Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Update Day 1_Data PreProcessing.md #38

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Update Day 1_Data PreProcessing.md #38

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 5 additions & 0 deletions Code/Day 1_Data PreProcessing.md
View file
Open in desktop
Original file line number Diff line number Diff line change
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)
Expand Down