decision tree classification much better

decision_tree_classification.py

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+# Decision Tree Classification
+import os
+os.chdir('C:\\Users\\saket\\Desktop\\cricket machine learning')
+# Importing the libraries
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('worldCup.csv')
+X = dataset.iloc[:,[1,2,3,4,5,6,7,8] ].values
+y = dataset.iloc[:, 9].values
+
+# Splitting the dataset into the Training set and Test set
+from sklearn.cross_validation 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)
+
+# Fitting Decision Tree Classification to the Training set
+from sklearn.tree import DecisionTreeClassifier
+classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
+classifier.fit(X_train, y_train)
+
+# Predicting the Test set results
+y_pred = classifier.predict(X_test)
+
+# Making the Confusion Matrix
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)