Simple Linear Regression

preprocessing

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

dataset = pd.read_csv('_data/02-data.csv')

x = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values

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)

train

from sklearn.linear_model import LinearRegression

regressor = LinearRegression()
regressor.fit(X_train, y_train)

LinearRegression()

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predict

y_pred = regressor.predict(X_test)

visualize

plt.scatter(X_train, y_train, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Salary vs Experience (training set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()

plt.scatter(X_test, y_test, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Salary vs Experience (test set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()

evaluate

from sklearn.metrics import r2_score

r2_score(y_test, y_pred)

0.9261621443754907