https://github.com/nikhilfuke1/movies-recommendation-system-python-project-
This project is a simple movie recommendation system built using Python. It suggests movies based on the similarity of titles and a predefined similarity score matrix.
https://github.com/nikhilfuke1/movies-recommendation-system-python-project-
difflib numpy pandas python sklearn
Last synced: about 1 month ago
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This project is a simple movie recommendation system built using Python. It suggests movies based on the similarity of titles and a predefined similarity score matrix.
- Host: GitHub
- URL: https://github.com/nikhilfuke1/movies-recommendation-system-python-project-
- Owner: nikhilfuke1
- Created: 2025-01-03T10:57:43.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-21T03:21:28.000Z (11 months ago)
- Last Synced: 2025-04-01T02:59:05.999Z (10 months ago)
- Topics: difflib, numpy, pandas, python, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 5.92 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Project - Movies Recommendation System
Here's a description
🎬 Movie Recommendation System
This project is a simple movie recommendation system built using Python. It suggests movies based on the similarity of titles and a predefined similarity score matrix.
📋 Features
User Input: Enter your favorite movie, and the system will recommend similar titles.
Similarity Matching: Uses difflib to find the closest matching movie titles from a list.
Ranking: Movies are ranked based on similarity scores and displayed in descending order.
Top 10 Recommendations: The system returns the top 10 movies similar to the input movie.
🛠️ Technologies Used
Python
Pandas – for handling movie dataframes
difflib – for string matching and closest title search
📂 Project Structure
Movie Recommendation System.ipynb – Main Jupyter notebook with the code.
📈 Future Improvements
Integrate collaborative filtering for more advanced recommendations.
Add a graphical user interface (GUI) for easier interaction.
Incorporate a larger dataset for improved accuracy.
Feel free to contribute or fork this project! 😊
Movie Recomendation System
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. Recommender systems produce a list of recommendations in any of the two ways -
Collaborative filtering: Collaborative filtering approaches build a model from the user's past behavior (i.e. items purchased or searched by the user) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that users may have an interest in.
Content-based filtering: Content-based filtering approaches uses a series of discrete characteristics of an item in order to recommend additional items with similar properties. Content-based filtering methods are totally based on a description of the item and a profile of the user's preferences. It recommends items based on the user's past preferences. Let's develop a basic recommendation system using Python and Pandas.
Let's develop a basic recommendation system by suggesting items that are most similar to a particular item, in this case, movies. It just tells what movies/items are most similar to the user's movie choice.