Random Forest Classification added

machine_learning/Random Forest Classification/Social_Network_Ads.csv

@@ -0,0 +1,401 @@
+User ID,Gender,Age,EstimatedSalary,Purchased
+15624510,Male,19,19000,0
+15810944,Male,35,20000,0
+15668575,Female,26,43000,0
+15603246,Female,27,57000,0
+15804002,Male,19,76000,0
+15728773,Male,27,58000,0
+15598044,Female,27,84000,0
+15694829,Female,32,150000,1
+15600575,Male,25,33000,0
+15727311,Female,35,65000,0
+15570769,Female,26,80000,0
+15606274,Female,26,52000,0
+15746139,Male,20,86000,0
+15704987,Male,32,18000,0
+15628972,Male,18,82000,0
+15697686,Male,29,80000,0
+15733883,Male,47,25000,1
+15617482,Male,45,26000,1
+15704583,Male,46,28000,1
+15621083,Female,48,29000,1
+15649487,Male,45,22000,1
+15736760,Female,47,49000,1
+15714658,Male,48,41000,1
+15599081,Female,45,22000,1
+15705113,Male,46,23000,1
+15631159,Male,47,20000,1
+15792818,Male,49,28000,1
+15633531,Female,47,30000,1
+15744529,Male,29,43000,0
+15669656,Male,31,18000,0
+15581198,Male,31,74000,0
+15729054,Female,27,137000,1
+15573452,Female,21,16000,0
+15776733,Female,28,44000,0
+15724858,Male,27,90000,0
+15713144,Male,35,27000,0
+15690188,Female,33,28000,0
+15689425,Male,30,49000,0
+15671766,Female,26,72000,0
+15782806,Female,27,31000,0
+15764419,Female,27,17000,0
+15591915,Female,33,51000,0
+15772798,Male,35,108000,0
+15792008,Male,30,15000,0
+15715541,Female,28,84000,0
+15639277,Male,23,20000,0
+15798850,Male,25,79000,0
+15776348,Female,27,54000,0
+15727696,Male,30,135000,1
+15793813,Female,31,89000,0
+15694395,Female,24,32000,0
+15764195,Female,18,44000,0
+15744919,Female,29,83000,0
+15671655,Female,35,23000,0
+15654901,Female,27,58000,0
+15649136,Female,24,55000,0
+15775562,Female,23,48000,0
+15807481,Male,28,79000,0
+15642885,Male,22,18000,0
+15789109,Female,32,117000,0
+15814004,Male,27,20000,0
+15673619,Male,25,87000,0
+15595135,Female,23,66000,0
+15583681,Male,32,120000,1
+15605000,Female,59,83000,0
+15718071,Male,24,58000,0
+15679760,Male,24,19000,0
+15654574,Female,23,82000,0
+15577178,Female,22,63000,0
+15595324,Female,31,68000,0
+15756932,Male,25,80000,0
+15726358,Female,24,27000,0
+15595228,Female,20,23000,0
+15782530,Female,33,113000,0
+15592877,Male,32,18000,0
+15651983,Male,34,112000,1
+15746737,Male,18,52000,0
+15774179,Female,22,27000,0
+15667265,Female,28,87000,0
+15655123,Female,26,17000,0
+15595917,Male,30,80000,0
+15668385,Male,39,42000,0
+15709476,Male,20,49000,0
+15711218,Male,35,88000,0
+15798659,Female,30,62000,0
+15663939,Female,31,118000,1
+15694946,Male,24,55000,0
+15631912,Female,28,85000,0
+15768816,Male,26,81000,0
+15682268,Male,35,50000,0
+15684801,Male,22,81000,0
+15636428,Female,30,116000,0
+15809823,Male,26,15000,0
+15699284,Female,29,28000,0
+15786993,Female,29,83000,0
+15709441,Female,35,44000,0
+15710257,Female,35,25000,0
+15582492,Male,28,123000,1
+15575694,Male,35,73000,0
+15756820,Female,28,37000,0
+15766289,Male,27,88000,0
+15593014,Male,28,59000,0
+15584545,Female,32,86000,0
+15675949,Female,33,149000,1
+15672091,Female,19,21000,0
+15801658,Male,21,72000,0
+15706185,Female,26,35000,0
+15789863,Male,27,89000,0
+15720943,Male,26,86000,0
+15697997,Female,38,80000,0
+15665416,Female,39,71000,0
+15660200,Female,37,71000,0
+15619653,Male,38,61000,0
+15773447,Male,37,55000,0
+15739160,Male,42,80000,0
+15689237,Male,40,57000,0
+15679297,Male,35,75000,0
+15591433,Male,36,52000,0
+15642725,Male,40,59000,0
+15701962,Male,41,59000,0
+15811613,Female,36,75000,0
+15741049,Male,37,72000,0
+15724423,Female,40,75000,0
+15574305,Male,35,53000,0
+15678168,Female,41,51000,0
+15697020,Female,39,61000,0
+15610801,Male,42,65000,0
+15745232,Male,26,32000,0
+15722758,Male,30,17000,0
+15792102,Female,26,84000,0
+15675185,Male,31,58000,0
+15801247,Male,33,31000,0
+15725660,Male,30,87000,0
+15638963,Female,21,68000,0
+15800061,Female,28,55000,0
+15578006,Male,23,63000,0
+15668504,Female,20,82000,0
+15687491,Male,30,107000,1
+15610403,Female,28,59000,0
+15741094,Male,19,25000,0
+15807909,Male,19,85000,0
+15666141,Female,18,68000,0
+15617134,Male,35,59000,0
+15783029,Male,30,89000,0
+15622833,Female,34,25000,0
+15746422,Female,24,89000,0
+15750839,Female,27,96000,1
+15749130,Female,41,30000,0
+15779862,Male,29,61000,0
+15767871,Male,20,74000,0
+15679651,Female,26,15000,0
+15576219,Male,41,45000,0
+15699247,Male,31,76000,0
+15619087,Female,36,50000,0
+15605327,Male,40,47000,0
+15610140,Female,31,15000,0
+15791174,Male,46,59000,0
+15602373,Male,29,75000,0
+15762605,Male,26,30000,0
+15598840,Female,32,135000,1
+15744279,Male,32,100000,1
+15670619,Male,25,90000,0
+15599533,Female,37,33000,0
+15757837,Male,35,38000,0
+15697574,Female,33,69000,0
+15578738,Female,18,86000,0
+15762228,Female,22,55000,0
+15614827,Female,35,71000,0
+15789815,Male,29,148000,1
+15579781,Female,29,47000,0
+15587013,Male,21,88000,0
+15570932,Male,34,115000,0
+15794661,Female,26,118000,0
+15581654,Female,34,43000,0
+15644296,Female,34,72000,0
+15614420,Female,23,28000,0
+15609653,Female,35,47000,0
+15594577,Male,25,22000,0
+15584114,Male,24,23000,0
+15673367,Female,31,34000,0
+15685576,Male,26,16000,0
+15774727,Female,31,71000,0
+15694288,Female,32,117000,1
+15603319,Male,33,43000,0
+15759066,Female,33,60000,0
+15814816,Male,31,66000,0
+15724402,Female,20,82000,0
+15571059,Female,33,41000,0
+15674206,Male,35,72000,0
+15715160,Male,28,32000,0
+15730448,Male,24,84000,0
+15662067,Female,19,26000,0
+15779581,Male,29,43000,0
+15662901,Male,19,70000,0
+15689751,Male,28,89000,0
+15667742,Male,34,43000,0
+15738448,Female,30,79000,0
+15680243,Female,20,36000,0
+15745083,Male,26,80000,0
+15708228,Male,35,22000,0
+15628523,Male,35,39000,0
+15708196,Male,49,74000,0
+15735549,Female,39,134000,1
+15809347,Female,41,71000,0
+15660866,Female,58,101000,1
+15766609,Female,47,47000,0
+15654230,Female,55,130000,1
+15794566,Female,52,114000,0
+15800890,Female,40,142000,1
+15697424,Female,46,22000,0
+15724536,Female,48,96000,1
+15735878,Male,52,150000,1
+15707596,Female,59,42000,0
+15657163,Male,35,58000,0
+15622478,Male,47,43000,0
+15779529,Female,60,108000,1
+15636023,Male,49,65000,0
+15582066,Male,40,78000,0
+15666675,Female,46,96000,0
+15732987,Male,59,143000,1
+15789432,Female,41,80000,0
+15663161,Male,35,91000,1
+15694879,Male,37,144000,1
+15593715,Male,60,102000,1
+15575002,Female,35,60000,0
+15622171,Male,37,53000,0
+15795224,Female,36,126000,1
+15685346,Male,56,133000,1
+15691808,Female,40,72000,0
+15721007,Female,42,80000,1
+15794253,Female,35,147000,1
+15694453,Male,39,42000,0
+15813113,Male,40,107000,1
+15614187,Male,49,86000,1
+15619407,Female,38,112000,0
+15646227,Male,46,79000,1
+15660541,Male,40,57000,0
+15753874,Female,37,80000,0
+15617877,Female,46,82000,0
+15772073,Female,53,143000,1
+15701537,Male,42,149000,1
+15736228,Male,38,59000,0
+15780572,Female,50,88000,1
+15769596,Female,56,104000,1
+15586996,Female,41,72000,0
+15722061,Female,51,146000,1
+15638003,Female,35,50000,0
+15775590,Female,57,122000,1
+15730688,Male,41,52000,0
+15753102,Female,35,97000,1
+15810075,Female,44,39000,0
+15723373,Male,37,52000,0
+15795298,Female,48,134000,1
+15584320,Female,37,146000,1
+15724161,Female,50,44000,0
+15750056,Female,52,90000,1
+15609637,Female,41,72000,0
+15794493,Male,40,57000,0
+15569641,Female,58,95000,1
+15815236,Female,45,131000,1
+15811177,Female,35,77000,0
+15680587,Male,36,144000,1
+15672821,Female,55,125000,1
+15767681,Female,35,72000,0
+15600379,Male,48,90000,1
+15801336,Female,42,108000,1
+15721592,Male,40,75000,0
+15581282,Male,37,74000,0
+15746203,Female,47,144000,1
+15583137,Male,40,61000,0
+15680752,Female,43,133000,0
+15688172,Female,59,76000,1
+15791373,Male,60,42000,1
+15589449,Male,39,106000,1
+15692819,Female,57,26000,1
+15727467,Male,57,74000,1
+15734312,Male,38,71000,0
+15764604,Male,49,88000,1
+15613014,Female,52,38000,1
+15759684,Female,50,36000,1
+15609669,Female,59,88000,1
+15685536,Male,35,61000,0
+15750447,Male,37,70000,1
+15663249,Female,52,21000,1
+15638646,Male,48,141000,0
+15734161,Female,37,93000,1
+15631070,Female,37,62000,0
+15761950,Female,48,138000,1
+15649668,Male,41,79000,0
+15713912,Female,37,78000,1
+15586757,Male,39,134000,1
+15596522,Male,49,89000,1
+15625395,Male,55,39000,1
+15760570,Male,37,77000,0
+15566689,Female,35,57000,0
+15725794,Female,36,63000,0
+15673539,Male,42,73000,1
+15705298,Female,43,112000,1
+15675791,Male,45,79000,0
+15747043,Male,46,117000,1
+15736397,Female,58,38000,1
+15678201,Male,48,74000,1
+15720745,Female,37,137000,1
+15637593,Male,37,79000,1
+15598070,Female,40,60000,0
+15787550,Male,42,54000,0
+15603942,Female,51,134000,0
+15733973,Female,47,113000,1
+15596761,Male,36,125000,1
+15652400,Female,38,50000,0
+15717893,Female,42,70000,0
+15622585,Male,39,96000,1
+15733964,Female,38,50000,0
+15753861,Female,49,141000,1
+15747097,Female,39,79000,0
+15594762,Female,39,75000,1
+15667417,Female,54,104000,1
+15684861,Male,35,55000,0
+15742204,Male,45,32000,1
+15623502,Male,36,60000,0
+15774872,Female,52,138000,1
+15611191,Female,53,82000,1
+15674331,Male,41,52000,0
+15619465,Female,48,30000,1
+15575247,Female,48,131000,1
+15695679,Female,41,60000,0
+15713463,Male,41,72000,0
+15785170,Female,42,75000,0
+15796351,Male,36,118000,1
+15639576,Female,47,107000,1
+15693264,Male,38,51000,0
+15589715,Female,48,119000,1
+15769902,Male,42,65000,0
+15587177,Male,40,65000,0
+15814553,Male,57,60000,1
+15601550,Female,36,54000,0
+15664907,Male,58,144000,1
+15612465,Male,35,79000,0
+15810800,Female,38,55000,0
+15665760,Male,39,122000,1
+15588080,Female,53,104000,1
+15776844,Male,35,75000,0
+15717560,Female,38,65000,0
+15629739,Female,47,51000,1
+15729908,Male,47,105000,1
+15716781,Female,41,63000,0
+15646936,Male,53,72000,1
+15768151,Female,54,108000,1
+15579212,Male,39,77000,0
+15721835,Male,38,61000,0
+15800515,Female,38,113000,1
+15591279,Male,37,75000,0
+15587419,Female,42,90000,1
+15750335,Female,37,57000,0
+15699619,Male,36,99000,1
+15606472,Male,60,34000,1
+15778368,Male,54,70000,1
+15671387,Female,41,72000,0
+15573926,Male,40,71000,1
+15709183,Male,42,54000,0
+15577514,Male,43,129000,1
+15778830,Female,53,34000,1
+15768072,Female,47,50000,1
+15768293,Female,42,79000,0
+15654456,Male,42,104000,1
+15807525,Female,59,29000,1
+15574372,Female,58,47000,1
+15671249,Male,46,88000,1
+15779744,Male,38,71000,0
+15624755,Female,54,26000,1
+15611430,Female,60,46000,1
+15774744,Male,60,83000,1
+15629885,Female,39,73000,0
+15708791,Male,59,130000,1
+15793890,Female,37,80000,0
+15646091,Female,46,32000,1
+15596984,Female,46,74000,0
+15800215,Female,42,53000,0
+15577806,Male,41,87000,1
+15749381,Female,58,23000,1
+15683758,Male,42,64000,0
+15670615,Male,48,33000,1
+15715622,Female,44,139000,1
+15707634,Male,49,28000,1
+15806901,Female,57,33000,1
+15775335,Male,56,60000,1
+15724150,Female,49,39000,1
+15627220,Male,39,71000,0
+15672330,Male,47,34000,1
+15668521,Female,48,35000,1
+15807837,Male,48,33000,1
+15592570,Male,47,23000,1
+15748589,Female,45,45000,1
+15635893,Male,60,42000,1
+15757632,Female,39,59000,0
+15691863,Female,46,41000,1
+15706071,Male,51,23000,1
+15654296,Female,50,20000,1
+15755018,Male,36,33000,0
+15594041,Female,49,36000,1

machine_learning/Random Forest Classification/random_forest_classification.py

@@ -0,0 +1,69 @@
+# Random Forest Classification
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Social_Network_Ads.csv')
+X = dataset.iloc[:, [2, 3]].values
+y = dataset.iloc[:, 4].values
+
+# Splitting the dataset into the Training set and Test set
+from sklearn.cross_validation import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
+
+# Feature Scaling
+from sklearn.preprocessing import StandardScaler
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+
+# Fitting Random Forest Classification to the Training set
+from sklearn.ensemble import RandomForestClassifier
+classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
+classifier.fit(X_train, y_train)
+
+# Predicting the Test set results
+y_pred = classifier.predict(X_test)
+
+# Making the Confusion Matrix
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)
+
+# Visualising the Training set results
+from matplotlib.colors import ListedColormap
+X_set, y_set = X_train, y_train
+X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
+                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
+plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
+             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(), X1.max())
+plt.ylim(X2.min(), X2.max())
+for i, j in enumerate(np.unique(y_set)):
+    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
+                c = ListedColormap(('red', 'green'))(i), label = j)
+plt.title('Random Forest Classification (Training set)')
+plt.xlabel('Age')
+plt.ylabel('Estimated Salary')
+plt.legend()
+plt.show()
+
+# Visualising the Test set results
+from matplotlib.colors import ListedColormap
+X_set, y_set = X_test, y_test
+X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
+                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
+plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
+             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(), X1.max())
+plt.ylim(X2.min(), X2.max())
+for i, j in enumerate(np.unique(y_set)):
+    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
+                c = ListedColormap(('red', 'green'))(i), label = j)
+plt.title('Random Forest Classification (Test set)')
+plt.xlabel('Age')
+plt.ylabel('Estimated Salary')
+plt.legend()
+plt.show()