diff --git a/LinearRegression.py b/LinearRegression.py
index cbe418e..b9361e2 100644
--- a/LinearRegression.py
+++ b/LinearRegression.py
@@ -1,32 +1,30 @@
 # Import the libraries
 from random import randint
 from sklearn.linear_model import LinearRegression
+import numpy as np
 
 # Create a range limit for random numbers in the training set, and a count of the number of rows in the training set
 TRAIN_SET_LIMIT = 1000
 TRAIN_SET_COUNT = 100
 
-
-# Create an empty list of the input training set 'X' and create an empty list of the output for each training set 'Y'
-TRAIN_INPUT = list()
-TRAIN_OUTPUT= list()
+# Create an empty numpy array of shape (TRAIN_SET_COUNT, 3) for the input training set 'X' and create an empty numpy array for the output for each training set 'y'
+TRAIN_INPUT = np.empty((TRAIN_SET_COUNT, 3))
+TRAIN_OUTPUT = np.empty(TRAIN_SET_COUNT)
 
 #Create and append a randomly generated data set to the input and output
 for i in range(TRAIN_SET_COUNT):
-    a = randint(0, TRAIN_SET_LIMIT)
-    b = randint(0, TRAIN_SET_LIMIT)
-    c = randint(0, TRAIN_SET_LIMIT)
-    #Create a linear function for the output dataset 'Y'
+    a, b, c = randint(0, TRAIN_SET_LIMIT), randint(0, TRAIN_SET_LIMIT), randint(0, TRAIN_SET_LIMIT)
+    #Create a linear function for the output dataset 'y'
     op = (10*a) + (2*b) + (3*c)
-    TRAIN_INPUT.append([a,b,c])
-    TRAIN_OUTPUT.append(op)
+    TRAIN_INPUT[i] = [a, b, c]
+    TRAIN_OUTPUT[i] = op
 
-predictor = LinearRegression(n_jobs=-1) #Create a linear regression object NOTE n_jobs = the number of jobs to use for computation, -1 means use all processors
+predictor = LinearRegression(n_jobs=-1) #Create a linear regression object with all processors
 predictor.fit(X=TRAIN_INPUT, y=TRAIN_OUTPUT)  #fit the linear model (approximate a target function)
 
-X_TEST = [[10,20,30]] #Create our testing data set, the ouput should be 10*10 + 2*20 + 3*30 = 230
-outcome = predictor.predict(X=X_TEST) # Predict the ouput of the test data using the linear model
+X_TEST = np.array([[10, 20, 30]]) #Create our testing data set, the ouput should be 10*10 + 2*20 + 3*30 = 230
+outcome = predictor.predict(X=X_TEST) # Predict the output of the test data using the linear model
 
 coefficients = predictor.coef_  #The estimated coefficients for the linear regression problem.
 
-print('Outcome: {} \n Coefficients: {}'.format(outcome, coefficients))
+print(f"Outcome: {outcome} \nCoefficients: {coefficients}")