Support Vector Machine (SVM) Classification Algorithm | Machine Learning Algorithm
In this ML Algorithms course tutorial, we are going to learn “Support Vector Machine Classifier in detail. we covered it by practically and theoretical intuition.
- What is Linear Support Vector Classifier?
- What is Non-Linear Support Vector Classifier?
- How to implement Support Vector Classifier in python?

Support Vector Classification Practical Code
# -*- coding: utf-8 -*- """support vector classification.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1apM2WDL3ejysFr3C-Zr44eB8on8lFa-2 #Support Vector Classification ### Import Libraries """ # import libraries import numpy as np import pandas as pd """### Load Dataset""" #load dataset from sklearn.datasets import load_breast_cancer data = load_breast_cancer() data.data data.feature_names data.target data.target_names # create dtaframe df = pd.DataFrame(np.c_[data.data, data.target], columns=[list(data.feature_names)+['target']]) df.head() df.tail() df.shape """### Split Data""" X = df.iloc[:, 0:-1] y = df.iloc[:, -1] 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=2020) 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) """## Train Support Vector Classification Model""" from sklearn.svm import SVC classification_rbf = SVC(kernel='rbf') classification_rbf.fit(X_train, y_train) classification_rbf.score(X_test, y_test) """## Feature Scaling""" from sklearn.preprocessing import StandardScaler sc = StandardScaler() sc.fit(X_train) X_train_sc = sc.transform(X_train) X_test_sc = sc.transform(X_test) classification_rbf_2 = SVC(kernel='rbf') classification_rbf_2.fit(X_train_sc, y_train) classification_rbf_2.score(X_test_sc, y_test) """## SVC with kernel Polynomial""" classification_poly = SVC(kernel='poly', degree=2) classification_poly.fit(X_train_sc, y_train) classification_poly.score(X_test_sc, y_test) """## SVC with Kernel Linear""" classification_linear = SVC(kernel='linear') classification_linear.fit(X_train_sc, y_train) classification_linear.score(X_test_sc, y_test) """## Predict Cancer""" patient1 = [17.99, 10.38, 122.8, 1001.0, 0.1184, 0.2776, 0.3001, 0.1471, 0.2419, 0.07871, 1.095, 0.9053, 8.589, 153.4, 0.006399, 0.04904, 0.05373, 0.01587, 0.03003, 0.006193, 25.38, 17.33, 184.6, 2019.0, 0.1622, 0.6656, 0.7119, 0.2654, 0.4601, 0.1189] patient1_sc = sc.transform(np.array([patient1])) patient1_sc pred= classification_linear.predict(patient1_sc) pred data.target_names if pred[0] == 0: print('Patient has Cancer (malignant tumor)') else: print('Patient has no Cancer (malignant benign)') """Ab milenge next tutorial me,Tab tak ke liye SIKHATE SIKHATE kuch IMPLEMENT karte raho, Thank You.....-:)"""