###### tags: `machine learning`|`python`

# 機器學習 - 簡單線性回歸(Simple Linear Regression)
## 介紹
<font color="#3355FF">**一個**</font>應變數($Y$)和<font color="#3355FF">**一個**</font>自變數($X$)之<font color="#008000">線性關係</font>(皆為<font color="#f00">連續型數</font>)
> 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：$y = b_0 + b_1x_1$



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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()