Import
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Country,Age,Salary,Purchased
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France,44,72000,No
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Spain,27,48000,Yes
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Germany,30,54000,No
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Spain,38,61000,No
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Germany,40,,Yes
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France,35,58000,Yes
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Spain,,52000,No
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France,48,79000,Yes
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Germany,50,83000,No
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France,37,67000,Yes
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# Data Preprocessing
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# Importer les librairies
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# Importer le dataset
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dataset = pd.read_csv('Data.csv')
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X = dataset.iloc[:, :-1].values
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y = dataset.iloc[:, -1].values
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# Gérer les données manquantes
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from sklearn.preprocessing import Imputer
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imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
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imputer.fit(X[:, 1:3])
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X[:, 1:3] = imputer.transform(X[:, 1:3])
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# Gérer les variables catégoriques
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from sklearn.preprocessing import LabelEncoder, OneHotEncoder
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labelencoder_X = LabelEncoder()
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X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
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onehotencoder = OneHotEncoder(categorical_features = [0])
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X = onehotencoder.fit_transform(X).toarray()
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labelencoder_y = LabelEncoder()
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y = labelencoder_y.fit_transform(y)
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# Diviser le dataset entre le Training set et le Test set
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
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# Feature Scaling
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from sklearn.preprocessing import StandardScaler
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sc = StandardScaler()
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X_train = sc.fit_transform(X_train)
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X_test = sc.transform(X_test)
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@ -0,0 +1,51 @@
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R&D Spend,Administration,Marketing Spend,State,Profit
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165349.2,136897.8,471784.1,New York,192261.83
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162597.7,151377.59,443898.53,California,191792.06
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153441.51,101145.55,407934.54,Florida,191050.39
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144372.41,118671.85,383199.62,New York,182901.99
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142107.34,91391.77,366168.42,Florida,166187.94
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131876.9,99814.71,362861.36,New York,156991.12
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134615.46,147198.87,127716.82,California,156122.51
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130298.13,145530.06,323876.68,Florida,155752.6
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120542.52,148718.95,311613.29,New York,152211.77
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123334.88,108679.17,304981.62,California,149759.96
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101913.08,110594.11,229160.95,Florida,146121.95
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100671.96,91790.61,249744.55,California,144259.4
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93863.75,127320.38,249839.44,Florida,141585.52
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91992.39,135495.07,252664.93,California,134307.35
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119943.24,156547.42,256512.92,Florida,132602.65
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114523.61,122616.84,261776.23,New York,129917.04
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78013.11,121597.55,264346.06,California,126992.93
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94657.16,145077.58,282574.31,New York,125370.37
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91749.16,114175.79,294919.57,Florida,124266.9
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86419.7,153514.11,0,New York,122776.86
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76253.86,113867.3,298664.47,California,118474.03
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78389.47,153773.43,299737.29,New York,111313.02
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73994.56,122782.75,303319.26,Florida,110352.25
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67532.53,105751.03,304768.73,Florida,108733.99
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77044.01,99281.34,140574.81,New York,108552.04
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64664.71,139553.16,137962.62,California,107404.34
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75328.87,144135.98,134050.07,Florida,105733.54
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72107.6,127864.55,353183.81,New York,105008.31
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66051.52,182645.56,118148.2,Florida,103282.38
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65605.48,153032.06,107138.38,New York,101004.64
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61994.48,115641.28,91131.24,Florida,99937.59
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61136.38,152701.92,88218.23,New York,97483.56
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63408.86,129219.61,46085.25,California,97427.84
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55493.95,103057.49,214634.81,Florida,96778.92
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46426.07,157693.92,210797.67,California,96712.8
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46014.02,85047.44,205517.64,New York,96479.51
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28663.76,127056.21,201126.82,Florida,90708.19
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44069.95,51283.14,197029.42,California,89949.14
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20229.59,65947.93,185265.1,New York,81229.06
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38558.51,82982.09,174999.3,California,81005.76
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28754.33,118546.05,172795.67,California,78239.91
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27892.92,84710.77,164470.71,Florida,77798.83
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23640.93,96189.63,148001.11,California,71498.49
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15505.73,127382.3,35534.17,New York,69758.98
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22177.74,154806.14,28334.72,California,65200.33
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1000.23,124153.04,1903.93,New York,64926.08
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1315.46,115816.21,297114.46,Florida,49490.75
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0,135426.92,0,California,42559.73
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542.05,51743.15,0,New York,35673.41
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0,116983.8,45173.06,California,14681.4
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Position,Level,Salary
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Business Analyst,1,45000
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Junior Consultant,2,50000
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Senior Consultant,3,60000
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Manager,4,80000
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Country Manager,5,110000
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Region Manager,6,150000
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Partner,7,200000
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Senior Partner,8,300000
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C-level,9,500000
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CEO,10,1000000
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@ -0,0 +1,31 @@
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YearsExperience,Salary
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1.1,39343.00
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1.3,46205.00
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1.5,37731.00
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2.0,43525.00
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2.2,39891.00
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2.9,56642.00
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3.0,60150.00
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3.2,54445.00
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3.2,64445.00
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3.7,57189.00
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3.9,63218.00
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4.0,55794.00
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4.0,56957.00
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4.1,57081.00
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4.5,61111.00
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4.9,67938.00
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5.1,66029.00
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5.3,83088.00
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5.9,81363.00
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6.0,93940.00
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6.8,91738.00
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7.1,98273.00
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7.9,101302.00
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8.2,113812.00
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8.7,109431.00
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9.0,105582.00
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9.5,116969.00
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9.6,112635.00
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10.3,122391.00
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10.5,121872.00
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@ -0,0 +1,32 @@
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# Regression Linéaire Multiple
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# Importer les librairies
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# Importer le dataset
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dataset = pd.read_csv('50_Startups.csv')
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X = dataset.iloc[:, :-1].values
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y = dataset.iloc[:, -1].values
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# Gérer les variables catégoriques
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from sklearn.preprocessing import LabelEncoder, OneHotEncoder
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labelencoder_X = LabelEncoder()
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X[:, 3] = labelencoder_X.fit_transform(X[:, 3])
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onehotencoder = OneHotEncoder(categorical_features = [3])
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X = onehotencoder.fit_transform(X).toarray()
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X = X[:, 1:]
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# Diviser le dataset entre le Training set et le Test set
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
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# Construction du modèle
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from sklearn.linear_model import LinearRegression
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regressor = LinearRegression()
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regressor.fit(X_train, y_train)
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# Faire de nouvelles prédictions
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y_pred = regressor.predict(X_test)
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regressor.predict(np.array([[1, 0, 130000, 140000, 300000]]))
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@ -0,0 +1,32 @@
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# Regression Linéaire Simple
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# Importer les librairies
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# Importer le dataset
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dataset = pd.read_csv('Salary_Data.csv')
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X = dataset.iloc[:, :-1].values
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y = dataset.iloc[:, -1].values
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# Diviser le dataset entre le Training set et le Test set
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1.0/3, random_state = 0)
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# Construction du modèle
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from sklearn.linear_model import LinearRegression
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regressor = LinearRegression()
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regressor.fit(X_train, y_train)
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# Faire de nouvelles prédictions
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y_pred = regressor.predict(X_test)
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regressor.predict(15)
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# Visualiser les résultats
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plt.scatter(X_test, y_test, color = 'red')
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plt.plot(X_train, regressor.predict(X_train), color = 'blue')
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plt.title('Salaire vs Experience')
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plt.xlabel('Experience')
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plt.ylabel('Salaire')
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plt.show()
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@ -0,0 +1,40 @@
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# Regression Polynomiale
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|
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# Importer les librairies
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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|
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# Importer le dataset
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dataset = pd.read_csv('Position_Salaries.csv')
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X = dataset.iloc[:, 1:2].values
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y = dataset.iloc[:, -1].values
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|
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# Construction du modèle
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from sklearn.linear_model import LinearRegression
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from sklearn.preprocessing import PolynomialFeatures
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poly_reg = PolynomialFeatures(degree = 4)
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X_poly = poly_reg.fit_transform(X)
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regressor = LinearRegression()
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regressor.fit(X_poly, y)
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|
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# Faire de nouvelles prédictions
|
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|
regressor.predict(15)
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|
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# Visualiser les résultats
|
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|
plt.scatter(X, y, color = 'red')
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plt.plot(X, regressor.predict(X_poly), color = 'blue')
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plt.title('Salaire vs Experience')
|
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|
plt.xlabel('Experience')
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|
plt.ylabel('Salaire')
|
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|
plt.show()
|
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|
|
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|
# Visualiser les résultats (courbe plus lisse)
|
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|
X_grid = np.arange(min(X), max(X), 0.1)
|
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|
X_grid = X_grid.reshape((len(X_grid), 1))
|
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|
plt.scatter(X, y, color = 'red')
|
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|
plt.plot(X_grid, regressor.predict(poly_reg.fit_transform(X_grid)), color = 'blue')
|
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|
plt.title('Salaire vs Experience')
|
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|
plt.xlabel('Experience')
|
||||||
|
plt.ylabel('Salaire')
|
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|
plt.show()
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@ -0,0 +1,401 @@
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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
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15592877,Male,32,18000,0
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15651983,Male,34,112000,1
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15746737,Male,18,52000,0
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15774179,Female,22,27000,0
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15667265,Female,28,87000,0
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15655123,Female,26,17000,0
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15595917,Male,30,80000,0
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15668385,Male,39,42000,0
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15709476,Male,20,49000,0
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15663939,Female,31,118000,1
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15694946,Male,24,55000,0
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15768816,Male,26,81000,0
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15682268,Male,35,50000,0
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15684801,Male,22,81000,0
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15636428,Female,30,116000,0
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15809823,Male,26,15000,0
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15699284,Female,29,28000,0
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15786993,Female,29,83000,0
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15709441,Female,35,44000,0
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15710257,Female,35,25000,0
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15582492,Male,28,123000,1
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15575694,Male,35,73000,0
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15756820,Female,28,37000,0
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15766289,Male,27,88000,0
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15584545,Female,32,86000,0
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15675949,Female,33,149000,1
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15672091,Female,19,21000,0
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15801658,Male,21,72000,0
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15706185,Female,26,35000,0
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15789863,Male,27,89000,0
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15720943,Male,26,86000,0
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15665416,Female,39,71000,0
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15660200,Female,37,71000,0
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15619653,Male,38,61000,0
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15773447,Male,37,55000,0
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15739160,Male,42,80000,0
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15591433,Male,36,52000,0
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15642725,Male,40,59000,0
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15701962,Male,41,59000,0
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15811613,Female,36,75000,0
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15741049,Male,37,72000,0
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15724423,Female,40,75000,0
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15574305,Male,35,53000,0
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15678168,Female,41,51000,0
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15697020,Female,39,61000,0
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15610801,Male,42,65000,0
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15745232,Male,26,32000,0
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15722758,Male,30,17000,0
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15675185,Male,31,58000,0
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15801247,Male,33,31000,0
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15725660,Male,30,87000,0
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15638963,Female,21,68000,0
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15800061,Female,28,55000,0
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15578006,Male,23,63000,0
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15668504,Female,20,82000,0
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15687491,Male,30,107000,1
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15610403,Female,28,59000,0
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15741094,Male,19,25000,0
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15807909,Male,19,85000,0
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15666141,Female,18,68000,0
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15617134,Male,35,59000,0
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15783029,Male,30,89000,0
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15622833,Female,34,25000,0
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15746422,Female,24,89000,0
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15750839,Female,27,96000,1
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15749130,Female,41,30000,0
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15779862,Male,29,61000,0
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15767871,Male,20,74000,0
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15576219,Male,41,45000,0
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15699247,Male,31,76000,0
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15619087,Female,36,50000,0
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15605327,Male,40,47000,0
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15610140,Female,31,15000,0
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15791174,Male,46,59000,0
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15602373,Male,29,75000,0
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15762605,Male,26,30000,0
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15598840,Female,32,135000,1
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15744279,Male,32,100000,1
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15670619,Male,25,90000,0
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15599533,Female,37,33000,0
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15757837,Male,35,38000,0
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15697574,Female,33,69000,0
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15578738,Female,18,86000,0
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15762228,Female,22,55000,0
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15614827,Female,35,71000,0
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15789815,Male,29,148000,1
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15579781,Female,29,47000,0
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15587013,Male,21,88000,0
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15570932,Male,34,115000,0
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15614420,Female,23,28000,0
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15594577,Male,25,22000,0
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15584114,Male,24,23000,0
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15685576,Male,26,16000,0
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15603319,Male,33,43000,0
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15759066,Female,33,60000,0
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15814816,Male,31,66000,0
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15724402,Female,20,82000,0
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15571059,Female,33,41000,0
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15674206,Male,35,72000,0
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15715160,Male,28,32000,0
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15730448,Male,24,84000,0
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15662067,Female,19,26000,0
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15779581,Male,29,43000,0
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15662901,Male,19,70000,0
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15689751,Male,28,89000,0
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15667742,Male,34,43000,0
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15738448,Female,30,79000,0
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15680243,Female,20,36000,0
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15745083,Male,26,80000,0
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15708228,Male,35,22000,0
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15628523,Male,35,39000,0
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15708196,Male,49,74000,0
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15735549,Female,39,134000,1
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15809347,Female,41,71000,0
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15660866,Female,58,101000,1
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15766609,Female,47,47000,0
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15654230,Female,55,130000,1
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15794566,Female,52,114000,0
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15800890,Female,40,142000,1
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15697424,Female,46,22000,0
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15724536,Female,48,96000,1
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15735878,Male,52,150000,1
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15657163,Male,35,58000.0,0
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15622478,Male,47.0,43000,0
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15582066,Male,40,78000,0
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15666675,Female,46,96000,0
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15732987,Male,59,143000,1
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15789432,Female,41,80000,0
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15663161,Male,35,91000,1
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15694879,Male,37,144000,1
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15593715,Male,60,102000,1
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15575002,Female,35,60000,0
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15622171,Male,37,53000,0
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15795224,Female,36,126000,1
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15685346,Male,56,133000,1
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15691808,Female,40,72000,0
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15721007,Female,42,80000,1
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15694453,Male,39,42000,0
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15813113,Male,40,107000,1
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15614187,Male,49,86000,1
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15619407,Female,38,112000,0
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15646227,Male,46,79000,1
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15660541,Male,40,57000,0
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15617877,Female,46,82000,0
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15701537,Male,42,149000,1
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15736228,Male,38,59000,0
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15780572,Female,50,88000,1
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15769596,Female,56,104000,1
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15586996,Female,41,72000,0
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15638003,Female,35,50000,0
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15775590,Female,57,122000,1
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15730688,Male,41,52000,0
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15810075,Female,44,39000,0
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15723373,Male,37,52000,0
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15795298,Female,48,134000,1
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15584320,Female,37,146000,1
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15724161,Female,50,44000,0
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15569641,Female,58,95000,1
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15815236,Female,45,131000,1
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15811177,Female,35,77000,0
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15680587,Male,36,144000,1
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15767681,Female,35,72000,0
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15801336,Female,42,108000,1
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15746203,Female,47,144000,1
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15583137,Male,40,61000,0
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15680752,Female,43,133000,0
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15688172,Female,59,76000,1
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15589449,Male,39,106000,1
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15727467,Male,57,74000,1
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15734312,Male,38,71000,0
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15764604,Male,49,88000,1
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15613014,Female,52,38000,1
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15685536,Male,35,61000,0
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15750447,Male,37,70000,1
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15638646,Male,48,141000,0
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15734161,Female,37,93000,1
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15713912,Female,37,78000,1
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15586757,Male,39,134000,1
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15625395,Male,55,39000,1
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15760570,Male,37,77000,0
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15725794,Female,36,63000,0
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15675791,Male,45,79000,0
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15736397,Female,58,38000,1
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15720745,Female,37,137000,1
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15637593,Male,37,79000,1
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15733973,Female,47,113000,1
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15733964,Female,38,50000,0
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15623502,Male,36,60000,0
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15611191,Female,53,82000,1
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15575247,Female,48,131000,1
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15713463,Male,41,72000,0
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15785170,Female,42,75000,0
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15639576,Female,47,107000,1
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15587177,Male,40,65000,0
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15814553,Male,57,60000,1
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15601550,Female,36,54000,0
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15664907,Male,58,144000,1
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15612465,Male,35,79000,0
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15810800,Female,38,55000,0
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15665760,Male,39,122000,1
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15717560,Female,38,65000,0
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15629739,Female,47,51000,1
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15716781,Female,41,63000,0
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15768151,Female,54,108000,1
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15579212,Male,39,77000,0
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15800515,Female,38,113000,1
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15587419,Female,42,90000,1
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15750335,Female,37,57000,0
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15699619,Male,36,99000,1
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15768293,Female,42,79000,0
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15671249,Male,46,88000,1
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15624755,Female,54,26000,1
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15611430,Female,60,46000,1
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15629885,Female,39,73000,0
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15800215,Female,42,53000,0
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15577806,Male,41,87000,1
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15749381,Female,58,23000,1
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15683758,Male,42,64000,0
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15670615,Male,48,33000,1
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15715622,Female,44,139000,1
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15707634,Male,49,28000,1
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15806901,Female,57,33000,1
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15775335,Male,56,60000,1
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15724150,Female,49,39000,1
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15627220,Male,39,71000,0
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15672330,Male,47,34000,1
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15807837,Male,48,33000,1
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15592570,Male,47,23000,1
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15748589,Female,45,45000,1
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15635893,Male,60,42000,1
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||||||
|
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
|
||||||
|
|
|
@ -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()
|
||||||
|
|
@ -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()
|
||||||
|
|
@ -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
|
||||||
|
|
|
@ -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')
|
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|
plt.title('Clusters de clients')
|
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|
plt.xlabel('Salaire annuel')
|
||||||
|
plt.ylabel('Spending Score')
|
||||||
|
plt.legend()
|
||||||
Loading…
Reference in New Issue