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polynomial_regression.py
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polynomial_regression.py
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# Polynomial Regression
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('ENTER_THE_NAME_OF_YOUR_DATASET_HERE.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].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)
# Training the Polynomial Regression model on the Training set
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X_train)
regressor = LinearRegression()
regressor.fit(X_poly, y_train)
# Predicting the Test set results
y_pred = regressor.predict(poly_reg.transform(X_test))
np.set_printoptions(precision=2)
print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))
# Evaluating the Model Performance
from sklearn.metrics import r2_score
r2_score(y_test, y_pred)