Movie Recommendation System

Project Overview

This project develops a content-based movie recommendation system that utilizes a dataset containing a variety of movie features. By analyzing attributes like genres, directors, cast, and plot descriptions, the system provides personalized movie recommendations based on user preferences. The objective is to suggest movies that align with user tastes and explore the relationships between different features for improved recommendations.

Technical Highlights

• Data Processing: Utilizes pandas for data manipulation and preprocessing, ensuring that missing or irrelevant data is handled appropriately.
• Exploratory Data Analysis (EDA): Identifies patterns and insights from the dataset through visualizations, revealing trends in movie genres, keywords, production companies, and cast.
• Recommendation Algorithm: Implements a content-based filtering approach, where movies are recommended based on the similarity of their features to those liked by the user.
• Data Visualization: Leverages seaborn and matplotlib for creating insightful visualizations that enhance the understanding of the dataset's structure and the relationships between different movie attributes.

Purpose and Applications

The purpose of this system is to provide movie recommendations tailored to users based on the content they enjoy. This system can be used by:

• Movie Streaming Platforms: For recommending movies to users based on their past viewing history and preferences.
• Content Curation Services: To help platforms suggest content to their users by understanding the popularity and relationships between different genres, actors, and directors.
• Personalized User Experiences: Enhance user engagement by offering tailored movie suggestions that match their unique tastes.

Dataset

kaggle - https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata/code

Installation

To get started with the Movie Recommendation System, follow these steps:

1. Clone this repository:

   git clone https://github.com/BhaveshBhakta/Movie-Recommendation-System-Using-ML.git
   

2. Navigate to the project directory:

   cd Movie-Recommendation-System-Using-ML
   

3. Run the project:

   jupyter notebook
   

Collaboration

Contributions are welcome! Feel free to fork the repository, make improvements, and submit a pull request.