https://github.com/nickenshidqia/build_a_movie_recommendation_system
Build a machine learning model that can recommend movie based on user preference.
https://github.com/nickenshidqia/build_a_movie_recommendation_system
machine-learning movie-recommendation movie-recommendation-system python
Last synced: 7 months ago
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Build a machine learning model that can recommend movie based on user preference.
- Host: GitHub
- URL: https://github.com/nickenshidqia/build_a_movie_recommendation_system
- Owner: nickenshidqia
- Created: 2024-01-06T03:17:25.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-06T03:41:36.000Z (almost 2 years ago)
- Last Synced: 2025-04-10T00:37:32.218Z (7 months ago)
- Topics: machine-learning, movie-recommendation, movie-recommendation-system, python
- Language: Jupyter Notebook
- Homepage:
- Size: 503 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Machine Learning Project Build a Movie Recommendation System
## Project Description
**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.
## Project Goal
The Movie Recommendation System project aims to enhance the user experience by providing personalized movie recommendations.
## Tools & Library Used
[
](https://www.python.org/)
[
](https://jupyter.org/)
## Project Result
[Click here to get full code](https://github.com/nickenshidqia/Build_a_Movie_Recommendation_System/blob/3b69fed8cfddc6ce7a44df75f10eb3633f117d87/Movie%20recommendation%20system.ipynb)
**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.