The course_web_scrapping's intro from aravind-selvam

👨‍💻 About The Project

Web scraping is a term for various methods used to collect data from across the Internet.
This web scraper extracts all the data on iNeuron website's all course information.
The scrapped data is then stored to user specified Mongodb database.

Installing Python, PyCharm, Monogodb, Git to Computer.
Creating Flask app by importing Flask module.
Getting information about iNeuron website.
Gathering data from most static websites is a relatively straightforward process. However, dynamic website like iNeuron, JavaScript is used to load their content. These web pages require a different approach to collecting the desired public data.
Scraping dynamic website using one of the most popular Python libraries, BeautifulSoup which can load the data into Json format by using "script" in soup.find method.

With the Json data all the required data is stored into Dictionary format.
Extracted all the course data using loops and stored as list.
Mongodb Altas is used as DB here, with pymongo library mongodb is connected to python.
Database and collections created via python and the list of dictionaries is uploaded using collection.insert_many method.
Created an app.py to initialize

Importing the Flask module and creating a Flask web server from the Flask module.
Create an object app in flask class with __name__ which represents current app.py file.
Create / route to render default page html.
Create a route /course to get user input and if keyword is present in the Mongo DB it is shown in results.html page.
Run the flask app with app.run() code.

Create new repo in Github and push all the data using Git.
Install Heroku CLI and login using heroku login and setup the app in Heroku Web.
Connect with app heroku git:remote -a appname
Push to Heroku using git push heroku main
Heroku Deployment Link