This project aims to build a movie recommendation system using collaborative filtering technique. Collaborative filtering is a technique that recommends items based on the preferences of other users who have similar tastes.
Dataset The dataset used for this project is the MovieLens dataset. The dataset contains a total of 100,000 ratings from 943 users and 1,682 movies.
Model The recommendation system is built using Python, and the models are built using the Surprise library. The project is divided into two parts: data preprocessing and modeling. In the data preprocessing part, the dataset is preprocessed and transformed into the required format for the modeling part. In the modeling part, collaborative filtering algorithms are used to generate movie recommendations for users.
Technologies Used:
Python
Pandas Library
NumPy Library
Surprise Library
Matplotlib Library
Installation
pip install pandas
pip install numpy
pip install scikit-surprise
pip install matplotlib
Project Structure
├── Movie_Recommendation_System.ipynb
├── data
│ ├── movies.csv
│ └── ratings.csv
└── README.md
Conclusion
This project demonstrates how collaborative filtering algorithms can be used to build a movie recommendation system. The project showcases how to preprocess the
dataset and transform it into the required format for the modeling part. The results of this analysis can be used by movie streaming platforms to improve their
recommendation systems and provide personalized recommendations to their users.