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 svr_linear.predict([X_test]) 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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