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Last_Question_graph(5)

  #!/usr/bin/env python # coding: utf-8 # In[13]: import numpy as np import matplotlib.pyplot as plt data = { 'KNN' : 0.95 , 'Naivebayes' : 0.9125 , 'DecisionTree' : 0.92 , 'Randomforest' : 0.9125 , 'SVM' : 0.9125 } courses = list (data.keys()) values = list (data.values()) fig = plt.figure( figsize = ( 10 , 5 )) plt.bar(courses, values, color = 'red' , width = 0.2 ) plt.xlabel( "Different classifier" ) plt.ylabel( "Accuracy_score" ) plt.title( "classifdier and their accuracy score" ) plt.show() # In[ ]:
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Random_ForestClassifier

  #!/usr/bin/env python # coding: utf-8 # In[113]: import numpy as np import matplotlib.pyplot as plt import pandas as pd # In[114]: #mydatafile="Desktop\Machine_Learning\week3_Social_Network_Ads.csv" mydatafile = r "C: \U sers \91 887 \O neDrive\Desktop \M achine_Learning\week3_Social_Network_Ads.csv" dataset = pd.read_csv(mydatafile) X = dataset.iloc[:, : - 1 ].values y = dataset.iloc[:, - 1 ].values # In[115]: from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20 , random_state = 0 ) # In[116]: from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) # In[117]: print (X_test) # In[118]: print (X_train) # In[119]: from sklearn.ensemble import RandomForestClassifier   classifier = RandomForestClassifier( n_estimators = 10 , criterion = "entropy" )   classifier.fit(X_train, y_tra...
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Gaussian_NB

  #!/usr/bin/env python # coding: utf-8 # In[56]: import numpy as np import matplotlib.pyplot as plt import pandas as pd # In[57]: #mydatafile="Desktop\Machine_Learning\week3_Social_Network_Ads.csv" mydatafile = r "C: \U sers \91 887 \O neDrive\Desktop \M achine_Learning\week3_Social_Network_Ads.csv" dataset = pd.read_csv(mydatafile) X = dataset.iloc[:, : - 1 ].values y = dataset.iloc[:, - 1 ].values # In[58]: from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20 , random_state = 0 ) # In[59]: from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) # In[60]: print (X_test) # In[61]: print (X_train) # In[62]: from sklearn.naive_bayes import GaussianNB classifier = GaussianNB() classifier.fit(X_train, y_train) # In[63]: print (classifier.predict(sc.transform([[ 30 , 87000 ]]))) # In[64]: y_p...
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Decision_Tree_Classifier

  #!/usr/bin/env python # coding: utf-8 # In[47]: import numpy as np import matplotlib.pyplot as plt import pandas as pd # In[48]: #mydatafile="Desktop\Machine_Learning\week3_Social_Network_Ads.csv" mydatafile = r "C: \U sers \91 887 \O neDrive\Desktop \M achine_Learning\week3_Social_Network_Ads.csv" dataset = pd.read_csv(mydatafile) X = dataset.iloc[:, : - 1 ].values y = dataset.iloc[:, - 1 ].values # In[49]: from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20 , random_state = 0 ) # In[50]: from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) # In[51]: print (X_test) # In[52]: print (X_train) # In[53]: from sklearn.tree import DecisionTreeClassifier classifier = DecisionTreeClassifier( criterion = 'entropy' , random_state = 0 ) classifier.fit(X_train, y_train) # In[54]: p...
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SVC_Support _vector_Machine

  #!/usr/bin/env python # coding: utf-8 # In[1]: import numpy as np import matplotlib.pyplot as plt import pandas as pd # In[2]: #mydatafile="Desktop\Machine_Learning\week3_Social_Network_Ads.csv" mydatafile = r "C: \U sers \91 887 \O neDrive\Desktop \M achine_Learning\week3_Social_Network_Ads.csv" dataset = pd.read_csv(mydatafile) X = dataset.iloc[:, : - 1 ].values y = dataset.iloc[:, - 1 ].values # In[3]: from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20 , random_state = 0 ) # In[4]: from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) # In[6]: print (X_test) # In[7]: print (X_train) # In[15]: from sklearn.svm import SVC classifier = SVC( kernel = 'linear' , random_state = 0 ) classifier.fit(X_train, y_train) # In[16]: #print(classifier.predict(sc.transform([[30,87000]...
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K-NeighbourClassifier

  #!/usr/bin/env python # coding: utf-8 # In[1]: import numpy as np import matplotlib.pyplot as plt import pandas as pd # In[2]: #mydatafile="Desktop\Machine_Learning\week3_Social_Network_Ads.csv" mydatafile = r "C: \U sers \91 887 \O neDrive\Desktop \M achine_Learning\week3_Social_Network_Ads.csv" dataset = pd.read_csv(mydatafile) X = dataset.iloc[:, : - 1 ].values y = dataset.iloc[:, - 1 ].values # In[3]: from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20 , random_state = 0 ) # In[4]: from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) # In[6]: print (X_test) # In[7]: print (X_train) # In[8]: from sklearn.neighbors import KNeighborsClassifier classifier = KNeighborsClassifier( n_neighbors = 5 , metric = 'minkowski' , p = 2 ) classifier.fit(X_train, y_train) # In[13]:...
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Logistic Regresion Using external Dataset

  #!/usr/bin/env python # coding: utf-8 # In[ ]: import numpy as np import matplotlib.pyplot as plt import pandas as pd # In[ ]: #mydatafile="Desktop\Machine_Learning\week3_Social_Network_Ads.csv" mydatafile = r "C: \U sers \91 887 \O neDrive\Desktop \M achine_Learning\week3_Social_Network_Ads.csv" dataset = pd.read_csv(mydatafile) X = dataset.iloc[:, : - 1 ].values y = dataset.iloc[:, - 1 ].values # In[ ]: 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 ) # In[ ]: print (X_train) # In[ ]: from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) # In[ ]: print (X_test) # In[ ]: print (X_train) # In[ ]: print (y_test) # In[ ]: print (y_train) # In[ ]: from sklearn.linear_model import LogisticRegression classifier = LogisticRegression( random_state = 0 ) classifier....
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