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import matplotlib.pyplot as plt

import pandas as pd

# Importing the dataset

dataset = pd.read_csv(

"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/position_salaries.csv"

)

X = dataset.iloc[:, 1:2].values

y = dataset.iloc[:, 2].values

# Splitting the dataset into the Training set and Test set

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# Fitting Polynomial Regression to the dataset

from sklearn.preprocessing import PolynomialFeatures

from sklearn.linear_model import LinearRegression

poly_reg = PolynomialFeatures(degree=4)

X_poly = poly_reg.fit_transform(X)

pol_reg = LinearRegression()

pol_reg.fit(X_poly, y)

# Visualizing the Polymonial Regression results

def viz_polymonial():

plt.scatter(X, y, color="red")

plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color="blue")

plt.title("Truth or Bluff (Linear Regression)")

plt.xlabel("Position level")

plt.ylabel("Salary")

plt.show()

return

if __name__ == "__main__":

viz_polymonial()

# Predicting a new result with Polymonial Regression

pol_reg.predict(poly_reg.fit_transform([[5.5]]))

# output should be 132148.43750003