Problem :
The Movie Recommendation System project aims to develop an intelligent system that suggests personalized movie recommendations to users based on their preferences and viewing history.

Challenges :
Build a machine learning model that can recommend movie based on user preference.

The Movie Recommendation System project aims to enhance the user experience by providing personalized movie recommendations.

Click here to get full code

3 Types of Recommendation System :

1. Popularity based recommendation system
   Recommend list of popular movie.
   To get list of popular movie in this dataset, we calculate weighted rating, and here is the result :

   Top 10 Popular Movie:
   

2. Content based filtering
   When click certain movie, it will give recommendation of similar movie.
   To get list of similar movie, we use Term Frequency & Inverse Document Frequency.
   For example, we try to know 3 movies that is similar with movie title "John Carter" :
   

3. Collaborative filtering
   Predict what rating the user gonna give.
   Example :
   What rating of user 15 will give to movie id 1956?
   

• The user with id 15 predicted will give ratings 3.49 to movie id 1956
• The ratings quite good because the rating ranges from 1 to 5.