Implemented a Movie Recommender System, which will recommend the top 10 movies similar to the movie the user has selected. The focus was to understand the Data Science Lifecycle (from data collection to model deployment). Furthermore, using streamlit package created a simple UI which describe how the ML algorithms are integrated with a web application to perform prediction.

Movie Recommender UI

Steps

1. Clone this github repository

2. Install the required packages using pip
   pip install -r requirements.txt

3. The dataframes and models is already saved in directory dumped_obj. The code for this is in Jupyter notebook named Movie_Recommendation_System.ipynb.

   • Option 1 : You can re-execute the notebook file and it will save the dataframes and models again in the dumped_obj directory

   • Option 2 : Continue with saved model and run the python script written in model_deployment.py file from terminal:
     streamlit run model_deployment.py
     This command will run streamlit localhost engine and you will be navigated to Simple UI in default browser.

Note

Before executing the Jupyter Notebook and streamlit command, please make sure that your terminal is pointing to current working directory.

Dataset Information

The project utilizes two primary datasets containing comprehensive movie information.

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1. credits.csv

This dataset contains information regarding the cast and crew of the movies.

Feature	Description
movie_id	A unique identifier for each movie.
cast	The names of lead and supporting actors.
crew	The names of the Director, Editor, Composer, Writer, etc.

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2. movies.csv

This dataset contains metadata and performance metrics for the movies.

Feature	Description
budget	The budget in which the movie was made.
genre	The genre of the movie (Action, Comedy, Thriller, etc.).
homepage	A link to the homepage of the movie.
id	The unique identifier (matches movie_id in the credits dataset).
keywords	Keywords or tags related to the movie.
original_language	The language in which the movie was made.
original_title	The title of the movie before translation or adaptation.
overview	A brief description of the movie.
popularity	A numeric quantity specifying the movie's popularity.
production_companies	The production house of the movie.
production_countries	The country in which it was produced.
release_date	The date on which it was released.
revenue	The worldwide revenue generated by the movie.
runtime	The running time of the movie in minutes.
status	"Released" or "Rumored".
tagline	The movie's tagline.
title	The title of the movie.
vote_average	Average ratings the movie received.
vote_count	The count of votes received.

Thank you and have a nice day 😄