SVM(Support Vector Machines) is a supervised machine learning algorithm. More information about it can be found here.
You can learn more about SVM in the below video.
The below code will help you to create an SVM model for regression use cases.
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#### Create Gym Data for regression in Python #### import pandas as pd import numpy as np ColumnNames=['Hours','Calories', 'Weight'] DataValues=[[ 1.0, 2500, 95], [ 2.0, 2000, 85], [ 2.5, 1900, 83], [ 3.0, 1850, 81], [ 3.5, 1600, 80], [ 4.0, 1500, 78], [ 5.0, 1500, 77], [ 5.5, 1600, 80], [ 6.0, 1700, 75], [ 6.5, 1500, 70]] #Create the Data Frame GymData=pd.DataFrame(data=DataValues,columns=ColumnNames) GymData.head() #Separate Target Variable and Predictor Variables TargetVariable='Weight' Predictors=['Hours','Calories'] X=GymData[Predictors].values y=GymData[TargetVariable].values #Split the data into training and testing set 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=42) ################################################################## ###### Support Vector Machines(SVM) Regression in Python ####### import pandas as pd from sklearn import svm RegModel = svm.SVR(C=2, kernel='linear') #Printing all the parameters of KNN print(RegModel) #Creating the model on Training Data SVM=RegModel.fit(X_train,y_train) prediction=SVM.predict(X_test) #Measuring Goodness of fit in Training data from sklearn import metrics print('R2 Value:',metrics.r2_score(y_train, SVM.predict(X_train))) #Measuring accuracy on Testing Data print('Accuracy',100- (np.mean(np.abs((y_test - prediction) / y_test)) * 100)) #Plotting the feature importance for Top 10 most important columns #The built in attribute SVM.coef_ works only for linear kernel %matplotlib inline feature_importances = pd.Series(SVM.coef_[0], index=Predictors) feature_importances.nlargest(10).plot(kind='barh') #Printing some sample values of prediction TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors) TestingDataResults[TargetVariable]=y_test TestingDataResults[('Predicted'+TargetVariable)]=prediction TestingDataResults.head() |
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