# -*- coding: utf-8 -*-
"""
Created on Fri Aug 16 12:35:13 2019

@author: 
"""

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

#importing the dataset
dataset = pd.read_csv('Social_Network_Ads.csv')
X= dataset.iloc[:, [2,3]].values
y= dataset.iloc[:, 4].values

#Splitting the dataset into training and testset
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 scalling 
from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
X_train=sc.fit_transform(X_train)
X_test= sc.transform(X_test)

#Create the model for the SVM
from sklearn.svm import SVC
classifier=SVC(kernel='linear')
classifier.fit(X_train, y_train)

#Predict the test set results
y_pred = classifier.predict(X_test)

#Making the confusion matrix
from sklearn.metrics import confusion_matrix
cm=confusion_matrix(y_test, y_pred)


#Visualing the Test set Result
from matplotlib.colors import ListedColormap
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('SVM (Test)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

4c163a0b5339891317afbfa8e1dbc5a5797f4a21
<mask token>

<mask token>
classifier.fit(X_train, y_train)
<mask token>
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('SVM (Test)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

<mask token>
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
<mask token>
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
    random_state=0)
<mask token>
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
<mask token>
classifier = SVC(kernel='linear')
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
<mask token>
cm = confusion_matrix(y_test, y_pred)
<mask token>
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('SVM (Test)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

<mask token>
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('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.svm import SVC
classifier = SVC(kernel='linear')
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
from matplotlib.colors import ListedColormap
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('SVM (Test)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()

# -*- coding: utf-8 -*-
"""
Created on Fri Aug 16 12:35:13 2019

@author: 
"""

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

#importing the dataset
dataset = pd.read_csv('Social_Network_Ads.csv')
X= dataset.iloc[:, [2,3]].values
y= dataset.iloc[:, 4].values

#Splitting the dataset into training and testset
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 scalling 
from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
X_train=sc.fit_transform(X_train)
X_test= sc.transform(X_test)

#Create the model for the SVM
from sklearn.svm import SVC
classifier=SVC(kernel='linear')
classifier.fit(X_train, y_train)

#Predict the test set results
y_pred = classifier.predict(X_test)

#Making the confusion matrix
from sklearn.metrics import confusion_matrix
cm=confusion_matrix(y_test, y_pred)


#Visualing the Test set Result
from matplotlib.colors import ListedColormap
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('SVM (Test)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
