Movie Recommendation System

End To End Machine Learning Implementation of a movie recommendation system

Introduction

The recommendation system is built using Python and uses content based filtering techniques to provide personalized movie recommendations to users.

• Content Based Filtering: Content based filtering is a recommendation technique that uses the characteristics or attributes of items to make personalized recommendations. In the context of movie recommendation systems, content-based filtering analyzes the characteristics or content of movies, such as genre, actors, director, and plot, to identify similar movies and suggest them to users.

Dataset Overview

The recommendation system utilizes the TMDB Movie Metadata dataset from Kaggle, a comprehensive collection of movie information including titles, genres, cast and crew details, release dates, ratings, and much more. This dataset offers a wide array of attributes that form the basis for our intelligent recommendation system. With information spanning over several decades, the dataset presents a wealth of cinematic content, allowing us to a recommendation system from broad selection of movies.

• Dataset: TMDB 5000 Movie Dataset

Libraries Used

The movie recommendation system is implemented using several libraries:

• numpy: NumPy is a library for the Python programming language that provides support for large, multi-dimensional arrays and matrices. It is used for numerical computations and data manipulation in this project.

• pandas: Pandas is a powerful data analysis and manipulation library. It provides data structures and functions for efficiently handling and analyzing structured data. In this project, Pandas is used for loading and preprocessing the movie dataset.

• nltk.stem.porter: NLTK (Natural Language Toolkit) is a library for natural language processing in Python. The PorterStemmer class from NLTK is used for stemming, which is the process of reducing words to their base or root form. It helps in improving the accuracy of text-based features in content-based filtering.

• sklearn.feature_extraction.text: Scikit-learn is a popular machine learning library in Python, and the CountVectorizer class from sklearn.feature_extraction.text is used to convert text data (such as movie plots) into numerical feature vectors. It tokenizes the text and builds a vocabulary of known words, assigning a numerical value to each word.

• sklearn.metrics.pairwise: This module from scikit-learn provides various distance metrics and pairwise similarity computations. In this project, the cosine_similarity function from this module is used to calculate the cosine similarity between feature vectors. It helps in measuring the similarity between movies based on their content features.

• pickle: The pickle module in Python is used for object serialization and deserialization. In this project, it is used to save and load trained models or intermediate data structures.

• streamlit: Streamlit is a Python library used for building interactive web applications for machine learning and data science. It simplifies the process of creating web interfaces for your projects. In this project, Streamlit is used to deploy the movie recommendation system as a web application.

• requests: The requests library is used for making HTTP requests in Python. It allows your code to interact with web APIs or retrieve data from remote servers. In this project, it is used for fetching movie data from an external API.