diff --git a/guide/english/machine-learning/linear-regression/index.md b/guide/english/machine-learning/linear-regression/index.md
index c0daee1752..28209a1d55 100644
--- a/guide/english/machine-learning/linear-regression/index.md
+++ b/guide/english/machine-learning/linear-regression/index.md
@@ -51,21 +51,28 @@ Apply directly by using scikit library, thus making linear regression easy to us
 import pandas as pd
 from sklearn.cross_validation import train_test_split
 from sklearn.linear_model import LinearRegression as lr
+from sklearn import metrics
+
+# Load the data
 train = pd.read_csv('../input/train.csv')
 test = pd.read_csv('../input/test.csv') 
+
+# Separate the features and labels
 X = train.iloc[:, 0:4].values
 y = train.iloc[:, 4].values
+
+# Split the data into train and test
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
-X_train
+
+# Build the model
 model = lr()
 model.fit(X_train, y_train)
-print(model.score(X_train,y_train))
+print("Training score: ", model.score(X_train,y_train))
+print("Gradient: ", model.coef_)
+print("y-intercept: ", model.intercept_)
+
+# Evaluating the model on test data
 y_pred_class = model.predict(X_test)
-model.score(X_train,y_train)
-print(model.coef_)
-print(model.intercept_)
-# calculate accuracy
-from sklearn import metrics
 print(metrics.accuracy_score(y_test, y_pred_class))
 ```