- This project is made for Microsoft Engage 2022 and personal learning. The project delivers a solution to Challenge 3 by presenting a working prototype of a Movie Recommendation System.
- A good movie recommendation system, should be able to determine the user's interests. The same is achieved by using collaborative filtering which is a Machine Learning technique used to identify relationships between pieces of data.
- User will be able to search for movies in the database
- Rate movies according to their liking
- Personalised Recommendations will be generated
- Jwt authentication for sessions
This is a web application, with a machine learning model, which implements collaborative filtering.
- Frontend rendering & styling : HTML, CSS
- Backend handling: Flask SQLAlchemy, Jwt Authentication
- Machine Learning : Python Pandas, H5py
- Movie Dataset : MovieLens Small Dataset
- functionality of creating and modifying playlist
- Enhance Playlist : recommending movies specific to that playlist
- Adding Filter based search
- Improving Modularity and Cosmetics of Application
- Deployment
| Type | Cases |
|---|---|
| Classes | PascalCase |
| Objects | camelCase |
| Constants | SCREAMING_SNAKE_CASE |
| Variables | snake_case |
| Modules | snake_case |
| function | snake_case |
| Css Classes | small-kebab-case |
the Color Theme has 6 colors :
-
#000000 -
#C7493A -
#A33327 -
#689775 -
#917164 -
#AD8174
- Clone the repository
- Make a vitrual environment and activate it(optional)
- Run
pip install -r requirements.txt - Run
flask run - Open
localhost:5000on your browser - Active Internet Connection is required
- To make an account, enter dummy credentials
- To login, enter email and password
- Some dummy users already have been made:
| password | |
|---|---|
| [email protected] | PjOpUMP |
| [email protected] | MwDT58P4W |
| [email protected] | fo6hP2GqtuL |