The document outlines a project to develop a movie recommendation system using machine learning and a Kaggle dataset, focusing on personalizing user preferences by considering movie features such as genre and popularity. Key objectives include enhancing the accuracy, quality, and scalability of the system to improve user experience. It discusses various approaches, particularly content-based recommendation and cosine similarity algorithms, to effectively suggest movies to users.

1. Presented by :-
Dolly(2131750)
Bipasha Luthra(2131748)

2.  1.Problem statement
 2.Introduction
 3.Objective
 4.Project Requirements
 5.Data Analysis
 6.Design And Diagram
 7.Data preprocessing
 8.Approaches used
 9.Conclusion
 10.References

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

8. Genres distribution in
data
Number of ratings per
user

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.

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.

13.  Krish naik (Youtube)
 https://www.javatpoint.com
 https://www.geeksforgeek.org
 https://www.tutorialspoint.com
 https://www.kaggle.com