import numpy as np import pandas as pd import matplotlib.pyplot as pl.pdf

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import numpy as np import pandas as pd import matplotlib.pyplot as plt from natplotlib import reParans fron matplotlib.cm import rainbor X matplotib.indine import warnings warnings. filterwarnings('ignore') \# Other Libraries fron skLearn. model_selection import train_test_split from sklearn.preprocessing iaport StandardScaler H Machine Learning from sklearn. neighbors inport KNeighborsClassifier from sklearn. svi import sVC fron sklearn. tree import DecisionTreeclassifier from sklearn,ensemble import RandomForestClassifier dataset = pd.read_csv ('dataset.csv') dataset. info() dataset.describe() reParams [ ' figure figsize' ]=26,14 plt,matshow(dataset, conc () ) plt.yticks(np.arange(dataset. shape [1]), dataset. colunns) plt. xticks(np,arange(dataset. shape[1]), dataset. coluans) plt.colorbar() dataset.hist() reParans ['figure. figsize '] =8,6 plt, bar(dataset['target'], unique(), dataset['target'],value_counts(), color kn__cussifier = kNaighborstlassitier(n_noighbors =k ) plt.plot([k for k in range (1,21)1, knhegeores, ootor = 'red') for 1 in ranee (1,21) t plt, text(1, knn,teoresti-1), (1,kh,teores (11))) plt.xtieks ((1 for 1 in renge (1,21)1) plt.xLabet ("Wuaber of Nesghbors (k) ') plt.ylabet("seores') plt.tithe(' K Weighbers ctassifier sceres for d\{ferent K vatues') swe-sceros =[1] berhets = ['tinear', 'boty', "rbf", 'signoid'] Gitor 1 in ronge(cen(kernets)): svejtastsitier = sve ( kasne = kernets [1]) sve_elassifier,tit (X_train, y_train) suc,sceresidppend (sue_classifier, seora (x _test, y _test)) ootors = rainbow(np. tinspace (,1, uentkernets) ) ) plt,bar(kernets, sve soores, cotbe = ooters) Tor 1 in range(Lentiernels )): plt, text(1, sve_scores[1], sve_scores[1]) plt,atable "Kernels') ptt. Yabet('scores'] plt-title('Support Vector Classifler sebres fon elffecent learnets').

import numpy as np import pandas as pd import matplotlib.pyplot as pl.pdf

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A
alankritaecobags
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import numpy as np import pandas as pd import matplotlib.pyplot as plt from natplotlib import reParans fron matplotlib.cm import rainbor X matplotib.indine import warnings warnings. filterwarnings('ignore') \# Other Libraries fron skLearn. model_selection import train_test_split from sklearn.preprocessing iaport StandardScaler H Machine Learning from sklearn. neighbors inport KNeighborsClassifier from sklearn. svi import sVC fron sklearn. tree import DecisionTreeclassifier from sklearn,ensemble import RandomForestClassifier dataset = pd.read_csv ('dataset.csv') dataset. info() dataset.describe() reParams [ ' figure figsize' ]=26,14 plt,matshow(dataset, conc () ) plt.yticks(np.arange(dataset. shape [1]), dataset. colunns) plt. xticks(np,arange(dataset. shape[1]), dataset. coluans) plt.colorbar() dataset.hist() reParans ['figure. figsize '] =8,6 plt, bar(dataset['target'], unique(), dataset['target'],value_counts(), color kn__cussifier = kNaighborstlassitier(n_noighbors =k ) plt.plot([k for k in range (1,21)1, knhegeores, ootor = 'red') for 1 in ranee (1,21) t plt, text(1, knn,teoresti-1), (1,kh,teores (11))) plt.xtieks ((1 for 1 in renge (1,21)1) plt.xLabet ("Wuaber of Nesghbors (k) ') plt.ylabet("seores') plt.tithe(' K Weighbers ctassifier sceres for d\{ferent K vatues') swe-sceros =[1] berhets = ['tinear', 'boty', "rbf", 'signoid'] Gitor 1 in ronge(cen(kernets)): svejtastsitier = sve ( kasne = kernets [1]) sve_elassifier,tit (X_train, y_train) suc,sceresidppend (sue_classifier, seora (x _test, y _test)) ootors = rainbow(np. tinspace (,1, uentkernets) ) ) plt,bar(kernets, sve soores, cotbe = ooters) Tor 1 in range(Lentiernels )): plt, text(1, sve_scores[1], sve_scores[1]) plt,atable "Kernels') ptt. Yabet('scores'] plt-title('Support Vector Classifler sebres fon elffecent learnets').

import numpy as np import pandas as pd import matplotlib.pyplot as pl.pdf

1 von 1
import numpy as np import pandas as pd import matplotlib.pyplot as plt from natplotlib import reParans fron matplotlib.cm import rainbor X matplotib.indine import warnings warnings. filterwarnings('ignore') # Other Libraries fron skLearn. model_selection import train_test_split from sklearn.preprocessing iaport StandardScaler H Machine Learning from sklearn. neighbors inport KNeighborsClassifier from sklearn. svi import sVC fron sklearn. tree import DecisionTreeclassifier from sklearn,ensemble import RandomForestClassifier dataset = pd.read_csv ('dataset.csv') dataset. info() dataset.describe() reParams [ ' figure figsize' ]=26,14 plt,matshow(dataset, conc () ) plt.yticks(np.arange(dataset. shape [1]), dataset. colunns) plt. xticks(np,arange(dataset. shape[1]), dataset. coluans) plt.colorbar() dataset.hist() reParans ['figure. figsize '] =8,6 plt, bar(dataset['target'], unique(), dataset['target'],value_counts(), color kn__cussifier = kNaighborstlassitier(n_noighbors =k ) plt.plot([k for k in range (1,21)1, knhegeores, ootor = 'red') for 1 in ranee (1,21) t plt, text(1, knn,teoresti-1), (1,kh,teores (11))) plt.xtieks ((1 for 1 in renge (1,21)1) plt.xLabet ("Wuaber of Nesghbors (k) ') plt.ylabet("seores') plt.tithe(' K Weighbers ctassifier sceres for d{ferent K vatues') swe-sceros =[1] berhets = ['tinear', 'boty', "rbf", 'signoid'] Gitor 1 in ronge(cen(kernets)): svejtastsitier = sve ( kasne = kernets [1]) sve_elassifier,tit (X_train, y_train) suc,sceresidppend (sue_classifier, seora (x _test, y _test)) ootors = rainbow(np. tinspace (,1, uentkernets) ) ) plt,bar(kernets, sve soores, cotbe = ooters) Tor 1 in range(Lentiernels )): plt, text(1, sve_scores[1], sve_scores[1]) plt,atable "Kernels') ptt. Yabet('scores'] plt-title('Support Vector Classifler sebres fon elffecent learnets')

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import numpy as np import pandas as pd import matplotlib.pyplot as pl.pdf

  • 1. import numpy as np import pandas as pd import matplotlib.pyplot as plt from natplotlib import reParans fron matplotlib.cm import rainbor X matplotib.indine import warnings warnings. filterwarnings('ignore') # Other Libraries fron skLearn. model_selection import train_test_split from sklearn.preprocessing iaport StandardScaler H Machine Learning from sklearn. neighbors inport KNeighborsClassifier from sklearn. svi import sVC fron sklearn. tree import DecisionTreeclassifier from sklearn,ensemble import RandomForestClassifier dataset = pd.read_csv ('dataset.csv') dataset. info() dataset.describe() reParams [ ' figure figsize' ]=26,14 plt,matshow(dataset, conc () ) plt.yticks(np.arange(dataset. shape [1]), dataset. colunns) plt. xticks(np,arange(dataset. shape[1]), dataset. coluans) plt.colorbar() dataset.hist() reParans ['figure. figsize '] =8,6 plt, bar(dataset['target'], unique(), dataset['target'],value_counts(), color kn__cussifier = kNaighborstlassitier(n_noighbors =k ) plt.plot([k for k in range (1,21)1, knhegeores, ootor = 'red') for 1 in ranee (1,21) t plt, text(1, knn,teoresti-1), (1,kh,teores (11))) plt.xtieks ((1 for 1 in renge (1,21)1) plt.xLabet ("Wuaber of Nesghbors (k) ') plt.ylabet("seores') plt.tithe(' K Weighbers ctassifier sceres for d{ferent K vatues') swe-sceros =[1] berhets = ['tinear', 'boty', "rbf", 'signoid'] Gitor 1 in ronge(cen(kernets)): svejtastsitier = sve ( kasne = kernets [1]) sve_elassifier,tit (X_train, y_train) suc,sceresidppend (sue_classifier, seora (x _test, y _test)) ootors = rainbow(np. tinspace (,1, uentkernets) ) ) plt,bar(kernets, sve soores, cotbe = ooters) Tor 1 in range(Lentiernels )): plt, text(1, sve_scores[1], sve_scores[1]) plt,atable "Kernels') ptt. Yabet('scores'] plt-title('Support Vector Classifler sebres fon elffecent learnets')