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Author: aman64039

1log.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul  7 12:02:30 2019
+
+@author: aman
+"""
+
+"""
+Logistic regression produce a result in binary format which 
+is used to predict the outcome of caategorical dependdent 
+variable. So the output should be discreat or categorical
+classification family of algo are mostly of two type
+probablistic and determinstic
+logistic regression bellong to probabilstic family of
+classifiction
+"""
+
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+x = np.arange(-10,10,0.01)
+
+sig = 1/(1+np.power(np.e,-x))
+
+
+plt.plot(x,sig)
+plt.show()
+
+#y = mx+c
+lin= 3*x+7
+
+plt.plot(x,lin)
+plt.show()
+
+
+
+
+
+
+
+
+
+
+

2log.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul  7 12:37:14 2019
+
+@author: aman
+"""
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from sklearn.datasets import load_breast_cancer
+dataset=load_breast_cancer()
+
+X = dataset.data
+y = dataset.target
+
+
+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)
+
+
+from sklearn.linear_model import LogisticRegression
+log = LogisticRegression()
+log.fit(X_train,y_train)
+
+y_pred=log.predict(X_test)
+
+
+log.score(X_test,y_test)
+
+
+
+
+from sklearn.metrics import confusion_matrix
+
+confusion_matrix(y_pred,y_test)
+
+
+
+
+
+

3log.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul  7 13:10:03 2019
+
+@author: aman
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+data = pd.read_csv("/home/aman/Desktop/ML/suv.csv")
+data
+
+X = data.iloc[:,[2,3]].values
+y = data.iloc[:,4]
+
+
+from sklearn.preprocessing import StandardScaler
+#from sklearn.impute import SimpleImputer
+sc = StandardScaler()
+X = sc.fit_transform(X)
+
+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)
+
+
+from sklearn.linear_model import LogisticRegression
+log = LogisticRegression()
+log.fit(X_train,y_train)
+
+y_pred = log.predict(X_test)
+
+log.score(X_test,y_test)
+
+
+from sklearn.metrics import confusion_matrix
+confusion_matrix(y_pred,y_test)
+
+
+