Support Vector Machine in Python

At first download the Heart Disease Dataset from Kaggle:  https://www.kaggle.com/ronitf/heart-disease-uci.  Following, you should put the dataset in google drive. Then Using Google Colab you can run the following Support Vector Machine code:

!pip install -U -q PyDrive 

from pydrive.auth import GoogleAuth 

from pydrive.drive import GoogleDrive 

from google.colab import auth 

from oauth2client.client import GoogleCredentials 

# Authenticate and create the PyDrive client. 

auth.authenticate_user() 

gauth = GoogleAuth() 

gauth.credentials = GoogleCredentials.get_application_default() 

drive = GoogleDrive(gauth)

link =

'https://drive.google.com/file/d/1OglM8GvGIiVMMUXAchljXc86RtOq9K02

/view?usp=sharing'

import pandas as pd 

import numpy as np

# to get the id part of the file 

id = link.split("/")[-2] 

downloaded = drive.CreateFile({'id':id}) 

downloaded.GetContentFile('heart.csv') 

df = pd.read_csv('heart.csv') 

X = df.drop('target', axis=1)

y = df['target']

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y,

 test_size = 0.30)

from sklearn.svm import SVC

svclassifier = SVC(kernel='linear')

svclassifier.fit(X_train, y_train)

y_pred = svclassifier.predict(X_test)

from sklearn.metrics import confusion_matrix, classification_report

print(confusion_matrix(y_test,y_pred))

print(classification_report(y_test,y_pred))

from sklearn import metrics

print("Accuracy:",

metrics.accuracy_score(y_test, y_pred))

print("Precision:",

metrics.precision_score(y_test, y_pred))

print("Recall:",

metrics.recall_score(y_test, y_pred))

print("F1 Score:",metrics.f1_score(y_test, y_pred))

print('Mean Absolute Error:',

 metrics.mean_absolute_error(y_test, y_pred))

print('Mean Squared Error:', 

metrics.mean_squared_error(y_test, y_pred))

print('Root Mean Squared Error:', 

np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

Output: