期末大作业

一、boston房价预测

import numpy as np

from sklearn.datasets import load_boston

from sklearn.linear_model import LinearRegression

from sklearn.model_selection import train_test_split

import matplotlib.pyplot as plt

 

# 读取数据集

boston = load_boston()

 

print(boston.keys())

print(boston.target)# 房价数据

print(boston.feature_names) # 数据集特征

 

# 划分训练集与测试集

#随机擦痒25%的数据构建测试样本，剩余作为训练样本

X_train,X_test,y_train,y_test = train_test_split(boston.data,boston.target,test_size=0.3) #random_state：是随机数的种子

print(X_train.shape,y_train.shape)

 

# 建立模型

LineR = LinearRegression()

LineR.fit(X_train,y_train)

 

# 检查模型好坏

x_predict = LineR.predict(X_test)

print("各列权重",LineR.coef_)

print("测试集上的评分：",LineR.score(X_test, y_test))

print("训练集上的评分：",LineR.score(X_train, y_train))

print("预测的均方误差：", np.mean(x_predict - y_test)**2)

print("最小目标值:",np.min(boston.target))

print("平均目标值:",np.mean(boston.target))

 

# 画图

X = boston.data[:,12].reshape(-1,1)

y = boston.target

 

plt.scatter(X,y)

 

LineR2 = LinearRegression()

LineR2.fit(X,y)

y_predict = LineR2.predict(X)

plt.plot(X,y_predict,'r')

plt.show()

from sklearn.datasets import load_boston

from sklearn.linear_model import LinearRegression

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import PolynomialFeatures

import matplotlib.pyplot as plt

 

# 读取数据集

boston = load_boston()

 

# 划分训练集与测试集

#随机擦痒25%的数据构建测试样本，剩余作为训练样本

x_train,x_test,y_train,y_test = train_test_split(boston.data,boston.target,test_size=0.3) #random_state：是随机数的种子

 

x = x_train[:,12].reshape(-1,1)

poly= PolynomialFeatures(degree=2)

x_poly = poly.fit_transform(x)

 

# 建立多项式回归模型

lrp = LinearRegression()

lrp.fit(x_poly,y_train)

 

lr = LinearRegression()

lr.fit(x,y_train)

w = lr.coef_

b = lr.intercept_

 

# 预测

x_poly2 = poly.transform(x_test[:, 12].reshape(-1,1))

y_ploy_predict = lrp.predict(x_poly2)

 

# 画图

plt.scatter(x_test[:,12], y_test)

plt.plot(x, w * x + b, 'g')

plt.scatter(x_test[:,12], y_ploy_predict, c='r')

plt.show()

 

二、中文文本分类

#新闻文本分类

import os

import jieba

#读取文件内容

 

leirong=[]#存放新闻的内容

kinds=[]#存放新闻的类别

def read_txt(path):

    folder_list=os.listdir(path)#遍历data下的文件名

    for file in folder_list:

        new_path=os.path.join(path,file) #读取文件夹的名称，生成新的路径

        files=os.listdir(new_path)#存放文件的内容

        #遍历每个txt文件

        for f in files:

            with open(os.path.join(new_path,f),'r',encoding='UTF-8')as f: #打开txt文件

                word=f.read()

            leirong.append(processing(word))

            kinds.append(file)

#             print(content)

#             print(label)

             

#对数据进行预处理

with open(r'D:/stopsCN.txt', encoding='utf-8') as f:

    stopwords = f.read().split('\n')

def processing(texts):

    # 去掉非法的字符

    texts = "".join([char for char in texts if char.isalpha()])

    # 用jieba分词

    texts = [text for text in jieba.cut(texts,cut_all=True) if len(text) >=2]

    # 去掉停用词

    texts = " ".join([text for text in texts if text not in stopwords])

    return texts

if __name__== '__main__':

    path=r'D:\147'

    read_txt(path)

 

#划分训练集和测试，用TF-IDF算法进行单词权值的计算

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.model_selection import train_test_split

tfidf= TfidfVectorizer()

x_train,x_test,y_train,y_test=train_test_split(leirong,kinds,test_size=0.2)

X_train=tfidf.fit_transform(x_train)#转换成特征向量

X_test=tfidf.transform(x_test)

#构建贝叶斯模型

from sklearn.naive_bayes import MultinomialNB #用于离散特征分类，文本分类单词统计，以出现的次数作为特征值

mulp=MultinomialNB ()

mulp_NB=mulp.fit(X_train,y_train)

#对模型进行预测

y_predict=mulp.predict(X_test)

# # 从sklearn.metrics里导入classification_report做分类的性能报告

from sklearn.metrics import classification_report

print('模型的准确率为：', mulp.score(X_test, y_test))

print('classification_report:\n',classification_report(y_test, y_predict))