polynomial_regression.py

# -*- coding: utf-8 -*-
"""
Created on Fri Dec 21 18:59:49 2018

@author: Nhan Tran
"""

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('./Sample Data/PART 2. REGRESSION - Polynomial Regression - Polynomial_Regression/Polynomial_Regression/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)

"""
# Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
"""

# Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)

# Visualizing the Linear Regression results
def viz_linear():
    plt.scatter(X, y, color='red')
    plt.plot(X, lin_reg.predict(X), color='blue')
    plt.title('Truth or Bluff (Linear Regression)')
    plt.xlabel('Position level')
    plt.ylabel('Salary')
    plt.show()
    return
viz_linear()

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
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
viz_polymonial()

# Additional feature
# Making the plot line (Blue one) more smooth
def viz_polymonial_smooth():
    X_grid = np.arange(min(X), max(X), 0.1)
    X_grid = X_grid.reshape(len(X_grid), 1) #Why do we need to reshape? (https://www.tutorialspoint.com/numpy/numpy_reshape.htm)
    # Visualizing the Polymonial Regression results
    plt.scatter(X, y, color='red')
    plt.plot(X_grid, pol_reg.predict(poly_reg.fit_transform(X_grid)), color='blue')
    plt.title('Truth or Bluff (Linear Regression)')
    plt.xlabel('Position level')
    plt.ylabel('Salary')
    plt.show()
    return
viz_polymonial_smooth()

# Predicting a new result with Linear Regression
lin_reg.predict([[5.5]])
#output should be 249500

# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
#output should be 132148.43750003

Hello ,
I wanted to to calculate the r-squared in this code can you help me?

Hi @waelabdessmad you can go just like this sample
`from sklearn.metrics import r2_score

print(r2_score(y, pol_reg(x)))`

x is your test and y is your target
hope it helps.