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+""" Implementation of Support Vector Machines"""
+"""For this I have used the famous Iris flower data set.
+The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by Sir Ronald Fisher in the 1936 as an example of discriminant analysis."""
+# The Iris Setosa
+from IPython.display import Image
+
+url = 'http://upload.wikimedia.org/wikipedia/commons/5/56/Kosaciec_szczecinkowaty_Iris_setosa.jpg'
+Image(url,width=300, height=300)
+
+# The Iris Versicolor
+from IPython.display import Image
+url = 'http://upload.wikimedia.org/wikipedia/commons/4/41/Iris_versicolor_3.jpg'
+Image(url,width=300, height=300)
+
+# The Iris Virginica
+from IPython.display import Image
+url = 'http://upload.wikimedia.org/wikipedia/commons/9/9f/Iris_virginica.jpg'
+Image(url,width=300, height=300)
+
+"""
+The iris dataset contains measurements for 150 iris flowers from three different species.
+The three classes in the Iris dataset:
+Iris-setosa (n=50)
+Iris-versicolor (n=50)
+Iris-virginica (n=50)
+The four features of the Iris dataset:
+sepal length in cm
+sepal width in cm
+petal length in cm
+petal width in cm
+"""
+import seaborn as sns
+iris = sns.load_dataset('iris')
+import pandas as pd
+import matplotlib.pyplot as plt
+%matplotlib inline
+# Setosa is the most separable. 
+sns.pairplot(iris,hue='species',palette='Dark2')
+#Create a kde plot of sepal_length versus sepal width for setosa species of flower.
+setosa = iris[iris['species']=='setosa']
+sns.kdeplot( setosa['sepal_width'], setosa['sepal_length'],cmap="plasma", shade=True, shade_lowest=False)
+#Train Test Split
+from sklearn.model_selection import train_test_split
+X = iris.drop('species',axis=1)
+y = iris['species']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)
+# training a Support Vector Machine Classifier.
+from sklearn.svm import SVC
+svc_model = SVC()
+svc_model.fit(X_train,y_train)
+predictions = svc_model.predict(X_test)
+from sklearn.metrics import classification_report,confusion_matrix
+print(confusion_matrix(y_test,predictions))
+print(classification_report(y_test,predictions))
+
+#Gridsearch Practice
+from sklearn.model_selection import GridSearchCV
+param_grid = {'C': [0.1,1, 10, 100], 'gamma': [1,0.1,0.01,0.001]} 
+grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=2)
+grid.fit(X_train,y_train)
+grid_predictions = grid.predict(X_test)
+print(confusion_matrix(y_test,grid_predictions))
+print(classification_report(y_test,grid_predictions))