机器学习100天学习笔记1：简单的线性模型

import pandas as pd
df = pd.read_csv('Desktop/Data_analysis_practice/100-Days-Of-ML/studentscores.csv')
df.shape
df.columns

df.iloc[0,0] # 取第一行第一列的数字
Out[18]: 2.5 

df.iloc[0,:] # 取第一行所有列
Out[19]: 
Hours      2.5
Scores    21.0
Name: 0, dtype: float64

df.iloc[:,0] # 取第一列所有行
Out[20]: 
0     2.5
1     5.1
2     3.2
3     8.5
4     3.5
5     1.5
6     9.2
7     5.5
8     8.3
9     2.7
10    7.7
11    5.9
12    4.5
13    3.3
14    1.1
15    8.9
16    2.5
17    1.9
18    6.1
19    7.4
20    2.7
21    4.8
22    3.8
23    6.9
24    7.8
Name: Hours, dtype: float64

# 取第一列作为X，第二列作为Y

X = df.iloc[:,0].values
Y = df.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=0.25,random_state=0)
import numpy as np
X_trainNew = np.array(X_train).reshape(-1,1)
regressor = regressor.fit(X_trainNew,Y_train)
X_testNew = np.array(X_test).reshape(-1,1)
Y_pred = regressor.predict(X_testNew)

import matplotlib.pyplot as plt
plt.scatter(X_trainNew,Y_train,color='red')
plt.plot(X_trainNew,regressor.predict(X_trainNew),color='blue')
plt.show()

plt.scatter(X_testNew,Y_test,color='red')
plt.plot(X_testNew,regressor.predict(X_testNew),color='blue')
plt.show()

regressor.score(X_testNew,Y_test)
0.93676610433650542