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.

1. What is Linear Support Vector Regression?
2. What is Non-Linear Support Vector Regression?
3. 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

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