🎬 Movie Recommendation System
A content-based movie recommendation system built with Python, Machine Learning, and Flask, allowing users to get personalized movie suggestions based on their preferences.
Table of Contents
Project Overview
Features
Tech Stack
Installation
Usage
Dataset
Screenshots
Future Improvements
License
Project Overview
This project is a web-based Movie Recommendation System that provides movie suggestions to users based on a selected movie. The system uses cosine similarity to find movies with similar descriptions, genres, and other metadata.
It demonstrates practical applications of Natural Language Processing (NLP), Vectorization, and Machine Learning in recommendation systems.
Features
User-friendly Flask web interface
Enter a movie and get recommended movies instantly
Content-based filtering using TF-IDF / Count Vectorization
Recommendations based on movie descriptions and metadata
Works offline using a pre-trained model
Tech Stack
Python – Programming language
Flask – Web framework
Pandas & NumPy – Data manipulation
Scikit-learn – Machine learning & similarity calculations
HTML/CSS – Frontend interface
Pickle – Saving trained model
Installation
Clone the repository:
git clone https://github.com/ArnavP2305/Movie-Recommendation-System.git cd Movie-Recommendation-System
Create a virtual environment (optional but recommended):
python -m venv venv source venv/bin/activate # Linux/Mac venv\Scripts\activate # Windows
Install dependencies:
pip install -r requirements.txt
Usage
Run the Flask app:
python app.py
Open your browser and go to:
Enter a movie name and get top 5 recommended movies.
Dataset
Dataset used: Movie metadata from Kaggle or any publicly available movie dataset.
Includes movie titles, descriptions, genres, and other metadata.
Future Improvements
Add user login and personalized recommendations
Add collaborative filtering for hybrid recommendations
Improve UI with Bootstrap or React.js
License
This project is open-source and available under the MIT License.