Python Machine learning project in django or tkinter

We need Aspect-based opinion mining and collaborative filtering in our project.

A simple description of our project.

A user logs in and writes the reviews(reviews could be on anything movies,books etc. here we have choosen movies)

And based on those reviews, the aspects are gathered, and his preferences are known based on his reviews.

Then based on the reviews of other users, similarity is found between them

And the movies similar user liked are recommended to a user

For example I reviewed 10 movies

And based on those reviews it is known that I like movies that are thriller movies of short length

And users who liked movies with similar aspects are found and the movies liked by them that are thriller and short in length are recommended to me

From each review aspects are gathered

Such as for movies, length, acting, direction, cinematography, cast etc are aspects

And the textual reviews of the users on these aspects are converted to ratings

So for example for a movie

Length 1/5

Acting 4/5

Direction 5/5

Cast 4/5

Cinematography 3/5

So it can be deduced that the movie was good but it was long from the review of the user

And for collaborative filtering

If we have 5 users and each users has reviewed 10 movies

And I have reviewed 6 movies, at least one common movie with each user, based on which similarity can be calculated between me and them

And the movies that I haven't watched and they have liked are recommended to me

For this Pearson, similarity score is used.

Aspect Based Opinion mining code for your reference:-

[login to view URL]

The research paper is included.

Skills: Django, Machine Learning, Python

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About the Employer:
( 0 reviews ) India

Project ID: #18947366

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Hello! I am a python developer. I looked at your project and it seems interesting. I have all necessary skills required for this project. Ping me to discuss in detail.

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i will make this in 3 days lets go lets do this im a panda that will learn anything like a machine learning

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