NEWS-CLASSIFIER
所属分类：数据库系统
开发工具：HTML
文件大小：52232KB
下载次数：0
上传日期：2022-12-08 04:26:01
上 传 者：
sh-1993
说明： 开发一个以数据库为后台的新闻聚合器Web应用程序。
(Develop a News Aggregator Web Application with a Database serving as the Backend.)
文件列表:
.DS_Store (6148, 2020-10-15)
GIT_TUTORIAL.md (4398, 2020-10-15)
Procfile (21, 2020-10-15)
app.py (1125, 2020-10-15)
coc.md (4518, 2020-10-15)
model.pickle (11988040, 2020-10-15)
model.py (1785, 2020-10-15)
requirements.txt (3951, 2020-10-15)
runtime.txt (14, 2020-10-15)
venv (0, 2020-10-15)
venv\.DS_Store (6148, 2020-10-15)
venv\Model (0, 2020-10-15)
venv\Model\.idea (0, 2020-10-15)
venv\Model\.idea\Model.iml (284, 2020-10-15)
venv\Model\.idea\inspectionProfiles (0, 2020-10-15)
venv\Model\.idea\inspectionProfiles\profiles_settings.xml (174, 2020-10-15)
venv\Model\.idea\misc.xml (185, 2020-10-15)
venv\Model\.idea\modules.xml (262, 2020-10-15)
venv\Model\.idea\workspace.xml (1492, 2020-10-15)
venv\Model\test.csv (25144581, 2020-10-15)
venv\Model\train.csv (98628550, 2020-10-15)
venv\pyvenv.cfg (75, 2020-10-15)
venv\static (0, 2020-10-15)
venv\static\2.jpg (144453, 2020-10-15)
venv\static\bubble.jpg (21873, 2020-10-15)
venv\static\dialog(fake).css (557, 2020-10-15)
venv\static\dialog(real).css (557, 2020-10-15)
venv\static\fake.png (272319, 2020-10-15)
venv\static\style.css (4178, 2020-10-15)
venv\templates (0, 2020-10-15)
venv\templates\index.html (1586, 2020-10-15)
venv\templates\index1.html (1874, 2020-10-15)
venv\templates\index2.html (1848, 2020-10-15)
# **NEWS CLASSIFIER**
### **PROJECT ID: 06**
>## PROJECT DESCRIPTION
The effect of fake news has increased exponentially in the recent past and something must be done to prevent this from continuing in the future.
The main purpose of this project is to come up with a classifier which can differentiate fake news from the real news.
To develop an ML application to help users get notified about dubious news sources using Natural Language Processing.
>## Project Workflow
Dataset - We used real or fake news dataset from Kaggle.com in our project to evaluate the semantic feature.
Text pre-processing - we pre-process the raw text to extract semantic features for machine learning.
1). Tokenisation
2). Removing Stopwords
3). Lemmetisation
Trained the model.
Compared the models and their accuracy.
Worked on frontend and backend part.
>## TECHNOLOGY USED
1. Python and several of its libraries like scikit-learn, matplotlib, numpy and pandas for building the model.
2. Front-End: HTML, CSS and Javascript
3. Back-End API : Flask
```
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