Create SVM_iris_dataset.py

Author: Adp-797

SVM_iris_dataset.py

@@ -0,0 +1,88 @@
+#Understanding the dataset
+# Importing the libraries 
+import numpy as np
+import matplotlib.pyplot as plt 
+import pandas as pd
+import matplotlib
+# Importing the dataset
+dataset = pd.read_csv('/content/iris data.csv') 
+print(dataset)
+print(dataset.shape)
+x=dataset.iloc[:,0:4]
+y=dataset['iris_class']
+print(x)
+print(y)
+
+#Train test split for dummy classifiers
+from sklearn.linear_model import LogisticRegression
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=10)
+
+#Using dummy classifiers
+from sklearn.dummy import DummyClassifier
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import classification_report
+clf_dummy = DummyClassifier(random_state=42) 
+
+clf_dummy.fit(x_train, y_train)
+y_pred = clf_dummy.predict(x_test) #uniform parameter
+print('Accuracy of dummy classifier on test set: {:.2f}'.format(clf_dummy.score(x_test, y_test)*100)+ '%')
+
+confusion_matrix=confusion_matrix(y_test, y_pred)
+print(confusion_matrix)
+
+report = classification_report(y_test, y_pred)
+print(report)
+
+#Train test split for SVM
+from sklearn.linear_model import LogisticRegression
+from sklearn import metrics
+from sklearn.model_selection import train_test_split
+
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=10)
+
+#Applying 4 kernal functions
+from sklearn.svm import SVC  
+from sklearn.metrics import classification_report, confusion_matrix  
+import matplotlib.pyplot as plt
+
+kernels = ['Polynomial', 'RBF', 'Sigmoid','Linear']#A function which returns the corresponding SVC model
+def getClassifier(ktype):
+    if ktype == 0:
+        # Polynomial kernal
+        return SVC(kernel='poly', degree=8, gamma="auto")
+    elif ktype == 1:
+        # Radial Basis Function kernal
+        return SVC(kernel='rbf', gamma="auto")
+    elif ktype == 2:
+        # Sigmoid kernal
+        return SVC(kernel='sigmoid', gamma="auto")
+    elif ktype == 3:
+        # Linear kernal
+        return SVC(kernel='linear', gamma="auto")
+
+#SVC model
+for i in range(4):
+    # Separate data into test and training sets
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state=10)# Train a SVC model using different kernal
+    svclassifier = getClassifier(i) 
+    svclassifier.fit(x_train, y_train)# Make prediction
+    y_pred = svclassifier.predict(x_test)# Evaluate our model
+    print('Accuracy of SVM classifier on test set: {:.2f}'.format(svclassifier.score(x_test, y_test)*100)+ '%')
+    print("Evaluation:", kernels[i], "kernel")
+    print(classification_report(y_test,y_pred))
+    
+#GridsearchCV for Hyperparameter tuning
+from sklearn.model_selection import GridSearchCV
+param_grid = {'C': [0.1,1, 10, 100], 'gamma': [1,0.1,0.01,0.001],'kernel': ['rbf', 'poly', 'sigmoid']}
+grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=2)
+grid.fit(x_train,y_train)
+
+print(grid.best_estimator_) #finding the optimal parameters
+print(grid.best_params_)
+
+grid_predictions = grid.predict(x_test)
+print(confusion_matrix(y_test,grid_predictions))
+print(classification_report(y_test,grid_predictions))