import pandas as pd
from sklearn import linear_model
from sklearn.feature_extraction.text import TfidfVectorizer
f = open("D:\深度学习\smsspamcollection(1)\SMSSpamCollection.txt",'r', encoding='UTF-8')
#file = pd.read_csv(f)
df=pd.read_csv(f,delimiter='\t',header=None)
y,X_train=df[0],df[1]
vectorizer=TfidfVectorizer()
x=vectorizer.fit_transform(X_train)
print(x)
lr=linear_model.LogisticRegression()
lr.fit(x,y)
testX=vectorizer.transform(["URGENT! You phone number 123 had won a prize!",
"Hey,Whats up"])
predictions=lr.predict(testX)
print(predictions)
所需数据集下载https://download.youkuaiyun.com/download/devilangel2/12150884
本文介绍了一种使用Logistic回归和TF-IDF向量化技术进行短信垃圾邮件分类的方法。通过读取并预处理数据集,将短信内容转换为特征向量,训练模型以识别垃圾邮件。演示了如何预测新短信是否为垃圾邮件。
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