3. Aim: Build a movie recommendation system
based on ‘Kaggle’ dataset using machine
learning.
We wish to integrate the aspects of
personalization of user with the overall features
of movie such as genre, popularity etc.
4. A recommendation system or recommendation engine
is a model used for information filtering where it tries
to predict the preferences of a user and provide
suggests based on these preferences.
Movie Recommendation Systems helps us to search
our preferred movies among all of these different types
of movies and hence reduce the trouble of spending a
lot of time searching our favorable movies.
Recommendation systems have several benefits, the
most important being customer satisfaction and
revenue.
5. The goal of our project is to develop a movie
recommendation system for binge watchers to help and
recommend them good quality of movies.
The Objectives Are :
→ Improving the Accuracy of the recommendation
system
Improve the Quality of the movie Recommendation
system
→ Improving the Scalability.
Enhancing the user experience
6. Hardware Requirements
A PC with Windows/Linux OS
Minimum of 8gb RAM
2gb Graphic card
Software Requirements
Text Editor (VS-code)
Streamlit
Dataset
Jupyter(Editor)
Python libraries
9. Data Cleaning
Data Integration
Data Transformation
Data Reduction
10. To build recommendation system there are many approach that
can be used to build good recommendation system
Content based recommendation system
collaborative filtering.
Youtube also used content based recommended system, we also
used content based recommendation system in our project and
cosine similarity algorithm.
Cosine Similarity
Cosine similarity is used as a metric in different machine
learning algorithms like the KNN for determining the distance
between the neighbors, in recommendation systems, it is used to
recommend movies with the same similarities and for textual
data, it is used to find the similarity of texts in the document.
11.
12. • In this project, to improve the accuracy, quality and
scalability of movie recommendation system.
• The Proposed system will recommends good movies
according to user's choice.
• Bring interests and make users happy.