PixMusicaX/Movie-Recommendation-System

Movie Recommendation System

Welcome to the Movie Recommendation System! This project leverages machine learning techniques to recommend movies based on user input. The system uses a content-based filtering approach to suggest movies similar to the one provided by the user.

Table of Contents

• Overview
• Dataset
• Model
• Installation
• Usage
• Running the App
• Contributing
• License

Overview

The Movie Recommendation System uses a content-based approach to recommend movies. The system processes movie metadata, including genres, keywords, and overviews, to build a model that calculates similarities between movies. The recommendations are generated based on cosine similarity of the movie features.

Running the App

You can also access a live version of the Streamlit app hosted online:

• Movie Recommendation System

Dataset

The dataset used for this project is the TMDb Movie Metadata dataset. You can download it from the following link:

• TMDb Movie Metadata Dataset

Model

The model uses the following approach:

1. Data Preprocessing: The dataset is cleaned and processed to extract relevant features such as genres, keywords, and overviews.
2. Feature Extraction: Textual data is converted into numerical vectors using Bag of Words representation.
3. Similarity Calculation: Cosine similarity is used to measure the similarity between movies.
4. Recommendation: Based on the similarity scores, the system recommends movies similar to the input movie.

You can interact with the model via a Streamlit web application.

Installation

To set up this project locally, follow these steps:

1. Clone the Repository:

   git clone https://github.com/yourusername/Movie-Recommendation-System.git
   cd Movie-Recommendation-System

2. Install Required Packages:

   It is recommended to use a virtual environment. Install the required packages using pip:

   pip install -r requirements.txt

   Ensure you have the following dependencies in requirements.txt:

   pandas
   numpy
   scikit-learn
   nltk
   streamlit
   

3. Download the Dataset:

   Download the dataset from TMDb Movie Metadata Dataset and place it in the project directory.

Usage

1. Preprocess the Data:

   Run the preprocessing script to prepare the data and generate the similarity matrix:

   python preprocess_movies.py

   This script will create movies.pkl and similarity.pkl files containing the preprocessed data and similarity matrix, respectively.

2. Run the Streamlit App:

   Start the Streamlit application with the following command:

   streamlit run app.py

   Open your browser and navigate to the local URL provided by Streamlit (usually http://localhost:8501).

Contributing

Contributions to the project are welcome! Please fork the repository, make your changes, and submit a pull request. For any issues or suggestions, feel free to open an issue in the repository.

License

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