUJjrlAFe2bATg2sF3MjhJwCjN9Y54maQ9jQ+ybLzH0Z60E82MwOiwYyZscgVnJgPDwwzXK/kiKoi5LctYwC36qqUOWS0RJM/FMMqCzJzl6Vs5fJxPWO0nyPkhOGq8n1XjT7Fq5KlJ7eOAbnsBmpvRsflOY/7NOiZ6x1q5pqlT9OcAACAASURBVPG9h7WnV3Q21w6+kxtaH+aG1oe5cehUesZn0an97KmbSVNDoTYzFblbFxzff2ms8y9GrZdTqTRMuFQxpQjjDU3fXt92Xyd9vh8FvDWaoNqPF37nByWTYYccwdG6r+CjPTKTu0ZuK9RQ5i0NfZmpNIzt47PI5JVjGaSRG4dO5RdHfJfVzVsnwqxb/25Sv6x/5rUDPYHn2ykHeEUL46eDmtsa6mB0PFhfywGqLEy4VCmejmwfp3dJicFJLzNnoUODkBuS3DbTc12+2o9H23S/l+yT/NEHD8AeKRB6HfNmTP87z4tYAyb8M2Pdh/0zWfw0iR4tHgk3CQ14tweubNnoYW6D5oCC4Ngj4IW+IrsSulg5lcqjWBLlXxU7UVW/Gv90jNgIE/JbSmJw0uvBA3Do4OS+hw6iLa2+AsZzzXEnY/oIsqhCKzdirZDJ2oWfJtEpE4J4OuP6sbrZ2fE8a25rZJS6hkaaAj62+pVO8Wozf0tlUexPIPsf8WrgZJzSLwB/CPwqyUkZMRAm5LeUxOGkT6vgpIggKxFemkQrI4ed+mHRHd2QyXDiEV9gQJ0x3zT2Eug4m+sXMiBOFNubGnpYPNrL5rpOBsYbGclMmNmmwi/fx4RJZVMsWuxzACLyc+AkVT3gHn8WC0VOP2FCfkuIr5nKzy/ilbBbRHDq9m3p8jGVmEmahM46HC2WbY/CDBmZupNhuAS50yxgcrZ+xm0z0oyfhhDFaR0Tnk76vb3BhUsx8kvFUGYfUxlY3bw1FmECuSa026boCXqwm/MbL2JLy3GxXNuobIIIlzuBX4nIv7vH7wXuSG5KRhyEtf9r767JDvKZs5D5Rwa+XuTItHEfJ7Jfe6BJpcNUVu3k/u7/YfgOviareKB16hL8SWNlZcpLkJL7N4jIeuBtbtOFqvpUsXOMdBDU/l8gWAAOHnCe+gMImFgi0+LK3M+n3D6mKif/d9+hB/nM/nsAyipgwlSoNpIh6I6SM4D9qnoT0CUi4QP0jfTiV2U4aPXhYg72oLT6lKvzap8zN7i5rMw+pqrH43ffygiXH1xfpgk5hKlQbSTDlP95InId8CacqLFvA43Ad3GKWU4LEfkkcDFOePszOCX9O4G7gXnAJuDDqpoRkWYc09xyYA/wQVV9yR3napyCmmPAX7qlaRCRFcBNQD1wq6p+cbpzNQIQMjLNs0rx4KD3GB7tnia/1lYnPNnDx5TKZFL8TYlR55vUuJ74/I6PGu/zbC8VYStUG/ET5LHufcCJwJMAqvqKiEz7L1JEFgJ/CSxT1UERWQucB5wNfE1V73Y3KrsIuNl97VPV40XkPOBLwAdFZJl73uuAo4GfiMir3Mt8E2ebgC7gcRFZp6qbpztnYwpCmLT8TGihQpHxCQpoafVOlkxhMqmvKXFocLKQDDnfpMb1xed3v6OuHc0MHy7qGVd5/qDYTp/lJ4hwyaiqiogCiEgcFssGoFVERnBMbj3Au4A/dj+/A/gsjnBZ7b4HuBf4hjgbyqwG7lbVYeBFEdkKvNntt1VVX3Dne7fb14SLHzNneZvAgt50wkSm+ZnQQuL7FJ4vcLZvK2lOTGDtwO978Po9qDqCIIlxQ3wPnhrnnLms27+IG5tXThTrHF7PqtldPD3ztoJtCg5lprhITJRjp09jMkGEy1oR+WegXUQ+BvwpcOt0L6iq3SJyI/AyMAj8GMcM1q+q2UegLiZShBcC291zR0VkH47pbCGQmxmWe872vPZTvOYiIpcAlwAcM6/8IbrlQuYf6ZiZphktFioyLQYHe6gAghImk4ad17rGEwsqKOcXvZwYXD3DrIHJ33vYdQXs77e2de1v55oZKxlykzZfqZ/LNTPWQOPDrMYNh3YrKGvDMGgjuqPH2XStrpOBOsenNp29aorhl/lvzvzSESRa7EYRORPYj+N3+TtV3TDdC4rIHBxNYinQj7PDZUFp/1KgqrcAtwAsX3ps+MfnKkLmHwkhQo8Lzk8qM93LIR8mQ7+UyaQh5rWu+U1c0/JHE9WLszflAfwFTP642dyg3Bt+WIJ+Dz5ru3HsbQzVTa4rNuQWylzdvHVSBeVJp5YgJybpnT6N4kwZLSYiX1LVDar6N6p6papuEJEvRbjmu4EXVXW3qo4AP8AJDmh3d7kEWISzAybu62J3Lg3AETiO/cPteef4tRtpJz8CzM+0FkYb8YosSyqZNMS8bmw9e9KmYgBD0sSNM84JHgk3Ph7OpBjle/BZW4+0e7fnF8o0ao4gochnerStjHDNl4FTRWSG6zs5A8cf8jBwrtvnAuA+9/069xj385+qqrrt54lIsxsafQJOzbPHgRNEZKmINOE4/bN10Yxy4/ekXN8A8zomPnePPU1rxcbIQ2bO8hwXHH+MvvS88xo07LoYIebVg7fxv0eOKJxvXdCMgSnmFvT79Tvfg0713koyt1BmLbP7kLMF9MYu53X3oXLPqHQUq4p8KfBx4DgR+e+cj2YB/3e6F1TVx0TkXpzos1HgKRzT1I+Au0XkC25btt7EbcC/uA77vTjCAlV91o002+yOc5mqjrlzvxxnt8x64HZVfXa68zVipojzP7BpLWRpm/xxE9uOIMS8ilUvnnK+2XEhmOYS9vv1wmdtV9b/gmt1ZWyFMquJWk/kLGZw/VdgPfB/gKty2g+oaojsuEJU9TrgurzmF5iI9srtOwR8wGecG4AbPNrvB+6PMkcjGeIoS19sj5ZAhSsTqqpcbJ+Z/EirK2c/wjVMOMIBWnxuyjJzFveNvZYbx95Gj7TTqf1cWf8LVtVv8d4yeuYsJz8oxjwXv+989cwuGH54eoUydfJ+MuUKW06KYomcNS1cVHUfsA/4EICIHAm0ADNFZKaqvlyaKRrVRhzO/0jaSEIRZL77zLivuXNb1fczaNw/OVpseD2rGrqgefJ87xs+nmv1nQy6jvNXZA7X6koYg1V4CJeW1oJIvzi0Nb/f23QKZcpRC/lXfjx5jmUKW06KWk/kDJKh/4fAV3ESFXcBxwJbcJIXjSohrVnsgUlDBFmYHBOcqLCCyLC+hoL53jh0asFOj4M0cuPY21jFz7znkb/mBPfACfq3c9/w8Z4azqTzgbc0bGFLW3pVlqAFMWs9kTOIp/ALwKnA/6rqUhwHvBlUq4jDT7X5eRRxOLlLRRhtJEwdszjmEHEMv8grv0gtz3m4eTVvn3UtJxxxI2+fdS3rGk+MR1sL8Ldz3/DxXDv4Tl7R2SjCKzqbawffyX37Fk0+H7j44AZWDG6KNK+kyPpRskIj60fxctQvnu0kbuZSS4mcQYTLiKruAepEpE5VH8apNWZUC3EUniw3ISK1wtQxC0XYvWcC0ol3tcVO9anf5bHmdc1v4poZa3ilfi4qcjivZl1zxH/lgH87xbSv/PObGS174Us/whTE7GiDpe0TmkpTvXNcC/4WCJah3y8iM4GfA3eJyC6ghgLqaoCU7OwYyTQXJoIsJp9LwXz9IrfCRHZ5cOXg/ZMSLgFaNMOVQw94nzA+hr70/MRxfT03zjqfITzyalrPZjV3TWteQODvMqz2FUfhyyT2cwnrR6nlRM4gwmU1MAR8EjgfJ4nx+iQnZZSYYmVDSrSzY1SHc6gotBh8Lp7z9e2sTvWD3LmNj3kLm7q6gv11VgGMj3mWiglUQmZsjB6dDR6KlV++TWACfpe+odc+eTI76tqdaLJplopJKgy41v0oYQhS/uUQgIjMBv4z8RkZpafYHva5JLmzYwwO58BRaK2t3k72MD4Xr/mGmNskzSKX8XHPuXk5/9c1nsg1M9YEKiHTOd7HK/Xe+TbF0B3dkMlw4qzrPZM5L254iMvGHprc6KExXtmykWsH31mYD1P/C6d/znc5TAP/2OKVux2cpMKArSBmcIJEi/0Z8Dkc7WUc5/lHAdsouwLx3esDghVATGpnx1JeLw6fSwzzClW40oMbW3xKyLScXTDOlUP3c23bB6eX7NjUBHV1SFNzwUe/HH0Nl439zLkjFNEYs6HKhdFiXWh9x6RosVtnnsmDbadChsO1ycKGphYzXz3ZM31TmRXEDE4QO8CVwOtVtTfpyRjJMqXpKffJOutrySepnR1LWWAyDkE2nQrEOYTROvzoqZsTuH3VyFPQeuT0kh2noqHhsBAohl8+TPZvL5vn8suW10aekp/5CgojvSC8gDFhMjVB/nOfBwaSnohRAsKYnkKWWIlMKa8XUpD57WMSyJTog2/hSg+tww9fU5ePM3w6yY6Vipf5yotaypgvNUFCka8Gfiki/ywiX8/+JD0xIwFCPLHLzFnQNnNyo1vKJAn8Ckwmcj0/geXR7pfHARTO12+uHlsZhNE6/LiSh2lhZFKbE0XmUfkownYKZDIw7pRqyf9Bx6c/boJ4hQH7USsZ86UmiObyz8BPcfa6T+dfkhGMsNsRe5UyaWlNVsCkrSpAEW1PFh8baKtlr+/LN3qq7kDhzqDZ8/PaVs/bWVjXq3Ujqxq7nUz/GELIs+aup/l2kV7pzKbPN19lfS35WKRXMgQRLo2q+leJz8RInji2I04qWqyU+CWHeq0tpH8mqID0jZ5q2Ygc4bNxm0ebp6mrOYVCOgVYpFdpCSJc1rvbAf8ncLiEadTKyEbpiWU7Yp/2iqpNFmZtCQUa+EdP1YZPpBxYpFdpCfIf8iH39eqcNgtFrlACm57CmtCS2B8lKfLyKia155NgoEEtOdjTgkV6lY4gSZRLSzERI2VUswkthHAJu/9MHBpcmDFC9c3L/GfmrILS/OWg0qoiG8EothPlu1T1pyLyfq/PVfUHyU3LKDdJmtDKzrhPXIpPe1BtLw4NLswYofrmCxaAgwecvmUUMF67bF58cAPddXP4fv3yss3LiE4xzeUdOFFif+jxmQImXKqcJExoqaDU+7mE0eDCjBGmr9/2CQcPRAtTjorHGrJVkb9/hAmXSqbYTpTZbYivV9UXcz8TETOVGROUOuEyKknNt1h16aDmqzBaYIhq1qnFZw1HjfdFKlxplJ8gj2rfB07Ka7sXsMcKAwjvlyg3ic3XTyOqqwtuLgujVYWoZp1afNZQV9/A/7Z/CwA92M35jRexpcViiCqJYj6X1+DUizsiz+8yG2hJemJGZZHKBEgXX60h7vn6aUSqyZTdCVPN2i+Iody/s0rTeo3AFNNcXg2cA7Qz2e9yAPhYkpMyjOngWQMMShYmLTNnoUODk/0bbTP9/R0+ZXf0wD4YHp5obHJqkHlt3FZwPT9UPTP/yx0tVmlarxGcYj6X+4D7ROQ0VX20hHMyjND4RU55PrEnFCbtVzLHV2vwyhnq3TVZsIBzPLxr4jgrIIcGC6/nR32DI0gSFCa6o5vzGy5kc+PiSe0D6lQhmDE+xDLdAc3NPDZ6NLMkw7LRbu46dDM0NTnldIyqIYjP5X0i8iwwCDwA/B7wSVX97nQvKiLtwK3A63Eiz/4U+C3wb8AS4CVgjar2iYgANwFn41Rn/qiqPumOcwHwt+6wX1DVO9z25cB3gFbgfuAK1WmWrzUqA7/IKb9fe4gw6cDOeL851NV5z8PL9BNEC8mOG7RvzGam7AZi57ddyuaGhQxoI29q6IFG1/GOs+9LoeN9Btnc6/punP1h6pthpKngGmkgiW2Sa4kgwuUsVf2UiLwP56b/fuDnwLSFC46weEBVzxWRJpy/umuAh1T1iyJyFXAV8GlgJXCC+3MKcDNwiojMBa4D3oQjoDaJyDpV7XP7fAx4DEe4rADWR5ivkTI88zbCEDDsOFTuip/A8surKRVJVLNuaoLmZqS+mXMObOKqQ+uZP9JHb+Mc1i5YyaPtU8f7HMrAZuZzfuNFAGzeN5E4+dS+v3W0mQD7xCRBUtsk1xKBCle6r+8B7lHVfeJVJiMgInIE8HbgowCqmgEyIrIaON3tdgfwCI5wWQ3c6WoeG0WkXUQ63b4bsjXORGQDsEJEHgFmq+pGt/1O4L2YcKkaQgmW7N/qdB3GSVYfKFUFgwSrWa8Y3MRnBu6h1S393zHSx8Xd9wAUFTAnH5YZzWS1GckNamvK02bUKfl/+HCbs7HY5oaFhzWlk2OUQ0ltk1xLBBEu/yki/4NjFrtURDpwtjyeLkuB3cC3ReQNwCbgCmCBqva4fXYAC9z3C4HtOed3uW3F2rs82gtwC3JeAnDMPItOqRjCCJZ5rl3Gw9Hv5SAvIMnqA15j+PlnopBgKZ7LD64/LFiyNOsIa3auD6S95HMo47xmtRkONvNESycAM8aHWTbWDRJkG6poTLWLpTE1QWqLXSUiXwb2qeqYiAzgaBNRrnkS8Beq+piI3IRjAsu9popI4j4SVb0FuAVg+dJjzSdTDWTzJvIFRu4WzmFMXUlWH/AaIynXYEKleI7y2fVy3oh3ezEm+2cmclpOPvyuFTh+0jmvC32VYPhtk2x7vwTH9xFARD6Vc3iGqo4BqOoh4C8jXLML6FLVx9zje3GEzU7X3IX7mg2P6QZyw08WuW3F2hd5tBsViB48gG7fhr70vPM6hdYii49FlvyO8+r3pF7M1JXPnLmFBS2n4yD3GqO1tXBtSZXMSWjcHT47Z+5pDL6jZhpZPNvZ6yUX2/slHMX0y/Ny3l+d99mK6V5QVXcA20Xk1W7TGcBmYB1wgdt2AXCf+34d8BFxOBVHg+oBHgTOEpE5IjIHOAt40P1sv4ic6kaafSRnLKOC8N1iuLnZ+4SgZp+w2z3Hsf1y/hhtM50w4vy1tbZ6l/4vmJgE65ftm1BS4jdmrpy04RnAsDSydsHKRK5XKry2SV7abv6WMBR7nBGf917HYfkL4C43UuwF4EIcQbdWRC4CtgFr3L7344Qhb8UJRb4QnM3KROTzwONuv+tzNjD7OBOhyOsxZ35l4qdhjI5FSwgMaeqKo4Bn/hi6fZv32gYHHUHklQya39a7C1/8zIMhyYYdnzjreiekGlg2s9ep9zU2nyfqF8Js+OTgeuaN9LEnRLRY2rG9X6JRTLioz3uv41Co6tM4IcT5nOHRV4HLfMa5Hbjdo/0JnBwaIwGS2m+kgCIaRqSEwKRKjvjd8L3GjWH7ZC0iXGJNSGxqgro6Jy8FkA4nPibr73iZ5XzSSg0aeRQTLm8Qkf04Wkqr+x732GqL1ShJ7TfiSULO9HKUHCkQssU2LEuoXE1aNwszqpNi5V8sLsIoJKn9RrxobfUOO25tDTdnDxIpXOkVEAATVYlzBYbnpFw/Sn7Spd931txcWCom255HWjcLM6qX5APGjeoipv1GAjE4GK693Pity68MTV1dQaCAbza/V7BB56JCQdLc7LTnU2yzMMNIgJRuFWikljj2G/ExaxWYjipt++SwCZDj41Cf93wXNtjAQ5D4Voc2jBJimosRjjB5HyH6eoYd+5HW7ZOnUxYpSChyiGAD3/BtwygxKf0vNdJKGGd4KMe5l3/GcwKl30gqcMRb1AKV2VDk/D1gwhSe9PNzlRirKGyYcDFCE8YZHrjvVJpKmTaSiqVUTBjGRj33hAlceDLs9RPQAq2isAEmXIy0UCwBsZybSIWJePPLc6mrC6fVRImw8/ses3vKlGA7YasobIAJF8MlUrJjHKR1L/U4ggoaG71DhvMpFhAQ9Hp+3+Pc+c77EvyOraKwASZcDGJIdoyB1O6lHiZ6yy/PxU+wiEBdffCSLgGY8nss9/dp1AwmXIxkN8QKQSKJjVEJo1GF9XeoFpj8ipV0CUoqv0ej5jDhYlRePokPSZj2QmlUYR36MeQGGenAouMKsb9YoypuaEma9gJrAn5aTra8fhDtJ62+pxA0M8JwXhn+bDse7ZWORcd5Y0mURnwbYpWTMBuAJYTv3i/zjwy8J0xs+8eUkauHfkiDTn5YadBRrh76YZlmlCzFouO82H0InuyBjV3O6+5Dyc+xHFTOo6mRGKl1pochJaY9Py0nkdyglHL+0H8xe+wQN7acTU/dHDrH+7hy6H7OGXmKB3hfuacXO2Gi42pJyzHhYgCVf0OrBtNeXCQVVp4ZheEx54m7mF+ht3EOq0aeYtXIU5Pad1f41sd+NNV7C5Imj7rytZQDVHv/eTVE2XNXEqRgba2twf0apZxXmisKhCCjdQzn3ECLPXGvXbCSi7vvoVlHDreF3fr4tP5NrNm5nvkjffSmfHfLxbMnayMAdeK051NLOUAmXKqUNOSuJIXn2g4ddBzng4NVd2MPRUJh5RmPW4XfE3dWCKzZOb2tj0/r3zRJOHWM9HFx9z2Txk4T2fUHiRYLo+VUOiZcqpWU5K6EIfBTv9/acos9jo3C0GAsa/WbV0H7+Fj5v/OEfE9+pS/9nrgfbV8+bUGwZuf6SVoPQLOOsGbn+lQKF3AESRCzVhgtp9Ix4VKtpMTBHZRQT/1B1xDDTou+8xoanGyGKzanGL7zwFsUJ7U1NN4CJokn7vkjfZ7t83za04yXee+u1uXsGpjoM7+1+vwtYKHI1YvfzSStDu4wocRh1hB1p8ViWlLQUvYRv3PfLYq9svkTCitvolBgJfXE3evj+N9TYQEBWfNex0gfgmPeO2rXc+wdmKzu9Q5WZziyCZdqpdJyV8JoWl5rS4qoWkcc33mILYqTypNpknFa6h0NBhyNZWl7uCfuoPkdaxesZFgmJ1uGDQhIA17mvZuaz2KUyepesZyYSqZswkVE6kXkKRH5oXu8VEQeE5GtIvJvItLktje7x1vdz5fkjHG12/5bEfmDnPYVbttWEbmq1GtLAxWXjBdC0/JcW1LUhfwXqasr+3cuM2chi49FlvyO8xrT9ZsaYFYznLoITuoML1he7J/w0WSjzbwEzKPty7l14QfY3TiHcZwQ5lsXfiC1/hY/vMx7PXXe2pdFi8XLFcAWIKtYfwn4mqreLSLfAi4CbnZf+1T1eBE5z+33QRFZBpwHvA44GviJiLzKHeubwJlAF/C4iKxT1c2lWlhaqKjclZBlT/LX5mk6gujrL2b6yi+R75a2T60ALyNh8zuiBASkhd7GOXTkCZjO8T5eqS/8m67GaLGyaC4isgh4D3CreyzAu4B73S53AO913692j3E/P8Ptvxq4W1WHVfVFYCvwZvdnq6q+oKoZ4G63r5FiompaMv/IQkHi5/QOQzHh4jFfAN2+DX3peec1qs8H/AVkjEJMd3SjL7/IH+9ZwRv3XcRVu05i18u70KEhdg838K6hZziUmf74xfI70lAOJYk5eJn3rhj+MQ1M/jIsWixe/gH4FJD975gH9KseLkjUBSx03y8EtgOo6qiI7HP7LwQ25oyZe872vPZT4l6AET9RNS2ZfyREFSb5FNshM197SijPReYf6URqBYkWi0JTEzQ3szLzGz4z8B+04uaZ6EH+fuhejpsn09Ym/PI76qX85VCSKsnile+z48gTOLaxviYqKJdcuIjIOcAuVd0kIqeX+vp5c7kEuATgmHkpdXQb5SWMuS7B3KJEBKcPlx9cf1iwZImaZ+KX3yECY3k7QIcthxK13H2SJVm8zHsdVKcwyaccmstbgVUicjbQguNzuQloF5EGV3tZBHS7/buBxUCXiDQARwB7ctqz5J7j1z4JVb0FuAVg+dJjA8aVGmmgVGVWQhX1rLDcIj+OGo8/z8Qvi/15nyGDOrjj0DpqqSRLKSm5z0VVr1bVRaq6BMch/1NVPR94GDjX7XYBcJ/7fp17jPv5T1VV3fbz3GiypcAJwK+Ax4ET3OizJvca60qwNKNEHDY/ZW/aWfNTHP4ND5KKvkorO3wimqLmmXS0OVFmudFmfo7soA7usOXuw1yrGp3spSRNeS6fBv5KRLbi+FRuc9tvA+a57X8FXAWgqs8Ca4HNwAPAZao65mo+lwMP4kSjrXX7GkXQgwfid0QnRQr2bqlmvjFzJYOUJs9k8WzHPJZLnUB7czAHexxah98cqtHJXkrKmq6tqo8Aj7jvX8CJ9MrvMwR8wOf8G4AbPNrvB+6PcapVTSoKLoYhreanKin7/0DrcoZH4aqR6RWe9KOYbyS3vb3ZyVoPYuqKoxBkmMKTRnAq66/eSIZKK3KZ1pt4FWxRnOWHzcvZeVx8eSZT+UZyb+RP9gR3sMdVCDJo4UkjOCZcjPRqAn6k9CZeKTt66o5uyGQ4cdb1hysQLGt0Yl42j81nIAZHdr6WMjYeXGCEMXVVotYRNbqtUjDhYqRXE/AhzTfxiqmK0NQEdXVIUzMAsvA4wCl3ERUvLcUPP5NWGFNXJWkdts2xUVukVBMoRsXcxF28QqeBVArIqHhFcPnhJTDSvudJFM3Dtjk2aookNYEw+ShJ9S03ngET+eXy0x5EUYT8m22YSK325sK2NJu6omoetZRTY8LFAJLRBMJEoSXVNxV4BUx4keYgCh/CmMC86B/2bk+rqSuq5mHbHBtGHISJQvPru2c3mm9OKnF0W2QtKUxgRFqDKHwIYwLzotKe2KNqHmk3+cWJCRcjOcJEofn1VS3IxPfVAhK4MceiJfkFTPj1jUgpTYZBb6ql3CY5SaJqHmk2+cWNCRcjOcJEoQW9ARczLyUR3RaHluQVMOFFDEEUXsJwuHcvnx14Bw80nwhMhB0P0AwZaGua/vWC+lj8Vu7lc0kzcWgeaTX5xY0JFyM5wkShBb0B545Tiui2GHKA/AIm8GiLrGF4CMNmRvmLoZ/w4KxTnfm4YccnR7sS4H2zDYOfzyWthNU8aiWnxQsTLkZihIlC8+yr4zA+XtB3ku8ladNPTDlAvgETcc/ZR+j5VTqOitfNNowfpdJ8LhBc86ilnBYvTLgYiRImCm3KzbfgsIbiNW4ivoY5cwvDhrPtacRHGPpVOk5kCgJjITSZan26r6WcFi9MuBihKVU+ShjNJy3hyWXPv/EwLw7TwDdmxl/RGLyfzqX4KQVU69N9LeW0eGHCxQhFqfNRAms+SYUn+5Xx9xg3DQKuQCADt848kwda4ytCmYvX07kCDXWOozurjYwrjHpYOKF6n+5rKafFCxMuRjjiGR+cdAAAC+ZJREFUyF1JIh8lqeKbYcZNSXXprEDWHU5U2C9bXhvLuF7mK7+n8NFxZ1Ow3HO9oqz8AgGSerqPwwQXdIxaymnxwoRLjZFYQmCY3JUkEgWTKr4ZZtxKqy4dAj/ndEOdt0ZSL07p/Nwb8NL2wpty9jifJJ7u43CwhxmjlnJavDDhUkMkmhAYJncliXyUpIpvhhm3rs47uq0uTRu+Tg8/57TgrYGMK4y5QiN7A17a7mxvnE+pnu7jcLCHHaNWclq8qPy/eiM4cWwPPGeuc3PNpVjuStC+EZGZs2Bex4Tgqm+AeR2RfR2hxvXL0Qmau5Ni/MxUY+oIjaym0VTvaDP5K/bb176jrfD8pe3J3JDjcLDXupM+DKa51BIJJgQGzl1JMHoqqTL8gccto3AJugHYyQunN34x53T+0/nGLu8x/G7ApXq6j8PBXutO+jCYcKklipipwvhiKm0vlZJR7k3XEtwArJhzOt/B7eeHieMGHMUhH4eD3W+M9uZCH1OtmsOymHCpJfz8B62tiYTQ+vp4hgZhcLAi9mIJRWsrHDzg3V7hdLTBgWHYNTDRNt9d1ra+MUZxJIfzVD+Ol8U9ah2xqA75OBzsXmO0N0PvYHXm6kTBhEsN4VvjKskcEa9xc2/Aad+LJQyDg+HaK4jdh5wbaC69g7BvYIRRGvN6e7tyo9YRi8MhH4cJLn+MJ3uqN1cnCiZcjNLniORTgZtkeVLFoch+N/ZhbQickh/V6V3MmV5Ok5Q5+b0pebSYiCwWkYdFZLOIPCsiV7jtc0Vkg4g8577OcdtFRL4uIltF5L9F5KScsS5w+z8nIhfktC8XkWfcc74ukh+yVJscNlPl74/iFyobR45IUKrgBuy73lL5XBIkjhtlGJ/L7kOOwNjY5bzuPlT8/Exe2PPuQ9HmGga/edW6k78cocijwF+r6jLgVOAyEVkGXAU8pKonAA+5xwArgRPcn0uAm8ERRsB1wCnAm4HrsgLJ7fOxnPNWlGBd6cfPTKWaTMiwVyiyH1VwAy5l6HWp8btRHqGDtGhmUludjnlGyAX1uWR9K/kCo73ZcZ5PhV/Yc1Isnl04r1rKxPej5MJFVXtU9Un3/QFgC7AQWA3c4Xa7A3iv+341cKc6bATaRaQT+ANgg6ruVdU+YAOwwv1stqpuVFUF7swZq7YptttjUjkibTMnNzY3V+0NOKlcmzTgdwM9rXUHnxv8AUeP7UVUOXpsL7N1yPOhIqjPxc8E1z9cmBPjRylNUqXM1akkyvq4KCJLgBOBx4AFqtrjfrQDWOC+Xwhszzmty20r1t7l0e51/UtwtCGOmVf5N7cpyd9gK6c9THhx0LBlPXgADh2c3JjJOAKnzNFifmuIWh6nWsO0/SKt+tuOY0djH9/f+U/MG+ljT+McTmu71nOMoDf8Yj4ML2d6GvJOajkT34+yCRcRmQl8H/iEqu7PdYuoqopI4plnqnoLcAvA8qXHVn4a9VQUES5BCVVCxs8MNziILD423NxjpGiI9KGDZS/bn1b8bqCPti/n0faJqstNEW/4YRIVa704ZJopi3ARkUYcwXKXqv7Abd4pIp2q2uOatrI7NHUDi3NOX+S2dQOn57U/4rYv8uhveNW9KtbuRZiw5ZDRU15aw+FrRtByCsYd9/AJ5IdIT7U2w5eoN/ww59d6ccg0U45oMQFuA7ao6ldzPloHZCO+LgDuy2n/iBs1diqwzzWfPQicJSJzXEf+WcCD7mf7ReRU91ofyRmrtokjmimMwAhxPc9Itt5dzk9edJt6CQEfPMcNW46lGiLZSkhUH0TY8zvanIKYpy5yXk2wpINyaC5vBT4MPCMiT7tt1wBfBNaKyEXANmCN+9n9wNnAVmAAuBBAVfeKyOeBx91+16tqtgLjx4HvAK3AevfHCFk52FOTCFPiJMz1vDQiz0mF1CSCjluMaohkKzFRfRDmw6h8Sv5fo6r/hX/a1Rke/RW4zGes24HbPdqfAF4fYZpVSRzbBtM2c7JfAnwFRqjClWG0gyT6ioRam2EYxbFHshoj8rbBg4NOuG05i1yG0ST89lgRgbr6wmixltaSVXE2jGrGhIvhTRHfSlnDbcNqEkVMYl4Ra9UaSmwYpcY2CzO8SVMpkyhJiVW8gZdhpBnTXAxvkto2eBpEyokp9x4rhlGjiNoTHAAishsnSi0I84HeBKdTTg6vbf6M1rmds9oWNtbVN42Mj2V6Dhzq7h0YDLEncnCWtM8+Zm5ra0d++97Bwd0v9e9/ebrjzp/ROnfxEbOOFaSud2CA+TNmoOj49n0HtiW1ljJRrX+T1bouqJ61HauqBf+7JlymgYg8oapvKvc8ksDWVplU69qqdV1Q3WsD87kYhmEYCWDCxTAMw4gdEy7T45ZyTyBBbG2VSbWurVrXBdW9NvO5GIZhGPFjmothGIYROyZcDMMwjNgx4TIFInK7iOwSkd/ktM0VkQ0i8pz7Oqecc5wOIrJYRB4Wkc0i8qyIXOG2V8PaWkTkVyLya3dtn3Pbl4rIYyKyVUT+TUSayj3X6SIi9SLylIj80D2uirWJyEsi8oyIPC0iT7htFf83CSAi7SJyr4j8j4hsEZHTqmVtXphwmZrvACvy2q4CHlLVE4CH3ONKYxT4a1VdBpwKXCYiy6iOtQ0D71LVNwBvBFa4ewF9Cfiaqh4P9AEXlXGOUbkC2JJzXE1re6eqvjEnB6Qa/iYBbgIeUNXXAG/A+f1Vy9oKUVX7meIHWAL8Juf4t0Cn+74T+G255xjDGu8Dzqy2tQEzgCeBU3CyoRvc9tNwNpcr+xynsaZFODeidwE/xNnColrW9hIwP6+t4v8mgSOAF3GDqKppbX4/prlMjwXq7HgJsANYUM7JREVElgAnAo9RJWtzzUZP42yXvQF4HuhX1WyhsS5gYbnmF5F/AD4FZPcSmEf1rE2BH4vIJhG5xG2rhr/JpcBu4NuuOfNWEWmjOtbmiQmXiKjzyFGx8dwiMhP4PvAJVd2f+1klr01Vx1T1jThP+W8GXlPmKcWCiJwD7FLVTeWeS0L8vqqeBKzEMdW+PffDCv6bbABOAm5W1ROBQ+SZwCp4bZ6YcJkeO0WkE8B93VXm+UwLEWnEESx3qeoP3OaqWFsWVe0HHsYxFbWLSLYc8iKgu2wTmz5vBVaJyEvA3TimsZuojrWhqt3u6y7g33EeDKrhb7IL6FLVx9zje3GETTWszRMTLtNjHXCB+/4CHH9FRSEiAtwGbFHVr+Z8VA1r6xCRdvd9K44vaQuOkDnX7VaRa1PVq1V1kaouAc4Dfqqq51MFaxORNhGZlX0PnAX8hir4m1TVHcB2EXm123QGsJkqWJsflqE/BSLyPeB0nPLYO4HrgP8A1gLH4JTpX6OqFVW+XUR+H/gF8AwTtvtrcPwulb623wPuAOpxHqDWqur1InIcztP+XOAp4E9Udbh8M42GiJwOXKmq51TD2tw1/Lt72AD8q6reICLzqPC/SQAReSNwK9AEvABciPv3SYWvzQsTLoZhGEbsmFnMMAzDiB0TLoZhGEbsmHAxDMMwYseEi2EYhhE7JlwMwzCM2DHhYhgpQETeKyIqIlVRScAwTLgYRjr4EPBf7qthVDwmXAyjzLj13X4fp0z+eW5bnYj8k7v3xwYRuV9EznU/Wy4iP3OLOz6YLR9iGGnChIthlJ/VOPt8/C+wR0SWA+/H2ephGfBhnNpo2Xpw/wicq6rLgduBG8oxacMoRsPUXQzDSJgP4RSfBKeEy4dw/jfvUdVxYIeIPOx+/mrg9cAGpzwc9UAPhpEyTLgYRhkRkbk4lY1/V0QUR1goEzW2Ck4BnlXV00o0RcOYFmYWM4zyci7wL6p6rKouUdXFODsW7gX+yPW9LMApngrOzoUdInLYTCYiryvHxA2jGCZcDKO8fIhCLeX7wFE4e4BsBr6Ls1XzPlXN4AikL4nIr4GngbeUbrqGEQyrimwYKUVEZqrqQbfk/K+At7r7ghhG6jGfi2Gklx+6m541AZ83wWJUEqa5GIZhGLFjPhfDMAwjdky4GIZhGLFjwsUwDMOIHRMuhmEYRuyYcDEMwzBi5/8BUy6gQWbHTEEAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"SZ-j28aPihZx"},"source":["## Visualising the Test set results"]},{"cell_type":"code","metadata":{"id":"qeTjz2vDilAC","colab":{"base_uri":"https://localhost:8080/","height":351},"outputId":"4922fbce-eec4-4c46-e5ee-2ac9f4503751","executionInfo":{"status":"ok","timestamp":1667109981710,"user_tz":-240,"elapsed":223432,"user":{"displayName":"Hadelin de Ponteves","userId":"15047218817161520419"}}},"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 = 1),\n","                     np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 1))\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('K-NN (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 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\n"},"metadata":{"needs_background":"light"}}]}]}