# -*- coding: utf-8 -*-
"""
Created on Thu Jul 11 13:50:07 2019

@author: BME207_1
"""

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset = pd.read_csv('D:/100-Days-Of-ML-Code/datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
Y = dataset.iloc[:,4].values

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)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, Y_train)

y_pred = classifier.predict(X_test)

from sklearn.metrics import confusion_matrix, classification_report
cm = confusion_matrix(Y_test, y_pred)
#print(cm)
#print(classification_report(Y_test, y_pred))

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=0.75, cmap=ListedColormap(('red', 'green')))
#print(classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape))
plt.xlim(X1.min(), X1.max())
plt.xlim(X2.min(), X2.max())
for i,j in enumerate(np.unique(Y_test)):
    plt.scatter(X_set[Y_set==j,0], X_set[Y_set==j,1], c=ListedColormap(('red', 'green'))(i), label=j)
plt. title(' LOGISTIC(Training set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()

X_set,y_set=X_test,Y_test
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=0.75, 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(' LOGISTIC(Test set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()

























# -*- coding: utf-8 -*-
"""
Created on Thu Jul 11 13:50:07 2019

@author: BME207_1
"""

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset = pd.read_csv('D:/100-Days-Of-ML-Code/datasets/Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
Y = dataset.iloc[:,4].values

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)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, Y_train)

y_pred = classifier.predict(X_test)

from sklearn.metrics import confusion_matrix, classification_report
cm = confusion_matrix(Y_test, y_pred)
#print(cm)
#print(classification_report(Y_test, y_pred))

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=0.75, cmap=ListedColormap(('red', 'green')))
#print(classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape))
plt.xlim(X1.min(), X1.max())
plt.xlim(X2.min(), X2.max())
for i,j in enumerate(np.unique(Y_test)):
    plt.scatter(X_set[Y_set==j,0], X_set[Y_set==j,1], c=ListedColormap(('red', 'green'))(i), label=j)
plt. title(' LOGISTIC(Training set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()

X_set,y_set=X_test,Y_test
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=0.75, 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(' LOGISTIC(Test set)')
plt. xlabel(' Age')
plt. ylabel(' Estimated Salary')
plt. legend()
plt. show()

























# -*- coding: utf-8 -*- Created on Thu Jul 11 13:50:07 2019  @author: BME207_1 #print(cm) #print(classification_report(Y_test, y_pred)) #print(classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape))