diff --git a/Code/Day 1_Data PreProcessing.md b/Code/Day 1_Data PreProcessing.md
index 569d0e6a..0116d403 100644
--- a/Code/Day 1_Data PreProcessing.md	
+++ b/Code/Day 1_Data PreProcessing.md	
@@ -19,8 +19,8 @@ Y = dataset.iloc[ : , 3].values
 ```
 ## Step 3: Handling the missing data
 ```python
-from sklearn.preprocessing import Imputer
-imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
+from sklearn.impute import SimpleImputer
+imputer = SimpleImputer(missing_values = np.nan, strategy = "mean")
 imputer = imputer.fit(X[ : , 1:3])
 X[ : , 1:3] = imputer.transform(X[ : , 1:3])
 ```
@@ -32,14 +32,13 @@ X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])
 ```
 ### Creating a dummy variable
 ```python
-onehotencoder = OneHotEncoder(categorical_features = [0])
-X = onehotencoder.fit_transform(X).toarray()
-labelencoder_Y = LabelEncoder()
-Y =  labelencoder_Y.fit_transform(Y)
+from sklearn.compose import ColumnTransformer
+ct = ColumnTransformer([("Country", OneHotEncoder(), [0])], remainder = 'passthrough')
+X = ct.fit_transform(X)
 ```
 ## Step 5: Splitting the datasets into training sets and Test sets 
 ```python
-from sklearn.cross_validation import train_test_split
+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)
 ```