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:\Users\91887\OneDrive\Desktop\Machine_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]])))
y_pred = classifier.predict(X_test)
print(y_pred)


# In[17]:


from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test, y_pred)


# In[ ]:





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