diff --git a/Part 1 - Data Preprocessing/data_preprocessing.py b/Part 1 - Data Preprocessing/data_preprocessing.py
index 96e8bdd..251862d 100644
--- a/Part 1 - Data Preprocessing/data_preprocessing.py	
+++ b/Part 1 - Data Preprocessing/data_preprocessing.py	
@@ -1,5 +1,3 @@
-# Data Preprocessing Template
-
 # Importing the libraries
 import numpy as np
 import matplotlib.pyplot as plt
diff --git a/Part 2 - Regression/Section 5 - Multiple Linear Regression/Multiple_linear_regression.py b/Part 2 - Regression/Section 5 - Multiple Linear Regression/Multiple_linear_regression.py
index b467d1a..9a18c08 100644
--- a/Part 2 - Regression/Section 5 - Multiple Linear Regression/Multiple_linear_regression.py	
+++ b/Part 2 - Regression/Section 5 - Multiple Linear Regression/Multiple_linear_regression.py	
@@ -7,7 +7,6 @@
 X = dataset.iloc[:, :-1].values
 y = dataset.iloc[:, 4].values
 
-
 from sklearn.preprocessing import LabelEncoder, OneHotEncoder
 labelencoder_X = LabelEncoder()
 X[:, 3] = labelencoder_X.fit_transform(X[:, 3])
@@ -32,7 +31,10 @@
 X = np.append(arr=np.ones((50,1)).astype(int), values = X, axis=1)
 X_opt = X[:, [0,1,2,3,4,5]]
 regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
-regressor_OLS.summary()
+p_values = regressor_OLS.pvalues
+#p_values = p_values.numpy()
+max_p = np.amax(p_values)
+   
 
 X_opt = X[:, [0,1,3,4,5]]
 regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
diff --git a/Part 2 - Regression/Section 6 - Polynomial Regression/Position_Salaries.csv b/Part 2 - Regression/Section 6 - Polynomial Regression/Position_Salaries.csv
new file mode 100644
index 0000000..76d9d3e
--- /dev/null
+++ b/Part 2 - Regression/Section 6 - Polynomial Regression/Position_Salaries.csv	
@@ -0,0 +1,11 @@
+Position,Level,Salary
+Business Analyst,1,45000
+Junior Consultant,2,50000
+Senior Consultant,3,60000
+Manager,4,80000
+Country Manager,5,110000
+Region Manager,6,150000
+Partner,7,200000
+Senior Partner,8,300000
+C-level,9,500000
+CEO,10,1000000
\ No newline at end of file
diff --git a/Part 2 - Regression/Section 6 - Polynomial Regression/polynomial_linear_regression.py b/Part 2 - Regression/Section 6 - Polynomial Regression/polynomial_linear_regression.py
new file mode 100644
index 0000000..744adf7
--- /dev/null
+++ b/Part 2 - Regression/Section 6 - Polynomial Regression/polynomial_linear_regression.py	
@@ -0,0 +1,44 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Importing the dataset
+dataset = pd.read_csv('Position_Salaries.csv')
+X = dataset.iloc[:, 1:2].values
+y = dataset.iloc[:, 2].values
+
+#Fitting Linear Regression to the dataset
+from sklearn.linear_model import LinearRegression
+lin_reg = LinearRegression()
+lin_reg.fit(X,y)
+
+#Fitting Polynomial Regression to the dataset
+from sklearn.preprocessing import PolynomialFeatures
+poly_reg = PolynomialFeatures(degree=4)
+X_poly = poly_reg.fit_transform(X)      #transform to matrix
+lin_reg_2 = LinearRegression()
+lin_reg_2.fit(X_poly,y)
+
+#Visusalizong Linear Regression
+plt.scatter(X,y, color= 'red')
+plt.plot(X, lin_reg.predict(X), color='blue')
+plt.title('Truth or Bluff(Linear Refression)')
+plt.xlabel('Position Level')
+plt.ylabel('Salary')
+plt.show()
+
+#Visusalizong Polynomial Regression
+X_grid = np.arange(min(X), max(X), 0.1)
+X_grid = X_grid.reshape(len(X_grid),1)
+plt.scatter(X,y, color= 'red')
+plt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color='blue')
+plt.title('Truth or Bluff(Linear Refression)')
+plt.xlabel('Position Level')
+plt.ylabel('Salary')
+plt.show()
+
+#Predicting new result with Linear Regression
+lin_reg.predict(6.5)
+
+#Predicting new result with Linear Regression
+lin_reg_2.predict(poly_reg.fit_transform(6.5))
\ No newline at end of file