diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..1b0e92f
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+.vscode
+.history
+.__pycache__
\ No newline at end of file
diff --git a/.history/app_20201231195755.py b/.history/app_20201231195755.py
deleted file mode 100644
index e69de29..0000000
diff --git a/.history/app_20201231200318.py b/.history/app_20201231200318.py
deleted file mode 100644
index 0377641..0000000
--- a/.history/app_20201231200318.py
+++ /dev/null
@@ -1,103 +0,0 @@
-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
deleted file mode 100644
index 735243c..0000000
--- a/.history/app_20201231201128.py
+++ /dev/null
@@ -1,103 +0,0 @@
-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
deleted file mode 100644
index c5ce610..0000000
--- a/.history/app_20201231201209.py
+++ /dev/null
@@ -1,96 +0,0 @@
-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
deleted file mode 100644
index 011819d..0000000
--- a/.history/app_20201231201258.py
+++ /dev/null
@@ -1,96 +0,0 @@
-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
deleted file mode 100644
index 93e927b..0000000
--- a/.history/app_20201231201711.py
+++ /dev/null
@@ -1,96 +0,0 @@
-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
deleted file mode 100644
index dadd4e6..0000000
--- a/.history/app_20201231201714.py
+++ /dev/null
@@ -1,96 +0,0 @@
-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
deleted file mode 100644
index c8a63ca..0000000
--- a/.vscode/settings.json
+++ /dev/null
@@ -1,3 +0,0 @@
-{
-    "python.pythonPath": "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python38-32\\python.exe"
-}
\ No newline at end of file