To build a fully functional recommendation system, we need 3 components working together. A user interacting with an application through the browser, A web application that can manage movies, users, ratings and Recommendation engine.
movies recommendation using machine learning
To build a fully functional recommendation system, we need 3 components working together. A user interacting with an application through the browser, A web application that can manage movies, users, ratings and Recommendation engine.
The aim of the project is to develop a Complete Web Application in which the new users have to register and if they are existing user they have to log in. The objectives of the project are:
1. Using python and its powerful computation libraries for data manipulation.
2. Machine learning libraries for training our model as in our case it’s a recommender engine.
3. develop the complete recommendation engine which can suggest movies to the user.
4. Using web languages to construct the web application.
We will be uisng the ML for first building our recommender engine using scientific libariries like numpy pandas matplotlib and scikit-learn.For recommender engine training we will be using MovieLens dataset available on internet.
Our project aims at first of all on exploration towards recommender systems in ML also exploring the architecture and working algorithms of papular movie sites like Netflix and IMDb.
Also our aims is to build an fully functional web application for users with recommmendations according to thier taste.
A fully functional Web application with bachend with python(using python's Web framework Django).
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| NCS | Equipment | 0 | 0 | 0 |
| Deep lens | Equipment | 1 | 50000 | 50000 |
| Nvidia Jetson Nano | Equipment | 1 | 20000 | 20000 |
| Total in (Rs) | 70000 |
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