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46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
import pandas as pd
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from matplotlib import pyplot as plt
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from sklearn.linear_model import LinearRegression
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# Splitting the dataset into the Training set and Test set
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from sklearn.model_selection import train_test_split
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# Fitting Polynomial Regression to the dataset
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from sklearn.preprocessing import PolynomialFeatures
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# Importing the dataset
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dataset = pd.read_csv(
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"https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
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"position_salaries.csv"
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)
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X = dataset.iloc[:, 1:2].values
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y = dataset.iloc[:, 2].values
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
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poly_reg = PolynomialFeatures(degree=4)
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X_poly = poly_reg.fit_transform(X)
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pol_reg = LinearRegression()
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pol_reg.fit(X_poly, y)
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# Visualizing the Polymonial Regression results
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def viz_polymonial():
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plt.scatter(X, y, color="red")
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plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color="blue")
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plt.title("Truth or Bluff (Linear Regression)")
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plt.xlabel("Position level")
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plt.ylabel("Salary")
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plt.show()
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return
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if __name__ == "__main__":
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viz_polymonial()
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# Predicting a new result with Polymonial Regression
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pol_reg.predict(poly_reg.fit_transform([[5.5]]))
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# output should be 132148.43750003
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