polynomial linear regression
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The document shows how to perform polynomial regression in Python and R. It imports data on position level and salary. Linear and polynomial regression models are fit to predict salary from position level. Visualizations are created to compare the linear and polynomial regression fits and predictions. Polynomial regression fits the data better by capturing the nonlinear relationship between position level and salary.
![IMPORTING THE DATASET
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values
NOTE : X = dataset.iloc[:, 1:2].values can be rewritten as X =
dataset.iloc[: , 1].values
(see whats the problem)](https://image.slidesharecdn.com/3-polynomiallinearregression-171230051801/85/polynomial-linear-regression-5-320.jpg)
![READ DATASET
library(readr)
dataset <- read_csv("D:/machine learning AZ/Machine Learning A-Z
Template Folder/Part 2 - Regression/Section 6 - Polynomial
Regression/Polynomial_Regression/Position_Salaries.csv")
dataset = dataset[2:3]](https://image.slidesharecdn.com/3-polynomiallinearregression-171230051801/85/polynomial-linear-regression-15-320.jpg)
polynomial linear regression
- 3. PYTHON
- 4. IMPORTING THE LIBRARIES import numpy as np import matplotlib.pyplot as plt import pandas as pd
- 5. IMPORTING THE DATASET dataset = pd.read_csv('Position_Salaries.csv') X = dataset.iloc[:, 1:2].values y = dataset.iloc[:, 2].values NOTE : X = dataset.iloc[:, 1:2].values can be rewritten as X = dataset.iloc[: , 1].values (see whats the problem)
- 6. FITTING LINEAR REGRESSION TO THE DATASET from sklearn.linear_model import LinearRegression lin_reg = LinearRegression() lin_reg.fit(X, y)
- 7. FITTING POLYNOMIAL REGRESSION TO THE DATASET from sklearn.preprocessing import PolynomialFeatures poly_reg = PolynomialFeatures(degree = 4) X_poly = poly_reg.fit_transform(X) poly_reg.fit(X_poly, y) lin_reg_2 = LinearRegression() lin_reg_2.fit(X_poly, y)
- 8. VISUALISING THE LINEAR REGRESSION RESULTS 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()
- 9. VISUALIZING THE POLYNOMIAL REGRESSION RESULTS plt.scatter(X, y, color = 'red') plt.plot(X, lin_reg_2.predict(poly_reg.fit_transform(X)), color = 'blue') plt.title('Truth or Bluff (Polynomial Regression)') plt.xlabel('Position level') plt.ylabel('Salary') plt.show()
- 10. VISUALIZING THE POLYNOMIAL REGRESSION RESULTS (FOR HIGHER RESOLUTION AND SMOOTHER CURVE) 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 (Polynomial Regression)') plt.xlabel('Position level') plt.ylabel('Salary') plt.show()
- 12. PREDICTING A NEW RESULT WITH LINEAR REGRESSION lin_reg.predict(6.5)
- 13. PREDICTING A NEW RESULT WITH POLYNOMIAL REGRESSION lin_reg_2.predict(poly_reg.fit_transform(6.5))
- 14. R
- 15. READ DATASET library(readr) dataset <- read_csv("D:/machine learning AZ/Machine Learning A-Z Template Folder/Part 2 - Regression/Section 6 - Polynomial Regression/Polynomial_Regression/Position_Salaries.csv") dataset = dataset[2:3]
- 16. FITTING LINEAR REGRESSION TO THE DATASET lin_reg = lm(formula = Salary ~ ., data = dataset)
- 17. FITTING POLYNOMIAL REGRESSION TO THE DATASET dataset$Level2 = dataset$Level^2 dataset$Level3 = dataset$Level^3 dataset$Level4 = dataset$Level^4 poly_reg = lm(formula = Salary ~ ., data = dataset)
- 18. VISUALISING THE LINEAR REGRESSION RESULTS # install.packages('ggplot2') library(ggplot2) ggplot() + geom_point(aes(x = dataset$Level, y = dataset$Salary), colour = 'red') + geom_line(aes(x = dataset$Level, y = predict(lin_reg, newdata = dataset)), colour = 'blue') + ggtitle('Truth or Bluff (Linear Regression)') + xlab('Level') + ylab('Salary')
- 19. VISUALIZING THE POLYNOMIAL REGRESSION RESULTS # install.packages('ggplot2') library(ggplot2) ggplot() + geom_point(aes(x = dataset$Level, y = dataset$Salary), colour = 'red') + geom_line(aes(x = dataset$Level, y = predict(poly_reg, newdata = dataset)), colour = 'blue') + ggtitle('Truth or Bluff (Polynomial Regression)') + xlab('Level') + ylab('Salary')
- 20. SMOOTHER CURVE # install.packages('ggplot2') library(ggplot2) x_grid = seq(min(dataset$Level), max(dataset$Level), 0.1) ggplot() + geom_point(aes(x = dataset$Level, y = dataset$Salary), colour = 'red') + geom_line(aes(x = x_grid, y = predict(poly_reg, newdata = data.frame(Level = x_grid, Level2 = x_grid^2, Level3 = x_grid^3, Level4 = x_grid^4))), colour = 'blue') + ggtitle('Truth or Bluff (Polynomial Regression)') + xlab('Level') + ylab('Salary')
- 21. PREDICTIONS # Predicting a new result with Linear Regression predict(lin_reg, data.frame(Level = 6.5)) # Predicting a new result with Polynomial Regression predict(poly_reg, data.frame(Level = 6.5, Level2 = 6.5^2, Level3 = 6.5^3, Level4 = 6.5^4))