# Support Vector Regression Algorithm | Machine Learning Algorithm Tutorial

In this ML Algorithms course tutorial, we are going to learn “Support Vector Regression in detail. we covered it by practically and theoretical intuition.

- What is Linear Support Vector Regression?
- What is Non-Linear Support Vector Regression?
- How to implement Support Vector Regression in python?

## Practical Source Code

```
# -*- coding: utf-8 -*-
"""Support Vector Regression Bangalore - House Price Prediction.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1Wm2WonoKBMBzDfdo56ALEN5ugRZcA_gn
## Business Problem - Predict the Price of Bangalore House
Using Support Vector Regression - Supervised Machine Learning Algorithm
### Load Libraries
"""
import pandas as pd
"""### Load Data"""
path = r"https://drive.google.com/uc?export=download&id=1xxDtrZKfuWQfl-6KA9XEd_eatitNPnkB"
df = pd.read_csv(path)
df.head()
"""## Split Data"""
X = df.drop('price', axis=1)
y = df['price']
print('Shape of X = ', X.shape)
print('Shape of y = ', y.shape)
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, random_state=51)
print('Shape of X_train = ', X_train.shape)
print('Shape of y_train = ', y_train.shape)
print('Shape of X_test = ', X_test.shape)
print('Shape of y_test = ', y_test.shape)
"""## Feature Scaling"""
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(X_train)
X_train = sc.transform(X_train)
X_test = sc.transform(X_test)
"""## Support Vector Regression - ML Model Training"""
from sklearn.svm import SVR
svr_rbf = SVR(kernel='rbf')
svr_rbf.fit(X_train, y_train)
svr_rbf.score(X_test, y_test)
svr_linear = SVR(kernel='linear')
svr_linear.fit(X_train, y_train)
svr_linear.score(X_test, y_test)
svr_poly = SVR(kernel='poly',degree=2,)
svr_poly.fit(X_train, y_train)
svr_poly.score(X_test, y_test)
"""## Predict the value of Home and Test"""
X_test[0]
svr_linear.predict([X_test[0]])
y_pred = svr_linear.predict(X_test)
y_pred
y_test
from sklearn.metrics import mean_squared_error
import numpy as np
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
print('MSE = ', mse)
print('RMSE = ', rmse)
#End
```

**To learn more about Support Vector Regression: **Click Here

Thanks for providing clean dataset for the model traing.