Movie Recommendation System

Overview :

This project is a content-based movie recommendation system built using machine learning techniques and Python. The system suggests movies to users based on the similarity of movie descriptions or features. It employs natural language processing (NLP) to analyze movie descriptions and recommend similar movies to users based on their preferences. Some samples of project -

Features

• Content-Based Filtering: Recommends movies similar to those the user has liked in the past.
• Natural Language Processing: Utilizes NLP techniques to process and analyze movie descriptions.
• Streamlit Interface: Provides a user-friendly interface for users to interact with the recommendation system.

Data Sources

The dataset used in this project is sourced from credits.csv and movies.csv. It contains information about various movies including titles, descriptions, genres, etc. Similarity.pkl is the another important model file for prediction.

Download Dataset from Kaggle - https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata

Technologies Used

• Python
• Machine Learning
• Natural Language Processing
• Streamlit