Import

Partie 1 - Data Preprocessing/Data.csv

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+Country,Age,Salary,Purchased
+France,44,72000,No
+Spain,27,48000,Yes
+Germany,30,54000,No
+Spain,38,61000,No
+Germany,40,,Yes
+France,35,58000,Yes
+Spain,,52000,No
+France,48,79000,Yes
+Germany,50,83000,No
+France,37,67000,Yes

Partie 1 - Data Preprocessing/data_preprocessing.py

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+# Data Preprocessing
+
+# Importer les librairies
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importer le dataset
+dataset = pd.read_csv('Data.csv')
+X = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, -1].values
+
+# Gérer les données manquantes
+from sklearn.preprocessing import Imputer
+imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
+imputer.fit(X[:, 1:3])
+X[:, 1:3] = imputer.transform(X[:, 1:3])
+
+# Gérer les variables catégoriques
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+labelencoder_X = LabelEncoder()
+X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
+onehotencoder = OneHotEncoder(categorical_features = [0])
+X = onehotencoder.fit_transform(X).toarray()
+labelencoder_y = LabelEncoder()
+y = labelencoder_y.fit_transform(y)
+
+# Diviser le dataset entre le Training set et le Test set
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, 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)

Partie 2 - Régression/50_Startups.csv

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+R&D Spend,Administration,Marketing Spend,State,Profit
+165349.2,136897.8,471784.1,New York,192261.83
+162597.7,151377.59,443898.53,California,191792.06
+153441.51,101145.55,407934.54,Florida,191050.39
+144372.41,118671.85,383199.62,New York,182901.99
+142107.34,91391.77,366168.42,Florida,166187.94
+131876.9,99814.71,362861.36,New York,156991.12
+134615.46,147198.87,127716.82,California,156122.51
+130298.13,145530.06,323876.68,Florida,155752.6
+120542.52,148718.95,311613.29,New York,152211.77
+123334.88,108679.17,304981.62,California,149759.96
+101913.08,110594.11,229160.95,Florida,146121.95
+100671.96,91790.61,249744.55,California,144259.4
+93863.75,127320.38,249839.44,Florida,141585.52
+91992.39,135495.07,252664.93,California,134307.35
+119943.24,156547.42,256512.92,Florida,132602.65
+114523.61,122616.84,261776.23,New York,129917.04
+78013.11,121597.55,264346.06,California,126992.93
+94657.16,145077.58,282574.31,New York,125370.37
+91749.16,114175.79,294919.57,Florida,124266.9
+86419.7,153514.11,0,New York,122776.86
+76253.86,113867.3,298664.47,California,118474.03
+78389.47,153773.43,299737.29,New York,111313.02
+73994.56,122782.75,303319.26,Florida,110352.25
+67532.53,105751.03,304768.73,Florida,108733.99
+77044.01,99281.34,140574.81,New York,108552.04
+64664.71,139553.16,137962.62,California,107404.34
+75328.87,144135.98,134050.07,Florida,105733.54
+72107.6,127864.55,353183.81,New York,105008.31
+66051.52,182645.56,118148.2,Florida,103282.38
+65605.48,153032.06,107138.38,New York,101004.64
+61994.48,115641.28,91131.24,Florida,99937.59
+61136.38,152701.92,88218.23,New York,97483.56
+63408.86,129219.61,46085.25,California,97427.84
+55493.95,103057.49,214634.81,Florida,96778.92
+46426.07,157693.92,210797.67,California,96712.8
+46014.02,85047.44,205517.64,New York,96479.51
+28663.76,127056.21,201126.82,Florida,90708.19
+44069.95,51283.14,197029.42,California,89949.14
+20229.59,65947.93,185265.1,New York,81229.06
+38558.51,82982.09,174999.3,California,81005.76
+28754.33,118546.05,172795.67,California,78239.91
+27892.92,84710.77,164470.71,Florida,77798.83
+23640.93,96189.63,148001.11,California,71498.49
+15505.73,127382.3,35534.17,New York,69758.98
+22177.74,154806.14,28334.72,California,65200.33
+1000.23,124153.04,1903.93,New York,64926.08
+1315.46,115816.21,297114.46,Florida,49490.75
+0,135426.92,0,California,42559.73
+542.05,51743.15,0,New York,35673.41
+0,116983.8,45173.06,California,14681.4

Partie 2 - Régression/Position_Salaries.csv

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+Position,Level,Salary
+Business Analyst,1,45000
+Junior Consultant,2,50000
+Senior Consultant,3,60000
+Manager,4,80000
+Country Manager,5,110000
+Region Manager,6,150000
+Partner,7,200000
+Senior Partner,8,300000
+C-level,9,500000
+CEO,10,1000000

Partie 2 - Régression/Salary_Data.csv

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+YearsExperience,Salary
+1.1,39343.00
+1.3,46205.00
+1.5,37731.00
+2.0,43525.00
+2.2,39891.00
+2.9,56642.00
+3.0,60150.00
+3.2,54445.00
+3.2,64445.00
+3.7,57189.00
+3.9,63218.00
+4.0,55794.00
+4.0,56957.00
+4.1,57081.00
+4.5,61111.00
+4.9,67938.00
+5.1,66029.00
+5.3,83088.00
+5.9,81363.00
+6.0,93940.00
+6.8,91738.00
+7.1,98273.00
+7.9,101302.00
+8.2,113812.00
+8.7,109431.00
+9.0,105582.00
+9.5,116969.00
+9.6,112635.00
+10.3,122391.00
+10.5,121872.00

Partie 2 - Régression/regression_lineaire_multiple.py

@@ -0,0 +1,32 @@
+# Regression Linéaire Multiple
+
+# Importer les librairies
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importer le dataset
+dataset = pd.read_csv('50_Startups.csv')
+X = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, -1].values
+
+# Gérer les variables catégoriques
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+labelencoder_X = LabelEncoder()
+X[:, 3] = labelencoder_X.fit_transform(X[:, 3])
+onehotencoder = OneHotEncoder(categorical_features = [3])
+X = onehotencoder.fit_transform(X).toarray()
+X = X[:, 1:]
+
+# Diviser le dataset entre le Training set et le Test set
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
+
+# Construction du modèle
+from sklearn.linear_model import LinearRegression
+regressor = LinearRegression()
+regressor.fit(X_train, y_train)
+
+# Faire de nouvelles prédictions
+y_pred = regressor.predict(X_test)
+regressor.predict(np.array([[1, 0, 130000, 140000, 300000]]))

Partie 2 - Régression/regression_lineaire_simple.py

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+# Regression Linéaire Simple
+
+# Importer les librairies
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importer le dataset
+dataset = pd.read_csv('Salary_Data.csv')
+X = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, -1].values
+
+# Diviser le dataset entre le Training set et le Test set
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1.0/3, random_state = 0)
+
+# Construction du modèle
+from sklearn.linear_model import LinearRegression
+regressor = LinearRegression()
+regressor.fit(X_train, y_train)
+
+# Faire de nouvelles prédictions
+y_pred = regressor.predict(X_test)
+regressor.predict(15)
+
+# Visualiser les résultats
+plt.scatter(X_test, y_test, color = 'red')
+plt.plot(X_train, regressor.predict(X_train), color = 'blue')
+plt.title('Salaire vs Experience')
+plt.xlabel('Experience')
+plt.ylabel('Salaire')
+plt.show()

Partie 2 - Régression/regression_polynomiale.py

@@ -0,0 +1,40 @@
+# Regression Polynomiale
+
+# Importer les librairies
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importer le dataset
+dataset = pd.read_csv('Position_Salaries.csv')
+X = dataset.iloc[:, 1:2].values
+y = dataset.iloc[:, -1].values
+
+# Construction du modèle
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import PolynomialFeatures
+poly_reg = PolynomialFeatures(degree = 4)
+X_poly = poly_reg.fit_transform(X)
+regressor = LinearRegression()
+regressor.fit(X_poly, y)
+
+# Faire de nouvelles prédictions
+regressor.predict(15)
+
+# Visualiser les résultats
+plt.scatter(X, y, color = 'red')
+plt.plot(X, regressor.predict(X_poly), color = 'blue')
+plt.title('Salaire vs Experience')
+plt.xlabel('Experience')
+plt.ylabel('Salaire')
+plt.show()
+
+# Visualiser les résultats (courbe plus lisse)
+X_grid = np.arange(min(X), max(X), 0.1)
+X_grid = X_grid.reshape((len(X_grid), 1))
+plt.scatter(X, y, color = 'red')
+plt.plot(X_grid, regressor.predict(poly_reg.fit_transform(X_grid)), color = 'blue')
+plt.title('Salaire vs Experience')
+plt.xlabel('Experience')
+plt.ylabel('Salaire')
+plt.show()

Partie 3 - 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
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+15613014,Female,52,38000,1
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+15725794,Female,36,63000,0
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+15705298,Female,43,112000,1
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+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
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+15800215,Female,42,53000,0
+15577806,Male,41,87000,1
+15749381,Female,58,23000,1
+15683758,Male,42,64000,0
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+15715622,Female,44,139000,1
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+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

Partie 3 - Classification/regression_logistique.py

@@ -0,0 +1,51 @@
+# Regression Logistique
+
+# Importer les librairies
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importer le dataset
+dataset = pd.read_csv('Social_Network_Ads.csv')
+X = dataset.iloc[:, [2, 3]].values
+y = dataset.iloc[:, -1].values
+
+# Diviser le dataset entre le Training set et le Test set
+from sklearn.model_selection 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)
+
+# Construction du modèle
+from sklearn.linear_model import LogisticRegression
+classifier = LogisticRegression(random_state = 0)
+classifier.fit(X_train, y_train)
+
+# Faire de nouvelles prédictions
+y_pred = classifier.predict(X_test)
+
+# Matrice de confusion
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)
+
+# Visualiser les résultats
+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 = 0.4, 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('Résultats du Training set')
+plt.xlabel('Age')
+plt.ylabel('Salaire Estimé')
+plt.legend()
+plt.show()

Partie 3 - Classification/svm.py

@@ -0,0 +1,51 @@
+# SVM
+
+# Importer les librairies
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importer le dataset
+dataset = pd.read_csv('Social_Network_Ads.csv')
+X = dataset.iloc[:, [2, 3]].values
+y = dataset.iloc[:, -1].values
+
+# Diviser le dataset entre le Training set et le Test set
+from sklearn.model_selection 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)
+
+# Construction du modèle
+from sklearn.svm import SVC
+classifier = SVC(kernel = 'linear', random_state = 0)
+classifier.fit(X_train, y_train)
+
+# Faire de nouvelles prédictions
+y_pred = classifier.predict(X_test)
+
+# Matrice de confusion
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)
+
+# Visualiser les résultats
+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 = 0.4, 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('Résultats du Training set')
+plt.xlabel('Age')
+plt.ylabel('Salaire Estimé')
+plt.legend()
+plt.show()

Partie 4 - Clustering/Mall_Customers.csv

@@ -0,0 +1,201 @@
+CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100)
+0001,Male,19,15,39
+0002,Male,21,15,81
+0003,Female,20,16,6
+0004,Female,23,16,77
+0005,Female,31,17,40
+0006,Female,22,17,76
+0007,Female,35,18,6
+0008,Female,23,18,94
+0009,Male,64,19,3
+0010,Female,30,19,72
+0011,Male,67,19,14
+0012,Female,35,19,99
+0013,Female,58,20,15
+0014,Female,24,20,77
+0015,Male,37,20,13
+0016,Male,22,20,79
+0017,Female,35,21,35
+0018,Male,20,21,66
+0019,Male,52,23,29
+0020,Female,35,23,98
+0021,Male,35,24,35
+0022,Male,25,24,73
+0023,Female,46,25,5
+0024,Male,31,25,73
+0025,Female,54,28,14
+0026,Male,29,28,82
+0027,Female,45,28,32
+0028,Male,35,28,61
+0029,Female,40,29,31
+0030,Female,23,29,87
+0031,Male,60,30,4
+0032,Female,21,30,73
+0033,Male,53,33,4
+0034,Male,18,33,92
+0035,Female,49,33,14
+0036,Female,21,33,81
+0037,Female,42,34,17
+0038,Female,30,34,73
+0039,Female,36,37,26
+0040,Female,20,37,75
+0041,Female,65,38,35
+0042,Male,24,38,92
+0043,Male,48,39,36
+0044,Female,31,39,61
+0045,Female,49,39,28
+0046,Female,24,39,65
+0047,Female,50,40,55
+0048,Female,27,40,47
+0049,Female,29,40,42
+0050,Female,31,40,42
+0051,Female,49,42,52
+0052,Male,33,42,60
+0053,Female,31,43,54
+0054,Male,59,43,60
+0055,Female,50,43,45
+0056,Male,47,43,41
+0057,Female,51,44,50
+0058,Male,69,44,46
+0059,Female,27,46,51
+0060,Male,53,46,46
+0061,Male,70,46,56
+0062,Male,19,46,55
+0063,Female,67,47,52
+0064,Female,54,47,59
+0065,Male,63,48,51
+0066,Male,18,48,59
+0067,Female,43,48,50
+0068,Female,68,48,48
+0069,Male,19,48,59
+0070,Female,32,48,47
+0071,Male,70,49,55
+0072,Female,47,49,42
+0073,Female,60,50,49
+0074,Female,60,50,56
+0075,Male,59,54,47
+0076,Male,26,54,54
+0077,Female,45,54,53
+0078,Male,40,54,48
+0079,Female,23,54,52
+0080,Female,49,54,42
+0081,Male,57,54,51
+0082,Male,38,54,55
+0083,Male,67,54,41
+0084,Female,46,54,44
+0085,Female,21,54,57
+0086,Male,48,54,46
+0087,Female,55,57,58
+0088,Female,22,57,55
+0089,Female,34,58,60
+0090,Female,50,58,46
+0091,Female,68,59,55
+0092,Male,18,59,41
+0093,Male,48,60,49
+0094,Female,40,60,40
+0095,Female,32,60,42
+0096,Male,24,60,52
+0097,Female,47,60,47
+0098,Female,27,60,50
+0099,Male,48,61,42
+0100,Male,20,61,49
+0101,Female,23,62,41
+0102,Female,49,62,48
+0103,Male,67,62,59
+0104,Male,26,62,55
+0105,Male,49,62,56
+0106,Female,21,62,42
+0107,Female,66,63,50
+0108,Male,54,63,46
+0109,Male,68,63,43
+0110,Male,66,63,48
+0111,Male,65,63,52
+0112,Female,19,63,54
+0113,Female,38,64,42
+0114,Male,19,64,46
+0115,Female,18,65,48
+0116,Female,19,65,50
+0117,Female,63,65,43
+0118,Female,49,65,59
+0119,Female,51,67,43
+0120,Female,50,67,57
+0121,Male,27,67,56
+0122,Female,38,67,40
+0123,Female,40,69,58
+0124,Male,39,69,91
+0125,Female,23,70,29
+0126,Female,31,70,77
+0127,Male,43,71,35
+0128,Male,40,71,95
+0129,Male,59,71,11
+0130,Male,38,71,75
+0131,Male,47,71,9
+0132,Male,39,71,75
+0133,Female,25,72,34
+0134,Female,31,72,71
+0135,Male,20,73,5
+0136,Female,29,73,88
+0137,Female,44,73,7
+0138,Male,32,73,73
+0139,Male,19,74,10
+0140,Female,35,74,72
+0141,Female,57,75,5
+0142,Male,32,75,93
+0143,Female,28,76,40
+0144,Female,32,76,87
+0145,Male,25,77,12
+0146,Male,28,77,97
+0147,Male,48,77,36
+0148,Female,32,77,74
+0149,Female,34,78,22
+0150,Male,34,78,90
+0151,Male,43,78,17
+0152,Male,39,78,88
+0153,Female,44,78,20
+0154,Female,38,78,76
+0155,Female,47,78,16
+0156,Female,27,78,89
+0157,Male,37,78,1
+0158,Female,30,78,78
+0159,Male,34,78,1
+0160,Female,30,78,73
+0161,Female,56,79,35
+0162,Female,29,79,83
+0163,Male,19,81,5
+0164,Female,31,81,93
+0165,Male,50,85,26
+0166,Female,36,85,75
+0167,Male,42,86,20
+0168,Female,33,86,95
+0169,Female,36,87,27
+0170,Male,32,87,63
+0171,Male,40,87,13
+0172,Male,28,87,75
+0173,Male,36,87,10
+0174,Male,36,87,92
+0175,Female,52,88,13
+0176,Female,30,88,86
+0177,Male,58,88,15
+0178,Male,27,88,69
+0179,Male,59,93,14
+0180,Male,35,93,90
+0181,Female,37,97,32
+0182,Female,32,97,86
+0183,Male,46,98,15
+0184,Female,29,98,88
+0185,Female,41,99,39
+0186,Male,30,99,97
+0187,Female,54,101,24
+0188,Male,28,101,68
+0189,Female,41,103,17
+0190,Female,36,103,85
+0191,Female,34,103,23
+0192,Female,32,103,69
+0193,Male,33,113,8
+0194,Female,38,113,91
+0195,Female,47,120,16
+0196,Female,35,120,79
+0197,Female,45,126,28
+0198,Male,32,126,74
+0199,Male,32,137,18
+0200,Male,30,137,83

Partie 4 - Clustering/k_means.py

@@ -0,0 +1,39 @@
+# K-Means
+
+# Importer les librairies
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importer le dataset
+dataset = pd.read_csv('Mall_Customers.csv')
+X = dataset.iloc[:, [3, 4]].values
+
+# Utiliser la méthode elbow pour trouver le nombre optimal de clusters
+from sklearn.cluster import KMeans
+wcss = []
+for i in range(1, 11):
+    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 0)
+    kmeans.fit(X)
+    wcss.append(kmeans.inertia_)
+plt.plot(range(1, 11), wcss)
+plt.title('La méthode Elbow')
+plt.xlabel('Nombre de clusters')
+plt.ylabel('WCSS')
+plt.show()
+
+# Construction du modèle
+from sklearn.cluster import KMeans
+kmeans = KMeans(n_clusters = 5, init = 'k-means++', random_state = 0)
+y_kmeans = kmeans.fit_predict(X)
+
+# Visualiser les résultats
+plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], c = 'red', label = 'Cluster 1')
+plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], c = 'blue', label = 'Cluster 2')
+plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], c = 'green', label = 'Cluster 3')
+plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], c = 'cyan', label = 'Cluster 4')
+plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], c = 'magenta', label = 'Cluster 5')
+plt.title('Clusters de clients')
+plt.xlabel('Salaire annuel')
+plt.ylabel('Spending Score')
+plt.legend()

README.md

@@ -0,0 +1,3 @@
+# Introduction to Machine Learning
+The codes are from the following course:
+- [Introduction au Machine Learning | Udemy](https://www.udemy.com/course/introduction-au-machine-learning/)