From 8c0c0b9d7815b99378e4de2eedf32ad349f7f55b Mon Sep 17 00:00:00 2001
From: amirmoghi3 <siramirmoghi3@gmail.com>
Date: Thu, 31 Dec 2020 20:19:52 +0330
Subject: [PATCH] Initial Commit

---
 .history/app_20201231195755.py |   0
 .history/app_20201231200318.py | 103 +++++++++++
 .history/app_20201231201128.py | 103 +++++++++++
 .history/app_20201231201209.py |  96 +++++++++++
 .history/app_20201231201258.py |  96 +++++++++++
 .history/app_20201231201711.py |  96 +++++++++++
 .history/app_20201231201714.py |  96 +++++++++++
 .vscode/settings.json          |   3 +
 Train.csv                      | 300 +++++++++++++++++++++++++++++++++
 Train.xlsx                     | Bin 0 -> 24032 bytes
 app.py                         |  96 +++++++++++
 11 files changed, 989 insertions(+)
 create mode 100644 .history/app_20201231195755.py
 create mode 100644 .history/app_20201231200318.py
 create mode 100644 .history/app_20201231201128.py
 create mode 100644 .history/app_20201231201209.py
 create mode 100644 .history/app_20201231201258.py
 create mode 100644 .history/app_20201231201711.py
 create mode 100644 .history/app_20201231201714.py
 create mode 100644 .vscode/settings.json
 create mode 100644 Train.csv
 create mode 100644 Train.xlsx
 create mode 100644 app.py

diff --git a/.history/app_20201231195755.py b/.history/app_20201231195755.py
new file mode 100644
index 0000000..e69de29
diff --git a/.history/app_20201231200318.py b/.history/app_20201231200318.py
new file mode 100644
index 0000000..0377641
--- /dev/null
+++ b/.history/app_20201231200318.py
@@ -0,0 +1,103 @@
+import numpy as np 
+import pandas as pd 
+from sklearn.metrics import confusion_matrix 
+from sklearn.cross_validation import train_test_split 
+from sklearn.tree import DecisionTreeClassifier 
+from sklearn.metrics import accuracy_score 
+from sklearn.metrics import classification_report 
+  
+# Function importing Dataset 
+def importdata(): 
+    balance_data = pd.read_excel('Train.xlsx', sheet_name="Sheet1").to_numpy()
+      
+    # Printing the dataswet shape 
+    print ("Dataset Length: ", len(balance_data)) 
+    print ("Dataset Shape: ", balance_data.shape) 
+      
+    # Printing the dataset obseravtions 
+    print ("Dataset: ",balance_data.head()) 
+    return balance_data 
+  
+# Function to split the dataset 
+def splitdataset(balance_data): 
+  
+    # Separating the target variable 
+    X = balance_data.values[:, 1:5] 
+    Y = balance_data.values[:, 0] 
+  
+    # Splitting the dataset into train and test 
+    X_train, X_test, y_train, y_test = train_test_split(  
+    X, Y, test_size = 0.3, random_state = 100) 
+      
+    return X, Y, X_train, X_test, y_train, y_test 
+      
+# Function to perform training with giniIndex. 
+def train_using_gini(X_train, X_test, y_train): 
+  
+    # Creating the classifier object 
+    clf_gini = DecisionTreeClassifier(criterion = "gini", 
+            random_state = 100,max_depth=3, min_samples_leaf=5) 
+  
+    # Performing training 
+    clf_gini.fit(X_train, y_train) 
+    return clf_gini 
+      
+# Function to perform training with entropy. 
+def tarin_using_entropy(X_train, X_test, y_train): 
+  
+    # Decision tree with entropy 
+    clf_entropy = DecisionTreeClassifier( 
+            criterion = "entropy", random_state = 100, 
+            max_depth = 3, min_samples_leaf = 5) 
+  
+    # Performing training 
+    clf_entropy.fit(X_train, y_train) 
+    return clf_entropy 
+  
+  
+# Function to make predictions 
+def prediction(X_test, clf_object): 
+  
+    # Predicton on test with giniIndex 
+    y_pred = clf_object.predict(X_test) 
+    print("Predicted values:") 
+    print(y_pred) 
+    return y_pred 
+      
+# Function to calculate accuracy 
+def cal_accuracy(y_test, y_pred): 
+      
+    print("Confusion Matrix: ", 
+        confusion_matrix(y_test, y_pred)) 
+      
+    print ("Accuracy : ", 
+    accuracy_score(y_test,y_pred)*100) 
+      
+    print("Report : ", 
+    classification_report(y_test, y_pred)) 
+  
+# Driver code 
+def main(): 
+      
+    # Building Phase 
+    data = importdata() 
+    X, Y, X_train, X_test, y_train, y_test = splitdataset(data) 
+    clf_gini = train_using_gini(X_train, X_test, y_train) 
+    clf_entropy = tarin_using_entropy(X_train, X_test, y_train) 
+      
+    # Operational Phase 
+    print("Results Using Gini Index:") 
+      
+    # Prediction using gini 
+    y_pred_gini = prediction(X_test, clf_gini) 
+    cal_accuracy(y_test, y_pred_gini) 
+      
+    print("Results Using Entropy:") 
+    # Prediction using entropy 
+    y_pred_entropy = prediction(X_test, clf_entropy) 
+    cal_accuracy(y_test, y_pred_entropy) 
+      
+      
+# Calling main function 
+if __name__=="__main__": 
+    main() 
\ No newline at end of file
diff --git a/.history/app_20201231201128.py b/.history/app_20201231201128.py
new file mode 100644
index 0000000..735243c
--- /dev/null
+++ b/.history/app_20201231201128.py
@@ -0,0 +1,103 @@
+import numpy as np 
+import pandas as pd 
+from sklearn.metrics import confusion_matrix 
+from sklearn.model_selection import train_test_split 
+from sklearn.tree import DecisionTreeClassifier 
+from sklearn.metrics import accuracy_score 
+from sklearn.metrics import classification_report 
+  
+# Function importing Dataset 
+def importdata(): 
+    balance_data = pd.read_excel('Train.xlsx', sheet_name="Sheet1").to_numpy()
+      
+    # Printing the dataswet shape 
+    print ("Dataset Length: ", len(balance_data)) 
+    print ("Dataset Shape: ", balance_data.shape) 
+      
+    # Printing the dataset obseravtions 
+    print ("Dataset: ",balance_data.head()) 
+    return balance_data 
+  
+# Function to split the dataset 
+def splitdataset(balance_data): 
+  
+    # Separating the target variable 
+    X = balance_data.values[:, 1:5] 
+    Y = balance_data.values[:, 0] 
+  
+    # Splitting the dataset into train and test 
+    X_train, X_test, y_train, y_test = train_test_split(  
+    X, Y, test_size = 0.3, random_state = 100) 
+      
+    return X, Y, X_train, X_test, y_train, y_test 
+      
+# Function to perform training with giniIndex. 
+def train_using_gini(X_train, X_test, y_train): 
+  
+    # Creating the classifier object 
+    clf_gini = DecisionTreeClassifier(criterion = "gini", 
+            random_state = 100,max_depth=3, min_samples_leaf=5) 
+  
+    # Performing training 
+    clf_gini.fit(X_train, y_train) 
+    return clf_gini 
+      
+# Function to perform training with entropy. 
+def tarin_using_entropy(X_train, X_test, y_train): 
+  
+    # Decision tree with entropy 
+    clf_entropy = DecisionTreeClassifier( 
+            criterion = "entropy", random_state = 100, 
+            max_depth = 3, min_samples_leaf = 5) 
+  
+    # Performing training 
+    clf_entropy.fit(X_train, y_train) 
+    return clf_entropy 
+  
+  
+# Function to make predictions 
+def prediction(X_test, clf_object): 
+  
+    # Predicton on test with giniIndex 
+    y_pred = clf_object.predict(X_test) 
+    print("Predicted values:") 
+    print(y_pred) 
+    return y_pred 
+      
+# Function to calculate accuracy 
+def cal_accuracy(y_test, y_pred): 
+      
+    print("Confusion Matrix: ", 
+        confusion_matrix(y_test, y_pred)) 
+      
+    print ("Accuracy : ", 
+    accuracy_score(y_test,y_pred)*100) 
+      
+    print("Report : ", 
+    classification_report(y_test, y_pred)) 
+  
+# Driver code 
+def main(): 
+      
+    # Building Phase 
+    data = importdata() 
+    X, Y, X_train, X_test, y_train, y_test = splitdataset(data) 
+    clf_gini = train_using_gini(X_train, X_test, y_train) 
+    clf_entropy = tarin_using_entropy(X_train, X_test, y_train) 
+      
+    # Operational Phase 
+    print("Results Using Gini Index:") 
+      
+    # Prediction using gini 
+    y_pred_gini = prediction(X_test, clf_gini) 
+    cal_accuracy(y_test, y_pred_gini) 
+      
+    print("Results Using Entropy:") 
+    # Prediction using entropy 
+    y_pred_entropy = prediction(X_test, clf_entropy) 
+    cal_accuracy(y_test, y_pred_entropy) 
+      
+      
+# Calling main function 
+if __name__=="__main__": 
+    main() 
\ No newline at end of file
diff --git a/.history/app_20201231201209.py b/.history/app_20201231201209.py
new file mode 100644
index 0000000..c5ce610
--- /dev/null
+++ b/.history/app_20201231201209.py
@@ -0,0 +1,96 @@
+import numpy as np 
+import pandas as pd 
+from sklearn.metrics import confusion_matrix 
+from sklearn.model_selection import train_test_split 
+from sklearn.tree import DecisionTreeClassifier 
+from sklearn.metrics import accuracy_score 
+from sklearn.metrics import classification_report 
+  
+# Function importing Dataset 
+def importdata(): 
+    balance_data = pd.read_excel('Train.xlsx', sheet_name="Sheet1").to_numpy()
+    return balance_data 
+  
+# Function to split the dataset 
+def splitdataset(balance_data): 
+  
+    # Separating the target variable 
+    X = balance_data.values[:, 1:5] 
+    Y = balance_data.values[:, 0] 
+  
+    # Splitting the dataset into train and test 
+    X_train, X_test, y_train, y_test = train_test_split(  
+    X, Y, test_size = 0.3, random_state = 100) 
+      
+    return X, Y, X_train, X_test, y_train, y_test 
+      
+# Function to perform training with giniIndex. 
+def train_using_gini(X_train, X_test, y_train): 
+  
+    # Creating the classifier object 
+    clf_gini = DecisionTreeClassifier(criterion = "gini", 
+            random_state = 100,max_depth=3, min_samples_leaf=5) 
+  
+    # Performing training 
+    clf_gini.fit(X_train, y_train) 
+    return clf_gini 
+      
+# Function to perform training with entropy. 
+def tarin_using_entropy(X_train, X_test, y_train): 
+  
+    # Decision tree with entropy 
+    clf_entropy = DecisionTreeClassifier( 
+            criterion = "entropy", random_state = 100, 
+            max_depth = 3, min_samples_leaf = 5) 
+  
+    # Performing training 
+    clf_entropy.fit(X_train, y_train) 
+    return clf_entropy 
+  
+  
+# Function to make predictions 
+def prediction(X_test, clf_object): 
+  
+    # Predicton on test with giniIndex 
+    y_pred = clf_object.predict(X_test) 
+    print("Predicted values:") 
+    print(y_pred) 
+    return y_pred 
+      
+# Function to calculate accuracy 
+def cal_accuracy(y_test, y_pred): 
+      
+    print("Confusion Matrix: ", 
+        confusion_matrix(y_test, y_pred)) 
+      
+    print ("Accuracy : ", 
+    accuracy_score(y_test,y_pred)*100) 
+      
+    print("Report : ", 
+    classification_report(y_test, y_pred)) 
+  
+# Driver code 
+def main(): 
+      
+    # Building Phase 
+    data = importdata() 
+    X, Y, X_train, X_test, y_train, y_test = splitdataset(data) 
+    clf_gini = train_using_gini(X_train, X_test, y_train) 
+    clf_entropy = tarin_using_entropy(X_train, X_test, y_train) 
+      
+    # Operational Phase 
+    print("Results Using Gini Index:") 
+      
+    # Prediction using gini 
+    y_pred_gini = prediction(X_test, clf_gini) 
+    cal_accuracy(y_test, y_pred_gini) 
+      
+    print("Results Using Entropy:") 
+    # Prediction using entropy 
+    y_pred_entropy = prediction(X_test, clf_entropy) 
+    cal_accuracy(y_test, y_pred_entropy) 
+      
+      
+# Calling main function 
+if __name__=="__main__": 
+    main() 
\ No newline at end of file
diff --git a/.history/app_20201231201258.py b/.history/app_20201231201258.py
new file mode 100644
index 0000000..011819d
--- /dev/null
+++ b/.history/app_20201231201258.py
@@ -0,0 +1,96 @@
+import numpy as np 
+import pandas as pd 
+from sklearn.metrics import confusion_matrix 
+from sklearn.model_selection import train_test_split 
+from sklearn.tree import DecisionTreeClassifier 
+from sklearn.metrics import accuracy_score 
+from sklearn.metrics import classification_report 
+  
+# Function importing Dataset 
+def importdata(): 
+    dataset = pd.read_excel('Train.xlsx', sheet_name="Sheet1").to_numpy()
+    return dataset 
+  
+# Function to split the dataset 
+def splitdataset(dataset): 
+  
+    # Separating the target variable 
+    X = dataset.values[:, 1:5] 
+    Y = dataset.values[:, 0] 
+  
+    # Splitting the dataset into train and test 
+    X_train, X_test, y_train, y_test = train_test_split(  
+    X, Y, test_size = 0.3, random_state = 100) 
+      
+    return X, Y, X_train, X_test, y_train, y_test 
+      
+# Function to perform training with giniIndex. 
+def train_using_gini(X_train, X_test, y_train): 
+  
+    # Creating the classifier object 
+    clf_gini = DecisionTreeClassifier(criterion = "gini", 
+            random_state = 100,max_depth=3, min_samples_leaf=5) 
+  
+    # Performing training 
+    clf_gini.fit(X_train, y_train) 
+    return clf_gini 
+      
+# Function to perform training with entropy. 
+def tarin_using_entropy(X_train, X_test, y_train): 
+  
+    # Decision tree with entropy 
+    clf_entropy = DecisionTreeClassifier( 
+            criterion = "entropy", random_state = 100, 
+            max_depth = 3, min_samples_leaf = 5) 
+  
+    # Performing training 
+    clf_entropy.fit(X_train, y_train) 
+    return clf_entropy 
+  
+  
+# Function to make predictions 
+def prediction(X_test, clf_object): 
+  
+    # Predicton on test with giniIndex 
+    y_pred = clf_object.predict(X_test) 
+    print("Predicted values:") 
+    print(y_pred) 
+    return y_pred 
+      
+# Function to calculate accuracy 
+def cal_accuracy(y_test, y_pred): 
+      
+    print("Confusion Matrix: ", 
+        confusion_matrix(y_test, y_pred)) 
+      
+    print ("Accuracy : ", 
+    accuracy_score(y_test,y_pred)*100) 
+      
+    print("Report : ", 
+    classification_report(y_test, y_pred)) 
+  
+# Driver code 
+def main(): 
+      
+    # Building Phase 
+    data = importdata() 
+    X, Y, X_train, X_test, y_train, y_test = splitdataset(data) 
+    clf_gini = train_using_gini(X_train, X_test, y_train) 
+    clf_entropy = tarin_using_entropy(X_train, X_test, y_train) 
+      
+    # Operational Phase 
+    print("Results Using Gini Index:") 
+      
+    # Prediction using gini 
+    y_pred_gini = prediction(X_test, clf_gini) 
+    cal_accuracy(y_test, y_pred_gini) 
+      
+    print("Results Using Entropy:") 
+    # Prediction using entropy 
+    y_pred_entropy = prediction(X_test, clf_entropy) 
+    cal_accuracy(y_test, y_pred_entropy) 
+      
+      
+# Calling main function 
+if __name__=="__main__": 
+    main() 
\ No newline at end of file
diff --git a/.history/app_20201231201711.py b/.history/app_20201231201711.py
new file mode 100644
index 0000000..93e927b
--- /dev/null
+++ b/.history/app_20201231201711.py
@@ -0,0 +1,96 @@
+import numpy as np 
+import pandas as pd 
+from sklearn.metrics import confusion_matrix 
+from sklearn.model_selection import train_test_split 
+from sklearn.tree import DecisionTreeClassifier 
+from sklearn.metrics import accuracy_score 
+from sklearn.metrics import classification_report 
+  
+# Function importing Dataset 
+def importdata(): 
+    balance_data = pd.read_csv( 'Train.csv',sep= ',', header = None) 
+    return dataset 
+  
+# Function to split the dataset 
+def splitdataset(dataset): 
+  
+    # Separating the target variable 
+    X = dataset.values[:, 1:5] 
+    Y = dataset.values[:, 0] 
+  
+    # Splitting the dataset into train and test 
+    X_train, X_test, y_train, y_test = train_test_split(  
+    X, Y, test_size = 0.3, random_state = 100) 
+      
+    return X, Y, X_train, X_test, y_train, y_test 
+      
+# Function to perform training with giniIndex. 
+def train_using_gini(X_train, X_test, y_train): 
+  
+    # Creating the classifier object 
+    clf_gini = DecisionTreeClassifier(criterion = "gini", 
+            random_state = 100,max_depth=3, min_samples_leaf=5) 
+  
+    # Performing training 
+    clf_gini.fit(X_train, y_train) 
+    return clf_gini 
+      
+# Function to perform training with entropy. 
+def tarin_using_entropy(X_train, X_test, y_train): 
+  
+    # Decision tree with entropy 
+    clf_entropy = DecisionTreeClassifier( 
+            criterion = "entropy", random_state = 100, 
+            max_depth = 3, min_samples_leaf = 5) 
+  
+    # Performing training 
+    clf_entropy.fit(X_train, y_train) 
+    return clf_entropy 
+  
+  
+# Function to make predictions 
+def prediction(X_test, clf_object): 
+  
+    # Predicton on test with giniIndex 
+    y_pred = clf_object.predict(X_test) 
+    print("Predicted values:") 
+    print(y_pred) 
+    return y_pred 
+      
+# Function to calculate accuracy 
+def cal_accuracy(y_test, y_pred): 
+      
+    print("Confusion Matrix: ", 
+        confusion_matrix(y_test, y_pred)) 
+      
+    print ("Accuracy : ", 
+    accuracy_score(y_test,y_pred)*100) 
+      
+    print("Report : ", 
+    classification_report(y_test, y_pred)) 
+  
+# Driver code 
+def main(): 
+      
+    # Building Phase 
+    data = importdata() 
+    X, Y, X_train, X_test, y_train, y_test = splitdataset(data) 
+    clf_gini = train_using_gini(X_train, X_test, y_train) 
+    clf_entropy = tarin_using_entropy(X_train, X_test, y_train) 
+      
+    # Operational Phase 
+    print("Results Using Gini Index:") 
+      
+    # Prediction using gini 
+    y_pred_gini = prediction(X_test, clf_gini) 
+    cal_accuracy(y_test, y_pred_gini) 
+      
+    print("Results Using Entropy:") 
+    # Prediction using entropy 
+    y_pred_entropy = prediction(X_test, clf_entropy) 
+    cal_accuracy(y_test, y_pred_entropy) 
+      
+      
+# Calling main function 
+if __name__=="__main__": 
+    main() 
\ No newline at end of file
diff --git a/.history/app_20201231201714.py b/.history/app_20201231201714.py
new file mode 100644
index 0000000..dadd4e6
--- /dev/null
+++ b/.history/app_20201231201714.py
@@ -0,0 +1,96 @@
+import numpy as np 
+import pandas as pd 
+from sklearn.metrics import confusion_matrix 
+from sklearn.model_selection import train_test_split 
+from sklearn.tree import DecisionTreeClassifier 
+from sklearn.metrics import accuracy_score 
+from sklearn.metrics import classification_report 
+  
+# Function importing Dataset 
+def importdata(): 
+    dataset = pd.read_csv( 'Train.csv',sep= ',', header = None) 
+    return dataset 
+  
+# Function to split the dataset 
+def splitdataset(dataset): 
+  
+    # Separating the target variable 
+    X = dataset.values[:, 1:5] 
+    Y = dataset.values[:, 0] 
+  
+    # Splitting the dataset into train and test 
+    X_train, X_test, y_train, y_test = train_test_split(  
+    X, Y, test_size = 0.3, random_state = 100) 
+      
+    return X, Y, X_train, X_test, y_train, y_test 
+      
+# Function to perform training with giniIndex. 
+def train_using_gini(X_train, X_test, y_train): 
+  
+    # Creating the classifier object 
+    clf_gini = DecisionTreeClassifier(criterion = "gini", 
+            random_state = 100,max_depth=3, min_samples_leaf=5) 
+  
+    # Performing training 
+    clf_gini.fit(X_train, y_train) 
+    return clf_gini 
+      
+# Function to perform training with entropy. 
+def tarin_using_entropy(X_train, X_test, y_train): 
+  
+    # Decision tree with entropy 
+    clf_entropy = DecisionTreeClassifier( 
+            criterion = "entropy", random_state = 100, 
+            max_depth = 3, min_samples_leaf = 5) 
+  
+    # Performing training 
+    clf_entropy.fit(X_train, y_train) 
+    return clf_entropy 
+  
+  
+# Function to make predictions 
+def prediction(X_test, clf_object): 
+  
+    # Predicton on test with giniIndex 
+    y_pred = clf_object.predict(X_test) 
+    print("Predicted values:") 
+    print(y_pred) 
+    return y_pred 
+      
+# Function to calculate accuracy 
+def cal_accuracy(y_test, y_pred): 
+      
+    print("Confusion Matrix: ", 
+        confusion_matrix(y_test, y_pred)) 
+      
+    print ("Accuracy : ", 
+    accuracy_score(y_test,y_pred)*100) 
+      
+    print("Report : ", 
+    classification_report(y_test, y_pred)) 
+  
+# Driver code 
+def main(): 
+      
+    # Building Phase 
+    data = importdata() 
+    X, Y, X_train, X_test, y_train, y_test = splitdataset(data) 
+    clf_gini = train_using_gini(X_train, X_test, y_train) 
+    clf_entropy = tarin_using_entropy(X_train, X_test, y_train) 
+      
+    # Operational Phase 
+    print("Results Using Gini Index:") 
+      
+    # Prediction using gini 
+    y_pred_gini = prediction(X_test, clf_gini) 
+    cal_accuracy(y_test, y_pred_gini) 
+      
+    print("Results Using Entropy:") 
+    # Prediction using entropy 
+    y_pred_entropy = prediction(X_test, clf_entropy) 
+    cal_accuracy(y_test, y_pred_entropy) 
+      
+      
+# Calling main function 
+if __name__=="__main__": 
+    main() 
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..c8a63ca
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python38-32\\python.exe"
+}
\ No newline at end of file
diff --git a/Train.csv b/Train.csv
new file mode 100644
index 0000000..d5952af
--- /dev/null
+++ b/Train.csv
@@ -0,0 +1,300 @@
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diff --git a/app.py b/app.py
new file mode 100644
index 0000000..dadd4e6
--- /dev/null
+++ b/app.py
@@ -0,0 +1,96 @@
+import numpy as np 
+import pandas as pd 
+from sklearn.metrics import confusion_matrix 
+from sklearn.model_selection import train_test_split 
+from sklearn.tree import DecisionTreeClassifier 
+from sklearn.metrics import accuracy_score 
+from sklearn.metrics import classification_report 
+  
+# Function importing Dataset 
+def importdata(): 
+    dataset = pd.read_csv( 'Train.csv',sep= ',', header = None) 
+    return dataset 
+  
+# Function to split the dataset 
+def splitdataset(dataset): 
+  
+    # Separating the target variable 
+    X = dataset.values[:, 1:5] 
+    Y = dataset.values[:, 0] 
+  
+    # Splitting the dataset into train and test 
+    X_train, X_test, y_train, y_test = train_test_split(  
+    X, Y, test_size = 0.3, random_state = 100) 
+      
+    return X, Y, X_train, X_test, y_train, y_test 
+      
+# Function to perform training with giniIndex. 
+def train_using_gini(X_train, X_test, y_train): 
+  
+    # Creating the classifier object 
+    clf_gini = DecisionTreeClassifier(criterion = "gini", 
+            random_state = 100,max_depth=3, min_samples_leaf=5) 
+  
+    # Performing training 
+    clf_gini.fit(X_train, y_train) 
+    return clf_gini 
+      
+# Function to perform training with entropy. 
+def tarin_using_entropy(X_train, X_test, y_train): 
+  
+    # Decision tree with entropy 
+    clf_entropy = DecisionTreeClassifier( 
+            criterion = "entropy", random_state = 100, 
+            max_depth = 3, min_samples_leaf = 5) 
+  
+    # Performing training 
+    clf_entropy.fit(X_train, y_train) 
+    return clf_entropy 
+  
+  
+# Function to make predictions 
+def prediction(X_test, clf_object): 
+  
+    # Predicton on test with giniIndex 
+    y_pred = clf_object.predict(X_test) 
+    print("Predicted values:") 
+    print(y_pred) 
+    return y_pred 
+      
+# Function to calculate accuracy 
+def cal_accuracy(y_test, y_pred): 
+      
+    print("Confusion Matrix: ", 
+        confusion_matrix(y_test, y_pred)) 
+      
+    print ("Accuracy : ", 
+    accuracy_score(y_test,y_pred)*100) 
+      
+    print("Report : ", 
+    classification_report(y_test, y_pred)) 
+  
+# Driver code 
+def main(): 
+      
+    # Building Phase 
+    data = importdata() 
+    X, Y, X_train, X_test, y_train, y_test = splitdataset(data) 
+    clf_gini = train_using_gini(X_train, X_test, y_train) 
+    clf_entropy = tarin_using_entropy(X_train, X_test, y_train) 
+      
+    # Operational Phase 
+    print("Results Using Gini Index:") 
+      
+    # Prediction using gini 
+    y_pred_gini = prediction(X_test, clf_gini) 
+    cal_accuracy(y_test, y_pred_gini) 
+      
+    print("Results Using Entropy:") 
+    # Prediction using entropy 
+    y_pred_entropy = prediction(X_test, clf_entropy) 
+    cal_accuracy(y_test, y_pred_entropy) 
+      
+      
+# Calling main function 
+if __name__=="__main__": 
+    main() 
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