Movie Recommendation System

This repository contains an end-to-end movie recommendation system built using machine learning techniques, data analysis, NLTK for text processing, CountVectorizer for feature extraction, cosine similarity for recommendation, and TMDB API for fetching movie data.

Overview

The movie recommendation system analyzes and cleans movie data, applies natural language processing techniques, and uses machine learning algorithms to recommend movies based on user input. The cosine similarity metric is employed to find similarities between movies, providing personalized recommendations.

Features

• Data Analysis and Cleaning
• NLTK for Text Processing
• CountVectorizer for Feature Extraction
• Cosine Similarity for Recommendation
• Integration with TMDB API for Movie Data
• Deployment using Streamlit

Usage

1. Clone the repository:

   git clone <https://github.com/Bramitha-gowda-M/Movie-Recommendation-System>
   

2. Navigate to the project directory:

   cd MovieRecommendationSystem
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Run the Streamlit app:

   streamlit run app.py
   

5. Access the app in your browser.

Dependencies

• Python 3
• Pandas
• NumPy
• NLTK
• Scikit-learn
• Streamlit

Credits

Movie data: TMDB API

License

This project is licensed under the MIT License. See the LICENSE file for details.