This repository contains a machine learning web application for recommending movies. The application is built using Python and various libraries, and it is deployed using Streamlit.
This movie recommender system provides movie recommendations based on user preferences. The application utilizes collaborative filtering and content-based filtering techniques to suggest movies that users might enjoy.
- User-friendly web interface
- Movie recommendations based on user input
- Interactive visualizations
- Easy to deploy and run
- Python: Core programming language
- Pandas: Data manipulation and analysis
- NumPy: Numerical computing
- Pickle: Serializing and de-serializing Python objects
- Streamlit: Web application framework for deploying the model
The model creation process involves three main steps:
- Data Preprocessing: Cleaning and transforming the dataset for better performance.
- Model Creation: Building the recommendation model using collaborative filtering and content-based filtering techniques.
- Prediction: Generating movie recommendations based on user input.