msathyaanand / Movie-Recomendation-System

Movie Recommendation system using Content-based filtering

Movie Recommendation System

This repository contains a Movie Recommendation System implemented in Python using a movie dataset with Content-based filtering. The system suggests movies to users based on their preferences and previous ratings.

Dataset

The movie dataset used in this recommendation system is not included in this repository due to its large size. However, you can obtain a similar dataset from various sources such as MovieLens or IMDb.

Make sure to download the dataset and save it as a CSV file named movies.csv. The dataset should contain information about movies, including their titles, genres, and ratings.

Dependencies

To run this recommendation system, you need to have the following dependencies installed:

• Python (version 3.6 or higher)
• pandas
• numpy
• scikit-learn

You can install the required dependencies using pip with the following command:

pip install pandas numpy scikit-learn

Usage

1. Clone this repository to your local machine.

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

2. Download the movie dataset and save it as movies.csv in the cloned repository directory.
3. Open a terminal or command prompt and navigate to the repository directory.
4. Run the recommendation_system.py script.

python recommendation_system.py

5. The recommendation system will prompt you to enter a movie title for which you want recommendations. Type the title and press Enter.
6. The system will generate a list of recommended movies based on your input and previous ratings.

How it works

1. The script loads the movie dataset from the movies.csv file using pandas.
2. It preprocesses the data, including handling missing values and encoding categorical variables.
3. The system prompts the user to enter a movie title.
4. It then calculates the similarity between the entered movie and all other movies using a similarity metric (e.g., cosine similarity or Euclidean distance).
5. Based on the similarity scores, the system recommends a list of movies that are similar to the input movie.
6. The recommendations can be further improved by incorporating user ratings and preferences.

Contributing

Contributions to this movie recommendation system are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.