├── README.md ├── Social_Network_Ads.csv └── support_vector_machine.py /README.md: -------------------------------------------------------------------------------- 1 | # Support-Vector-Machine 2 | 3 | A simple implementation of a (linear) Support Vector Machine model in python. The classifier is an object of the SVC class which was imported from sklearn.svm library. 4 | 5 | the linear kernel type was choosen since this was a linear SVM classifier model. 6 | -------------------------------------------------------------------------------- /Social_Network_Ads.csv: -------------------------------------------------------------------------------- 1 | User ID,Gender,Age,EstimatedSalary,Purchased 2 | 15624510,Male,19,19000,0 3 | 15810944,Male,35,20000,0 4 | 15668575,Female,26,43000,0 5 | 15603246,Female,27,57000,0 6 | 15804002,Male,19,76000,0 7 | 15728773,Male,27,58000,0 8 | 15598044,Female,27,84000,0 9 | 15694829,Female,32,150000,1 10 | 15600575,Male,25,33000,0 11 | 15727311,Female,35,65000,0 12 | 15570769,Female,26,80000,0 13 | 15606274,Female,26,52000,0 14 | 15746139,Male,20,86000,0 15 | 15704987,Male,32,18000,0 16 | 15628972,Male,18,82000,0 17 | 15697686,Male,29,80000,0 18 | 15733883,Male,47,25000,1 19 | 15617482,Male,45,26000,1 20 | 15704583,Male,46,28000,1 21 | 15621083,Female,48,29000,1 22 | 15649487,Male,45,22000,1 23 | 15736760,Female,47,49000,1 24 | 15714658,Male,48,41000,1 25 | 15599081,Female,45,22000,1 26 | 15705113,Male,46,23000,1 27 | 15631159,Male,47,20000,1 28 | 15792818,Male,49,28000,1 29 | 15633531,Female,47,30000,1 30 | 15744529,Male,29,43000,0 31 | 15669656,Male,31,18000,0 32 | 15581198,Male,31,74000,0 33 | 15729054,Female,27,137000,1 34 | 15573452,Female,21,16000,0 35 | 15776733,Female,28,44000,0 36 | 15724858,Male,27,90000,0 37 | 15713144,Male,35,27000,0 38 | 15690188,Female,33,28000,0 39 | 15689425,Male,30,49000,0 40 | 15671766,Female,26,72000,0 41 | 15782806,Female,27,31000,0 42 | 15764419,Female,27,17000,0 43 | 15591915,Female,33,51000,0 44 | 15772798,Male,35,108000,0 45 | 15792008,Male,30,15000,0 46 | 15715541,Female,28,84000,0 47 | 15639277,Male,23,20000,0 48 | 15798850,Male,25,79000,0 49 | 15776348,Female,27,54000,0 50 | 15727696,Male,30,135000,1 51 | 15793813,Female,31,89000,0 52 | 15694395,Female,24,32000,0 53 | 15764195,Female,18,44000,0 54 | 15744919,Female,29,83000,0 55 | 15671655,Female,35,23000,0 56 | 15654901,Female,27,58000,0 57 | 15649136,Female,24,55000,0 58 | 15775562,Female,23,48000,0 59 | 15807481,Male,28,79000,0 60 | 15642885,Male,22,18000,0 61 | 15789109,Female,32,117000,0 62 | 15814004,Male,27,20000,0 63 | 15673619,Male,25,87000,0 64 | 15595135,Female,23,66000,0 65 | 15583681,Male,32,120000,1 66 | 15605000,Female,59,83000,0 67 | 15718071,Male,24,58000,0 68 | 15679760,Male,24,19000,0 69 | 15654574,Female,23,82000,0 70 | 15577178,Female,22,63000,0 71 | 15595324,Female,31,68000,0 72 | 15756932,Male,25,80000,0 73 | 15726358,Female,24,27000,0 74 | 15595228,Female,20,23000,0 75 | 15782530,Female,33,113000,0 76 | 15592877,Male,32,18000,0 77 | 15651983,Male,34,112000,1 78 | 15746737,Male,18,52000,0 79 | 15774179,Female,22,27000,0 80 | 15667265,Female,28,87000,0 81 | 15655123,Female,26,17000,0 82 | 15595917,Male,30,80000,0 83 | 15668385,Male,39,42000,0 84 | 15709476,Male,20,49000,0 85 | 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15739160,Male,42,80000,0 117 | 15689237,Male,40,57000,0 118 | 15679297,Male,35,75000,0 119 | 15591433,Male,36,52000,0 120 | 15642725,Male,40,59000,0 121 | 15701962,Male,41,59000,0 122 | 15811613,Female,36,75000,0 123 | 15741049,Male,37,72000,0 124 | 15724423,Female,40,75000,0 125 | 15574305,Male,35,53000,0 126 | 15678168,Female,41,51000,0 127 | 15697020,Female,39,61000,0 128 | 15610801,Male,42,65000,0 129 | 15745232,Male,26,32000,0 130 | 15722758,Male,30,17000,0 131 | 15792102,Female,26,84000,0 132 | 15675185,Male,31,58000,0 133 | 15801247,Male,33,31000,0 134 | 15725660,Male,30,87000,0 135 | 15638963,Female,21,68000,0 136 | 15800061,Female,28,55000,0 137 | 15578006,Male,23,63000,0 138 | 15668504,Female,20,82000,0 139 | 15687491,Male,30,107000,1 140 | 15610403,Female,28,59000,0 141 | 15741094,Male,19,25000,0 142 | 15807909,Male,19,85000,0 143 | 15666141,Female,18,68000,0 144 | 15617134,Male,35,59000,0 145 | 15783029,Male,30,89000,0 146 | 15622833,Female,34,25000,0 147 | 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15746203,Female,47,144000,1 271 | 15583137,Male,40,61000,0 272 | 15680752,Female,43,133000,0 273 | 15688172,Female,59,76000,1 274 | 15791373,Male,60,42000,1 275 | 15589449,Male,39,106000,1 276 | 15692819,Female,57,26000,1 277 | 15727467,Male,57,74000,1 278 | 15734312,Male,38,71000,0 279 | 15764604,Male,49,88000,1 280 | 15613014,Female,52,38000,1 281 | 15759684,Female,50,36000,1 282 | 15609669,Female,59,88000,1 283 | 15685536,Male,35,61000,0 284 | 15750447,Male,37,70000,1 285 | 15663249,Female,52,21000,1 286 | 15638646,Male,48,141000,0 287 | 15734161,Female,37,93000,1 288 | 15631070,Female,37,62000,0 289 | 15761950,Female,48,138000,1 290 | 15649668,Male,41,79000,0 291 | 15713912,Female,37,78000,1 292 | 15586757,Male,39,134000,1 293 | 15596522,Male,49,89000,1 294 | 15625395,Male,55,39000,1 295 | 15760570,Male,37,77000,0 296 | 15566689,Female,35,57000,0 297 | 15725794,Female,36,63000,0 298 | 15673539,Male,42,73000,1 299 | 15705298,Female,43,112000,1 300 | 15675791,Male,45,79000,0 301 | 15747043,Male,46,117000,1 302 | 15736397,Female,58,38000,1 303 | 15678201,Male,48,74000,1 304 | 15720745,Female,37,137000,1 305 | 15637593,Male,37,79000,1 306 | 15598070,Female,40,60000,0 307 | 15787550,Male,42,54000,0 308 | 15603942,Female,51,134000,0 309 | 15733973,Female,47,113000,1 310 | 15596761,Male,36,125000,1 311 | 15652400,Female,38,50000,0 312 | 15717893,Female,42,70000,0 313 | 15622585,Male,39,96000,1 314 | 15733964,Female,38,50000,0 315 | 15753861,Female,49,141000,1 316 | 15747097,Female,39,79000,0 317 | 15594762,Female,39,75000,1 318 | 15667417,Female,54,104000,1 319 | 15684861,Male,35,55000,0 320 | 15742204,Male,45,32000,1 321 | 15623502,Male,36,60000,0 322 | 15774872,Female,52,138000,1 323 | 15611191,Female,53,82000,1 324 | 15674331,Male,41,52000,0 325 | 15619465,Female,48,30000,1 326 | 15575247,Female,48,131000,1 327 | 15695679,Female,41,60000,0 328 | 15713463,Male,41,72000,0 329 | 15785170,Female,42,75000,0 330 | 15796351,Male,36,118000,1 331 | 15639576,Female,47,107000,1 332 | 15693264,Male,38,51000,0 333 | 15589715,Female,48,119000,1 334 | 15769902,Male,42,65000,0 335 | 15587177,Male,40,65000,0 336 | 15814553,Male,57,60000,1 337 | 15601550,Female,36,54000,0 338 | 15664907,Male,58,144000,1 339 | 15612465,Male,35,79000,0 340 | 15810800,Female,38,55000,0 341 | 15665760,Male,39,122000,1 342 | 15588080,Female,53,104000,1 343 | 15776844,Male,35,75000,0 344 | 15717560,Female,38,65000,0 345 | 15629739,Female,47,51000,1 346 | 15729908,Male,47,105000,1 347 | 15716781,Female,41,63000,0 348 | 15646936,Male,53,72000,1 349 | 15768151,Female,54,108000,1 350 | 15579212,Male,39,77000,0 351 | 15721835,Male,38,61000,0 352 | 15800515,Female,38,113000,1 353 | 15591279,Male,37,75000,0 354 | 15587419,Female,42,90000,1 355 | 15750335,Female,37,57000,0 356 | 15699619,Male,36,99000,1 357 | 15606472,Male,60,34000,1 358 | 15778368,Male,54,70000,1 359 | 15671387,Female,41,72000,0 360 | 15573926,Male,40,71000,1 361 | 15709183,Male,42,54000,0 362 | 15577514,Male,43,129000,1 363 | 15778830,Female,53,34000,1 364 | 15768072,Female,47,50000,1 365 | 15768293,Female,42,79000,0 366 | 15654456,Male,42,104000,1 367 | 15807525,Female,59,29000,1 368 | 15574372,Female,58,47000,1 369 | 15671249,Male,46,88000,1 370 | 15779744,Male,38,71000,0 371 | 15624755,Female,54,26000,1 372 | 15611430,Female,60,46000,1 373 | 15774744,Male,60,83000,1 374 | 15629885,Female,39,73000,0 375 | 15708791,Male,59,130000,1 376 | 15793890,Female,37,80000,0 377 | 15646091,Female,46,32000,1 378 | 15596984,Female,46,74000,0 379 | 15800215,Female,42,53000,0 380 | 15577806,Male,41,87000,1 381 | 15749381,Female,58,23000,1 382 | 15683758,Male,42,64000,0 383 | 15670615,Male,48,33000,1 384 | 15715622,Female,44,139000,1 385 | 15707634,Male,49,28000,1 386 | 15806901,Female,57,33000,1 387 | 15775335,Male,56,60000,1 388 | 15724150,Female,49,39000,1 389 | 15627220,Male,39,71000,0 390 | 15672330,Male,47,34000,1 391 | 15668521,Female,48,35000,1 392 | 15807837,Male,48,33000,1 393 | 15592570,Male,47,23000,1 394 | 15748589,Female,45,45000,1 395 | 15635893,Male,60,42000,1 396 | 15757632,Female,39,59000,0 397 | 15691863,Female,46,41000,1 398 | 15706071,Male,51,23000,1 399 | 15654296,Female,50,20000,1 400 | 15755018,Male,36,33000,0 401 | 15594041,Female,49,36000,1 -------------------------------------------------------------------------------- /support_vector_machine.py: -------------------------------------------------------------------------------- 1 | # Support Vector Machine 2 | # Importing the libraries 3 | 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | import pandas as pd 7 | 8 | # Importing the datasets 9 | 10 | datasets = pd.read_csv('Social_Network_Ads.csv') 11 | X = datasets.iloc[:, [2,3]].values 12 | Y = datasets.iloc[:, 4].values 13 | 14 | # Splitting the dataset into the Training set and Test set 15 | 16 | from sklearn.model_selection import train_test_split 17 | X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.25, random_state = 0) 18 | 19 | # Feature Scaling 20 | 21 | from sklearn.preprocessing import StandardScaler 22 | sc_X = StandardScaler() 23 | X_Train = sc_X.fit_transform(X_Train) 24 | X_Test = sc_X.transform(X_Test) 25 | 26 | # Fitting the classifier into the Training set 27 | 28 | from sklearn.svm import SVC 29 | classifier = SVC(kernel = 'linear', random_state = 0) 30 | classifier.fit(X_Train, Y_Train) 31 | 32 | # Predicting the test set results 33 | 34 | Y_Pred = classifier.predict(X_Test) 35 | 36 | # Making the Confusion Matrix 37 | 38 | from sklearn.metrics import confusion_matrix 39 | cm = confusion_matrix(Y_Test, Y_Pred) 40 | 41 | # Visualising the Training set results 42 | 43 | from matplotlib.colors import ListedColormap 44 | X_Set, Y_Set = X_Train, Y_Train 45 | X1, X2 = np.meshgrid(np.arange(start = X_Set[:, 0].min() - 1, stop = X_Set[:, 0].max() + 1, step = 0.01), 46 | np.arange(start = X_Set[:, 1].min() - 1, stop = X_Set[:, 1].max() + 1, step = 0.01)) 47 | plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), 48 | alpha = 0.75, cmap = ListedColormap(('red', 'green'))) 49 | plt.xlim(X1.min(), X1.max()) 50 | plt.ylim(X2.min(), X2.max()) 51 | for i, j in enumerate(np.unique(Y_Set)): 52 | plt.scatter(X_Set[Y_Set == j, 0], X_Set[Y_Set == j, 1], 53 | c = ListedColormap(('red', 'green'))(i), label = j) 54 | plt.title('Support Vector Machine (Training set)') 55 | plt.xlabel('Age') 56 | plt.ylabel('Estimated Salary') 57 | plt.legend() 58 | plt.show() 59 | 60 | # Visualising the Test set results 61 | 62 | from matplotlib.colors import ListedColormap 63 | X_Set, Y_Set = X_Test, Y_Test 64 | X1, X2 = np.meshgrid(np.arange(start = X_Set[:, 0].min() - 1, stop = X_Set[:, 0].max() + 1, step = 0.01), 65 | np.arange(start = X_Set[:, 1].min() - 1, stop = X_Set[:, 1].max() + 1, step = 0.01)) 66 | plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), 67 | alpha = 0.75, cmap = ListedColormap(('red', 'green'))) 68 | plt.xlim(X1.min(), X1.max()) 69 | plt.ylim(X2.min(), X2.max()) 70 | for i, j in enumerate(np.unique(Y_Set)): 71 | plt.scatter(X_Set[Y_Set == j, 0], X_Set[Y_Set == j, 1], 72 | c = ListedColormap(('red', 'green'))(i), label = j) 73 | plt.title('Support Vector Machine (Test set)') 74 | plt.xlabel('Age') 75 | plt.ylabel('Estimated Salary') 76 | plt.legend() 77 | plt.show() 78 | --------------------------------------------------------------------------------