added till may 9th

all deep learning algorithms until 9th may

Author: RajKrishna2123

1_learning/10_model_selection/Social_Network_Ads.csv

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+# Grid Search
+
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Social_Network_Ads.csv')
+X = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, -1].values
+
+# Splitting the dataset into the Training set and Test set
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
+
+# Feature Scaling
+from sklearn.preprocessing import StandardScaler
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+
+# Training the Kernel SVM model on the Training set
+from sklearn.svm import SVC
+classifier = SVC(kernel = 'rbf', random_state = 0)
+classifier.fit(X_train, y_train)
+
+# Making the Confusion Matrix
+from sklearn.metrics import confusion_matrix, accuracy_score
+y_pred = classifier.predict(X_test)
+cm = confusion_matrix(y_test, y_pred)
+print(cm)
+accuracy_score(y_test, y_pred)
+
+# Applying k-Fold Cross Validation
+from sklearn.model_selection import cross_val_score
+accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)
+print("Accuracy: {:.2f} %".format(accuracies.mean()*100))
+print("Standard Deviation: {:.2f} %".format(accuracies.std()*100))
+
+# Applying Grid Search to find the best model and the best parameters
+from sklearn.model_selection import GridSearchCV
+parameters = [{'C': [0.25, 0.5, 0.75, 1], 'kernel': ['linear']},
+              {'C': [0.25, 0.5, 0.75, 1], 'kernel': ['rbf'], 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}]
+grid_search = GridSearchCV(estimator = classifier,
+                           param_grid = parameters,
+                           scoring = 'accuracy',
+                           cv = 10,
+                           n_jobs = -1)
+grid_search.fit(X_train, y_train)
+best_accuracy = grid_search.best_score_
+best_parameters = grid_search.best_params_
+print("Best Accuracy: {:.2f} %".format(best_accuracy*100))
+print("Best Parameters:", best_parameters)
+
+# Visualising the Training set results
+from matplotlib.colors import ListedColormap
+X_set, y_set = X_train, y_train
+X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
+                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
+plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
+             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(), X1.max())
+plt.ylim(X2.min(), X2.max())
+for i, j in enumerate(np.unique(y_set)):
+    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
+                c = ListedColormap(('red', 'green'))(i), label = j)
+plt.title('Kernel SVM (Training set)')
+plt.xlabel('Age')
+plt.ylabel('Estimated Salary')
+plt.legend()
+plt.show()
+
+# Visualising the Test set results
+from matplotlib.colors import ListedColormap
+X_set, y_set = X_test, y_test
+X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
+                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
+plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
+             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
+plt.xlim(X1.min(), X1.max())
+plt.ylim(X2.min(), X2.max())
+for i, j in enumerate(np.unique(y_set)):
+    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
+                c = ListedColormap(('red', 'green'))(i), label = j)
+plt.title('Kernel SVM (Test set)')
+plt.xlabel('Age')
+plt.ylabel('Estimated Salary')
+plt.legend()
+plt.show()