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Update Day 1_Data PreProcessing.md #38

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5 changes: 5 additions & 0 deletions Code/Day 1_Data PreProcessing.md
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Expand Up @@ -20,6 +20,10 @@ Y = dataset.iloc[ : , 3].values
## Step 3: Handling the missing data
```python
from sklearn.preprocessing import Imputer
#Imputer is used to fill the missing values, based on three different strategies,
#1. Filling the mean value in the missing spaces.
#2. Filling the Median value in the missing spaces.
#3. Filling the most frequent values in the missing spaces
imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
imputer = imputer.fit(X[ : , 1:3])
X[ : , 1:3] = imputer.transform(X[ : , 1:3])
Expand All @@ -46,6 +50,7 @@ X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, ra
## Step 6: Feature Scaling
```python
from sklearn.preprocessing import StandardScaler
#StandardScaler is used to transform the data so that mean will be zero and standard deviation will be 1.
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)
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