This document provides an overview of a movie recommendation system project. It discusses using a movie dataset containing 5000 movies from TMDB. The project will use libraries like NumPy, Pandas, and Streamlit to preprocess the data, create a model for movie recommendations, and deploy the model through a Streamlit application. The scope of the project is to build a recommendation system that can predict movie ratings and provide personalized movie suggestions to users. The problem statement is to recommend movies to users based on their previous ratings and integrate social media analysis to improve recommendations.

1. Movie Recommendation System
by
Me

2. Introduction of Project
• Recommender System is a system that seeks to predict or filter preferences according to
the user’s choices. Recommender systems are utilized in a variety of areas including
Movies, music, news, books, research articles, search queries, social tags, and products in
general.
• Recommendation Systems are important as they help them make the right choices,
without having to expend their cognitive resources.
• Highly performing movie recommendation will suggest movies that match the
similarities with the highest degree of performance

3. Overview of Project
Libraries used
1.Numpy
2.Pandas
3.Streamlit
4.Numpy
5.Pickle
Dataset
TMDB 5000 Movie
Dataset
Plan
Create Model
1.PreProcessing
a.Merging of Files
b.List of String to List
c.Pass to Keys
2.Slicing
3.Vectorization
a.Vectorize
b.Pickling
Deploy
create py file and
integrate with model
on Streamlit

4. Scope of Project
• Every movies,music,books platforms are integrating the Recommendation
System.So,Recommendation systems are becoming increasingly important in
today’s extremely busy world.
• The music and video companies like Netflix, Youtube and Spotify use them to
generate music and video recommendations.
• Restaurants and hotels use it to generate food related recommendations. As well as
in the research articles, financial services and life insurance..

5. Problem Statement
• 1. OMRES (Online Movie Recommendation System) aims to recommend movies to users based on user-movie
(item) ratings. Problem Statement Given a set of users with their previous ratings for a set of movies, can we
predict the rating they will assign to a movie they have not previously rated?(by cs.bilkent.edu)
• 2. It greatly influences what we interact with the world: shopping (Amazon, Best Buy),
music(Spotify), video(Youtube, Netflix), etc.
• 3. Some companies have taken steps to integrate analytics in their recommender system
algorithms. They ask the customer to connect to their social media accounts such as Twitter,
YouTube, and Meta not only for advertising but also to analyze the activity of the user on these
social media accounts to recommend the best movies for them.

6. THANKS