tags: machine learning|python

機器學習 - 簡單線性回歸(Simple Linear Regression)

介紹

一個應變數(

$Y$)和一個自變數(

$X$)之線性關係(皆為連續型數)

目的：

• 解釋data過去現象
• 利用自變數(
  $X$)來預測應變數(
  $Y$)的未來可能數值

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方程式：

$y=b_0+b_1{x}_1$

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練習

機器學習-作業4

# Importing the libraries 
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv("Salary_Data.csv")
x = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].values

'''
# Missing Data
# Categorical Data
'''
# 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)

'''
# Feature Scaling
'''
# Simple Linear Regression
from sklearn.linear_model import LinearRegression

regressor = LinearRegression()
regressor.fit(x_train, y_train)
y_pred = regressor.predict(x_test)

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 (testing set)")
plt.xlabel("Years of Experience")
plt.ylabel("Salary")
plt.show()