Spam Classifier using Python NLTK and Flask web app

Step by step approach to the web application

• Creating python project
• Machine Learning model building
• Export trained model
• Export Tfidf object used to create training dataset
• Set-up Flask environment
• creating app.py which will have routes of index and other html files.
• creating or using templates for attractive user interface.
• create two directories namely templates and static in root directory of the project.
  • templates directory will have html files.
  • static directory will have css and javascript files.
• Finally, create nltk.txt, requirements.txt and Procfile(shell script file.) in root directory.

Steps to deploy the flask web app on heroku platform.

• First of all, we need to create git repository for our project.
• Upload all the project files inside github repository.
• Go to Heroku platform and login or create your account. It's free platform where we can host our ML/DL project web application.
• Create your free Heroku project
• Integrate your github profile
• Search for the github repository within provided search bar.
• Finally, deploy your machine learning Flask web application.