Movie Recommendation System

This is a movie recommendation system built using Flask and Python. It recommends similar movies based on user input.

Overview

This movie recommendation system uses cosine similarity to recommend similar movies. It processes movie data from two CSV files: tmdb_5000_movies.csv and tmdb_5000_credits.csv. The system extracts relevant features such as genres, keywords, cast, crew, and overview for each movie. Then, it computes the cosine similarity between movies based on these features.

Features

• Provides movie recommendations based on user input.
• Utilizes natural language processing (NLP) techniques for feature extraction.
• Implements cosine similarity for recommending similar movies.
• Built using Flask for the backend server.
• Frontend interface provided for user interaction.

Requirements

• Python 3.x
• Flask
• pandas
• scikit-learn
• nltk

Installation

1. Clone the repository:

   git clone https://github.com/your_username/movie-recommendation-system.git

2. Download the required CSV files (tmdb_5000_movies.csv and tmdb_5000_credits.csv) and place them in the same directory as the Python scripts.

Install the required Python packages:

pip install -r requirements.txt

Usage

1. Run the Flask server:

python app.py

2. Open a web browser and navigate to http://localhost:5000.
3. Enter the name of a movie in the input field and click the "Recommend" button.
4. The system will display recommended movies based on the input.