diff --git a/Code/Day 1_Data_Preprocessing.py b/Code/Day 1_Data_Preprocessing.py
index 13f7c3e..5c7684a 100644
--- a/Code/Day 1_Data_Preprocessing.py	
+++ b/Code/Day 1_Data_Preprocessing.py	
@@ -1,76 +1,76 @@
-#Day 1: Data Prepocessing
-
-#Step 1: Importing the libraries
-import numpy as np
-import pandas as pd
-
-#Step 2: Importing dataset
-dataset = pd.read_csv('../datasets/Data.csv')
-X = dataset.iloc[ : , :-1].values
-Y = dataset.iloc[ : , 3].values
-print("Step 2: Importing dataset")
-print("X")
-print(X)
-print("Y")
-print(Y)
-
-#Step 3: Handling the missing data
-# If you use the newest version of sklearn, use the lines of code commented out
-from sklearn.impute import SimpleImputer
-imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
-#from sklearn.preprocessing import Imputer
-# axis=0表示按列进行
-#imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
-imputer = imputer.fit(X[ : , 1:3])
-X[ : , 1:3] = imputer.transform(X[ : , 1:3])
-print("---------------------")
-print("Step 3: Handling the missing data")
-print("step2")
-print("X")
-print(X)
-
-#Step 4: Encoding categorical data
-from sklearn.preprocessing import LabelEncoder, OneHotEncoder
-from sklearn.compose import ColumnTransformer 
-#labelencoder_X = LabelEncoder()
-#X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])
-#Creating a dummy variable
-#print(X)
-ct = ColumnTransformer([("", OneHotEncoder(), [0])], remainder = 'passthrough')
-X = ct.fit_transform(X)
-#onehotencoder = OneHotEncoder(categorical_features = [0])
-#X = onehotencoder.fit_transform(X).toarray()
-labelencoder_Y = LabelEncoder()
-Y =  labelencoder_Y.fit_transform(Y)
-print("---------------------")
-print("Step 4: Encoding categorical data")
-print("X")
-print(X)
-print("Y")
-print(Y)
-
-#Step 5: Splitting the datasets into training sets and Test sets
-from sklearn.model_selection import train_test_split
-X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)
-print("---------------------")
-print("Step 5: Splitting the datasets into training sets and Test sets")
-print("X_train")
-print(X_train)
-print("X_test")
-print(X_test)
-print("Y_train")
-print(Y_train)
-print("Y_test")
-print(Y_test)
-
-#Step 6: Feature Scaling
-from sklearn.preprocessing import StandardScaler
-sc_X = StandardScaler()
-X_train = sc_X.fit_transform(X_train)
-X_test = sc_X.transform(X_test)
-print("---------------------")
-print("Step 6: Feature Scaling")
-print("X_train")
-print(X_train)
-print("X_test")
-print(X_test)
+#Day 1: Data Prepocessing
+
+#Step 1: Importing the libraries
+import numpy as np
+import pandas as pd
+
+#Step 2: Importing dataset
+dataset = pd.read_csv('../datasets/Data.csv')
+X = dataset.iloc[ : , :-1].values
+Y = dataset.iloc[ : , 3].values
+print("Step 2: Importing dataset")
+print("X")
+print(X)
+print("Y")
+print(Y)
+
+#Step 3: Handling the missing data
+# If you use the newest version of sklearn, use the lines of code commented out
+from sklearn.impute import SimpleImputer
+imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
+#from sklearn.preprocessing import Imputer
+# axis=0表示按列进行
+#imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
+imputer = imputer.fit(X[ : , 1:3])
+X[ : , 1:3] = imputer.transform(X[ : , 1:3])
+print("---------------------")
+print("Step 3: Handling the missing data")
+print("step2")
+print("X")
+print(X)
+
+#Step 4: Encoding categorical data
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+from sklearn.compose import ColumnTransformer 
+#labelencoder_X = LabelEncoder()
+#X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])
+#Creating a dummy variable
+#print(X)
+ct = ColumnTransformer([("", OneHotEncoder(), [0])], remainder = 'passthrough')
+X = ct.fit_transform(X)
+#onehotencoder = OneHotEncoder(categorical_features = [0])
+#X = onehotencoder.fit_transform(X).toarray()
+labelencoder_Y = LabelEncoder()
+Y =  labelencoder_Y.fit_transform(Y)
+print("---------------------")
+print("Step 4: Encoding categorical data")
+print("X")
+print(X)
+print("Y")
+print(Y)
+
+#Step 5: Splitting the datasets into training sets and Test sets
+from sklearn.model_selection import train_test_split
+X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)
+print("---------------------")
+print("Step 5: Splitting the datasets into training sets and Test sets")
+print("X_train")
+print(X_train)
+print("X_test")
+print(X_test)
+print("Y_train")
+print(Y_train)
+print("Y_test")
+print(Y_test)
+
+#Step 6: Feature Scaling
+from sklearn.preprocessing import StandardScaler
+sc_X = StandardScaler()
+X_train = sc_X.fit_transform(X_train)
+X_test = sc_X.transform(X_test)
+print("---------------------")
+print("Step 6: Feature Scaling")
+print("X_train")
+print(X_train)
+print("X_test")
+print(X_test)