机器学习基础100天---day02 简单线性回归模型

绝地灬酷狼 2022-05-12 02:36 221阅读 0赞

数据集：

> Hours,Scores  
> 2.5,21  
> 5.1,47  
> 3.2,27  
> 8.5,75  
> 3.5,30  
> 1.5,20  
> 9.2,88  
> 5.5,60  
> 8.3,81  
> 2.7,25  
> 7.7,85  
> 5.9,62  
> 4.5,41  
> 3.3,42  
> 1.1,17  
> 8.9,95  
> 2.5,30  
> 1.9,24  
> 6.1,67  
> 7.4,69  
> 2.7,30  
> 4.8,54  
> 3.8,35  
> 6.9,76  
> 7.8,86

数据预处理常规步骤：

> 导入相关库  
> 导入数据集  
> 检查缺失值  
> 划分数据集  
> 特征缩放

#_*_coding:utf-8_*_
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    
    dataset = pd.read_csv('studentscores.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 = 1.0/4, random_state = 0)
    #注意：Python2.x 1/4=0  1.0/4=0.25	Python3.x 1/4=0.25
    # Fitting Simple Linear Regression Model to the training set
    from sklearn.linear_model import LinearRegression
    regressor = LinearRegression()
    #拟合模型
    regressor = regressor.fit(X_train, Y_train)
    
    # Predecting the Result
    Y_pred = regressor.predict(X_test)
    
    # Visualising the Training results
    plt.scatter(X_train , Y_train, color = 'red')
    plt.plot(X_train , regressor.predict(X_train), color ='blue')
    
    # Visualizing the test results
    plt.scatter(X_test , Y_test, color = 'yellow')
    plt.plot(X_test , regressor.predict(X_test), color ='green')
    plt.show()

[更多请参考大神GitHub][GitHub]

[GitHub]: https://github.com/MLEveryday/100-Days-Of-ML-Code