Create svm.py

Author: armankazmi

SVM/svm.py

@@ -0,0 +1,48 @@
+#Importing Libraries
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from matplotlib import pyplot as plt
+from sklearn import svm
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import KFold
+from sklearn.metrics import classification_report, confusion_matrix 
+
+#data
+data = pd.read_csv("17054.csv")
+
+#X and y for classification
+X = data.drop('label',axis =1)
+y = data.label
+
+#Scaling the features
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+X = scaler.fit_transform(X)
+
+#Splitting the dataset
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=51)
+
+
+#model without Grid search
+svm_model = svm.SVC(kernel='rbf',gamma='scale', C=1) #C value varied
+svm_model.fit(X_train,y_train)
+predictions = svm_model.predict(X_test)
+#score = accuracy_score(predictions, y_test)
+print("The report without using Grid search CV : ")
+print(classification_report(y_test, predictions))
+
+
+#Grid Search for best parameters
+from sklearn.model_selection import GridSearchCV 
+param_grid = {'C': [0.01,0.1, 1, 10, 100],  
+              'gamma': [1, 0.1, 0.01, 0.001, 'auto','scale'], 
+              'kernel': ['rbf']}  
+  
+grid = GridSearchCV(svm.SVC(), param_grid, refit = True, verbose = 3) 
+  
+# fitting the model for grid search 
+grid.fit(X_train, y_train)
+grid_predictions = grid.predict(X_test)
+print("After using Grid Search CV: ")
+print(classification_report(y_test, grid_predictions))